An overview of recommender systems in the healthy food domain
---
paper_title: Let's browse: a collaborative Web browsing agent

paper_content:

Web browsing, like most of today’s desktop applications, is usually a solitary activity. Other forms of media, such as watching television, are often done by groups of people, such as families or friends. What would it be like to do collaborative Web browsing? Could the computer provide assistance to group browsing by trying to help find mutual interests among the participants? Let’s Browse is an experiment in building an agent to assist a group of people in browsing, by suggesting new material likely to be of common interest. It is built as an extension to the single user Web browsing agent Letizia. Let’s Browse features automatic detection of the presence of users, automated “channel surfing” browsing, and dynamic display of the user profiles and explanation of recommendations.q 1999 Elsevier Science B.V. All rights reserved.
---
paper_title: TV Program Recommendation for Multiple Viewers Based on user Profile Merging

paper_content:

Since today's television can receive more and more programs, and televisions are often viewed by groups of people, such as a family or a student dormitory, this paper proposes a TV program recommendation strategy for multiple viewers based on user profile merging. This paper first introduces three alternative strategies to achieve program recommendation for multiple television viewers, discusses, and analyzes their advantages and disadvantages respectively, and then chooses the strategy based on user profile merging as our solution. The selected strategy first merges all user profiles to construct a common user profile, and then uses a recommendation approach to generate a common program recommendation list for the group according to the merged user profile. This paper then describes in detail the user profile merging scheme, the key technology of the strategy, which is based on total distance minimization. The evaluation results proved that the merging result can appropriately reflect the preferences of the majority of members within the group, and the proposed recommendation strategy is effective for multiple viewers watching TV together.
---
paper_title: Polylens: A recommender system for groups of user

paper_content:

We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email
---
paper_title: Intrigue: Personalized Recommendation Of Tourist Attractions For Desktop And Handset Devices

paper_content:

This paper presents INTRIGUE, a prototype tourist-information server that presents information about the area around Turin City, Italy, on desktop and hand held devices.This system recommends sightseeing destinations and itineraries by taking into account the preferences of heterogeneous tourist groups (such as families with children or the elderly) and explains the recommendations by addressing the group members' requirements. Moreover, the system provides an interactive agenda for scheduling the tour. The services offered by INTRIGUE rely on user modeling and adaptive hypermedia techniques; furthermore, XML-based technologies support the generation of the user interface and its adaptation to Web browsers and WAP minibrowsers.
---
paper_title: Deriving a recipe similarity measure for recommending healthful meals

paper_content:

A recipe recommender system may stimulate healthful and varied eating, when the presented recipes fit the lifestyle of the user. As consumers face the barrier to change their eating and cooking behavior, we aim for a strategy to provide more healthful variations to routine recipes. In this paper, a similarity measure for recipes is derived by taking a user-centered approach. Such a measure can be used to recommend healthier alternatives to commonly selected meals, which are perceived to be similar. Recipes presented using this strategy may fit the demand for health and variation within the boundaries of a busy lifestyle. Having derived and evaluated a recipe similarity measure, we explore its use through an at-home trial.
---
paper_title: Recipe recommendation: accuracy and reasoning

paper_content:

Food and diet are complex domains for recommender technology, but the need for systems that assist users in embarking on and engaging with healthy living programs has never been more real. One key to sustaining long term engagement with eHealth services is the provision of tools, which assist and train users in planning correctly around the areas of diet and exercise. These tools require an understanding of user reasoning as well as user needs and are ideal application areas for recommender and personalization technologies. Here, we report on a large scale analysis of real user ratings on a set of recipes in order to judge the applicability and practicality of a number of personalization algorithms. Further to this, we report on apparent user reasoning patterns uncovered in rating data supplied for recipes and suggest ways to exploit this reasoning understanding in the recommendation process.
---
paper_title: Intelligent menu planning: recommending set of recipes by ingredients

paper_content:

With the growth of recipe sharing services, online cooking recipes associated with ingredients and cooking procedures are available. Many recipe sharing sites have devoted to the development of recipe recommendation mechanism. However, there is a need for users to plan menu of meals by ingredients. While most research on food related research has been on recipe recommendation and retrieval, little research has been done on menu planning. In this paper, we investigate an intelligent menu planning mechanism which recommending sets of recipes by user-specified ingredients. Those recipes which are well-accompanied and contain the query ingredients are returned. We propose a graph-based algorithm for menu planning. The proposed approach constructs a recipe graph to capture the co-occurrence relationships between recipes from collection of menus. A menu is generated by approximate Steiner Tree Algorithm on the constructed recipe graph. Evaluation of menu collections from Food.com shows that the proposed approach achieves encouraging results.
---
paper_title: A recipe based on-line food store

paper_content:

Recent research in the area of information retrieval hypothesizes that people benefit from social clues, so called social navigation, when they try to navigate information spaces [7]. We have designed an on-line grocery store building upon those ideas manifested in several different ways. The most central feature is that the system uses a combination of content-based and collaborative filtering as the basis for recipe recommendations. This filtering process can in turn be controlled by editors, whose role is to control the content of the “recipe clubs”. Other types of social clues are also present, such as displaying how many users that have chosen a recipe. Finally, the system shows information about other users currently present in the system, and allows users to get in direct contact through chat.
---
paper_title: Dealing with Malnutrition: A Meal Planning System for Elderly

paper_content:

Malnutrition is a serious problem among people of old age. To overcome malnutrition, a change of food consumption behaviour is necessary, which needs to be based on specialist advice from health-care professionals. Changing food-related behaviour, however, is known to be difficult. Our approach to this problem is to provide an intelligent meal planning system to be used by the elderly person in his or her home. The system provides recommendations of suitable food recipes, taking into account the advice of the care givers (e.g. in terms of dietary restrictions, suitable energy and fat levels, etc). We describe the requirements, design, algorithms, and user interface of the system, and discuss ongoing and future work.
---
paper_title: Interaction Design in a Mobile Food Recommender System

paper_content:

One of the most important steps in building a recommender system is the interaction design process, which defines how the recommender system interacts with a user. It also shapes the experience the user gets, from the point she registers and provides her preferences to the system, to the point she receives recommendations generated by the system. A proper interaction design may improve user experience and hence may result in higher usability of the system, as well as, in higher satisfaction. In this paper, we focus on the interaction design of a mobile food recommender system that, through a novel interaction process, elicits users’ long-term and short-term preferences for recipes. User’s long-term preferences are captured by asking the user to rate and tag familiar recipes, while for collecting the short-term preferences, the user is asked to select the ingredients she would like to include in the recipe to be prepared. Based on the combined exploitation of both types of preferences, a set of personalized recommendations is generated. We conducted a user study measuring the usability of the proposed interaction. The results of the study show that the majority of users rates the quality of the recommendations high and the system achieves usability scores above the standard benchmark.
---
paper_title: A recipe recommendation system based on automatic nutrition information extraction

paper_content:

In this paper, we propose a goal-oriented recipe recommendation system that utilizes information about nutrition on the Internet. Our system enables users without knowledge about nutrition to search easily for recipes with natural language to improve specific health conditions. The natural language includes 'I want to cure my acne' and 'I want to recover from my fatigue'. To do that, we created a co-occurrence database that listed the co-occurrence of 45 common nutrients with nouns such as cold, acne, bone etc. Then we created a recipe database by collecting 800,000 recipes from www.cookpad.com the system and analyzed each recipe to calculate the amount of a nutrient in a dish. We compared the results of our system to the results we obtained by calculating the nutrient information manually. Evaluation was done on 1000 dishes. We measured the effectiveness of the system using F-Measure and the average F-measure was 0.64 respectively.
---
paper_title: Interactive Food Recommendation for Groups

paper_content:

We present a prototype of a novel interactive food recommender for groups of users that supports groups in planning their meals through a conversational process based on critiquing. The system comprises two novel elements: a user interface and interaction design based on tagging and critiquing, and a utility function incorporating healthiness and diet compliance factors.
---
paper_title: Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey

paper_content:

Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering is currently most widely used approach to build Recommendation System. CF techniques uses the user behavior in form of user item ratings as their information source for prediction. There are major challenges like sparsity of rating matrix and growing nature of data which is faced by CF algorithms. These challenges are been well taken care by Matrix Factorization. In this paper we attempt to present an overview on the role of different MF model to address the challenges of CF algorithms, which can be served as a roadmap for research in this area.
---
paper_title: Collaborative filtering recommender systems

paper_content:

One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.
---
paper_title: Constraint-based recommender systems: technologies and research issues

paper_content:

Recommender systems support users in identifying products and services in e-commerce and other information-rich environments. Recommendation problems have a long history as a successful AI application area, with substantial interest beginning in the mid-1990s, and increasing with the subsequent rise of e-commerce. Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services. In this paper, we first introduce a taxonomy of recommendation knowledge sources and algorithmic approaches. We then go on to discuss the most prevalent techniques of constraint-based recommendation and outline open research issues.
---
paper_title: Content-based book recommending using learning for text categorization

paper_content:

Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. By contrast,content-based methods use information about an item itself to make suggestions.This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.
---
paper_title: Case-based recommender systems

paper_content:

We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, covering the development of these systems over the last ten years.
---
paper_title: Fab: Content-based, collaborative recommendation

paper_content:

A passive, integrating electromagnetic radiation power dosimeter. A radiofrequency or microwave antenna is combined with a diode detector/rectifier, a squaring circuit, and a electrochemical storage cell to provide an apparatus for determining the average energy of electromagnetic radiation incident on a surface. After a particular period of irradiation, the dosimeter can be interrogated electrically or visually, depending on the type of electrochemical cell employed, to yield the desired information. The apparatus has a substantially linear response to the electromagnetic power density over a wide range of electromagnetic field, and all of the energy required to record the incident energy is supplied by the electromagnetic field.
---
paper_title: KNOWLEDGE-BASED RECOMMENDER TECHNOLOGIES FOR MARKETING AND SALES

paper_content:

Recommender applications support decision-making processes by helping online customers to identify products more effectively. Recommendation problems have a long history as a successful application area of Artificial Intelligence (AI) and the interest in recommender applications has dramatically increased due to the demand for personalization technologies by large and successful e-Commerce environments. Knowledge-based recommender applications are especially useful for improving the accessibility of complex products such as financial services or computers. Such products demand a more profound knowledge from customers than simple products such as CDs or movies. In this paper we focus on a discussion of AI technologies needed for the development of knowledge-based recommender applications. In this context, we report experiences from commercial projects and present the results of a study which investigated key factors influencing the acceptance of knowledge-based recommender technologies by end-users.
---
paper_title: Group Recommender Systems: Combining individual models

paper_content:

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.
---
paper_title: Polylens: A recommender system for groups of user

paper_content:

We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email
---
paper_title: Intrigue: Personalized Recommendation Of Tourist Attractions For Desktop And Handset Devices

paper_content:

This paper presents INTRIGUE, a prototype tourist-information server that presents information about the area around Turin City, Italy, on desktop and hand held devices.This system recommends sightseeing destinations and itineraries by taking into account the preferences of heterogeneous tourist groups (such as families with children or the elderly) and explains the recommendations by addressing the group members' requirements. Moreover, the system provides an interactive agenda for scheduling the tour. The services offered by INTRIGUE rely on user modeling and adaptive hypermedia techniques; furthermore, XML-based technologies support the generation of the user interface and its adaptation to Web browsers and WAP minibrowsers.
---
paper_title: Group Recommender Systems: Combining individual models

paper_content:

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.
---
paper_title: Group-based recipe recommendations: analysis of data aggregation strategies

paper_content:

Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.
---
paper_title: Group Recommender Systems: New Perspectives in the Social Web

paper_content:

An increasingly important type of recommender systems comprises those that generate suggestions for groups rather than for individuals. In this chapter, we revise state of the art approaches on group formation, modelling and recommendation, and present challenging problems to be included in the group recommender system research agenda in the context of the Social Web.
---
paper_title: Supporting flexible roles in a shared space

paper_content:

People in a group play various roles. Manager, intern, department chair, guest speaker, the woman who knows how the fax machine works, the guy who got the pizza, the person speaking now, the person capturing this on the whiteboard; roles can be formal or informal, long-lived or ephemeral. They can be bestowed by ceremony, or assumed with the invisibility of the taken-for-granted. But it is the interaction of minds that creates and sustains a role: a role is a human construct. Even roles like “mother” or “father”, which would seem to demand certain biological characteristics, cannot be defined at any depth without appeal to social convention. The largely hands-off approach taken by nature seems to work fairly well for groups of humans: when there is confusion or concern about who is playing what role, it is generally good for a group to work it out as a social issue. If roles were encoded solely in human biology or in physical law, life might be simpler but certainly more stulfiying.
---
paper_title: Biases in Decision Making

paper_content:

Decisions are typically taken on the basis of heuristics that are a door opener for different types of decision biases. Such biases can be interpreted as a tendency to decide in certain simplified ways which can often lead to suboptimal decision outcomes. Recommender systems support users in different types of decision making tasks and thus should be aware of such biases. In this paper we provide a short overview of different types of decision biases and their impacts on recommender systems. We also discuss some issues for future work.
---
paper_title: Polylens: A recommender system for groups of user

paper_content:

We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email
---
paper_title: Intrigue: Personalized Recommendation Of Tourist Attractions For Desktop And Handset Devices

paper_content:

This paper presents INTRIGUE, a prototype tourist-information server that presents information about the area around Turin City, Italy, on desktop and hand held devices.This system recommends sightseeing destinations and itineraries by taking into account the preferences of heterogeneous tourist groups (such as families with children or the elderly) and explains the recommendations by addressing the group members' requirements. Moreover, the system provides an interactive agenda for scheduling the tour. The services offered by INTRIGUE rely on user modeling and adaptive hypermedia techniques; furthermore, XML-based technologies support the generation of the user interface and its adaptation to Web browsers and WAP minibrowsers.
---
paper_title: More than the sum of its members: challenges for group recommender systems

paper_content:

Systems that recommend items to a group of two or more users raise a number of challenging issues that are so far only partly understood. This paper identifies four of these issues and points out that they have been dealt with to only a limited extent in the group recommender systems that have been developed so far. The issues are especially important in settings where group members specify their preferences explicitly and where they are not able to engage in face-to-face interaction. We illustrate some of the solutions discussed with reference to the T RAVEL D ECISION F ORUM prototype. The issues concern (a) the design of suitable preference elicitation and aggregation methods, in particular nonmanipulable aggregation mechanisms; and (b) ways of making members aware of each other's preferences and motivational orientations, such as the use of animated representatives of group members.
---
paper_title: Counteracting Serial Position Effects in the CHOICLA Group Decision Support Environment

paper_content:

Decisions are often suboptimal due to the fact that humans apply simple heuristics which cause different types of decision biases. CHOICLA is an environment that supports decision making for groups of users. It supports the determination of recommendations for groups and also includes mechanisms to counteract decision biases. In this paper we give an overview of the CHOICLA environment and report the results of a user study which analyzed two voting strategies with regard to their potential of counteracting serial position (primacy/recency) effects when evaluating decision alternatives.
---
paper_title: Group Recommender Systems: Combining individual models

paper_content:

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.
---
paper_title: Challenges for Nutrition Recommender Systems

paper_content:

Obesity or being overweight in general often leads to other more severe diseases. With more than half of the population in the west- ern countries being overweight or even obese, systems are developed to help users make healthier food choices by recommending healthier recipes or food items according to the user's needs or likes and dislikes. But there are many challenges designing such systems. In this paper we try to iden- tify the difficulties encountered and discuss ways to deal withthem.
---
paper_title: Reduction in the Incidence of Type 2 Diabetes with Lifestyle Intervention or Metformin

paper_content:

Background Type 2 diabetes affects approximately 8 percent of adults in the United States. Some risk factors — elevated plasma glucose concentrations in the fasting state and after an oral glucose load, overweight, and a sedentary lifestyle — are potentially reversible. We hypothesized that modifying these factors with a lifestyle-intervention program or the administration of metformin would prevent or delay the development of diabetes. Methods We randomly assigned 3234 nondiabetic persons with elevated fasting and post-load plasma glucose concentrations to placebo, metformin (850 mg twice daily), or a lifestyle modification program with the goals of at least a 7 percent weight loss and at least 150 minutes of physical activity per week. The mean age of the participants was 51 years, and the mean body-mass index (the weight in kilograms divided by the square of the height in meters) was 34.0; 68 percent were women, and 45 percent were members of minority groups. Results The average follow-up was 2.8 years. The incidence of diabetes was 11.0, 7.8, and 4.8 cases per 100 person-years in the placebo, metformin, and lifestyle groups, respectively. The lifestyle intervention reduced the incidence by 58 percent (95 percent confidence interval, 48 to 66 percent) and metformin by 31 percent (95 percent confidence interval, 17 to 43 percent), as compared with placebo; the lifestyle intervention was significantly more effective than metformin. To prevent one case of diabetes during a period of three years, 6.9 persons would have to participate in the lifestyle-intervention program, and 13.9 would have to receive metformin. Conclusions Lifestyle changes and treatment with metformin both reduced the incidence of diabetes in persons at high risk. The lifestyle intervention was more effective than metformin.
---
paper_title: Intelligent menu planning: recommending set of recipes by ingredients

paper_content:

With the growth of recipe sharing services, online cooking recipes associated with ingredients and cooking procedures are available. Many recipe sharing sites have devoted to the development of recipe recommendation mechanism. However, there is a need for users to plan menu of meals by ingredients. While most research on food related research has been on recipe recommendation and retrieval, little research has been done on menu planning. In this paper, we investigate an intelligent menu planning mechanism which recommending sets of recipes by user-specified ingredients. Those recipes which are well-accompanied and contain the query ingredients are returned. We propose a graph-based algorithm for menu planning. The proposed approach constructs a recipe graph to capture the co-occurrence relationships between recipes from collection of menus. A menu is generated by approximate Steiner Tree Algorithm on the constructed recipe graph. Evaluation of menu collections from Food.com shows that the proposed approach achieves encouraging results.
---
paper_title: A recipe based on-line food store

paper_content:

Recent research in the area of information retrieval hypothesizes that people benefit from social clues, so called social navigation, when they try to navigate information spaces [7]. We have designed an on-line grocery store building upon those ideas manifested in several different ways. The most central feature is that the system uses a combination of content-based and collaborative filtering as the basis for recipe recommendations. This filtering process can in turn be controlled by editors, whose role is to control the content of the “recipe clubs”. Other types of social clues are also present, such as displaying how many users that have chosen a recipe. Finally, the system shows information about other users currently present in the system, and allows users to get in direct contact through chat.
---
paper_title: Food Recommendation using Ontology and Heuristics

paper_content:

Recommender systems are needed to find food items of one’s interest. This paper reviews recommender systems and recommendation methods, then propose a food personalization framework based on adaptive hypermedia and extend Hermes framework with food recommendation functionality. Moreover, it combines TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.
---
paper_title: Interaction Design in a Mobile Food Recommender System

paper_content:

One of the most important steps in building a recommender system is the interaction design process, which defines how the recommender system interacts with a user. It also shapes the experience the user gets, from the point she registers and provides her preferences to the system, to the point she receives recommendations generated by the system. A proper interaction design may improve user experience and hence may result in higher usability of the system, as well as, in higher satisfaction. In this paper, we focus on the interaction design of a mobile food recommender system that, through a novel interaction process, elicits users’ long-term and short-term preferences for recipes. User’s long-term preferences are captured by asking the user to rate and tag familiar recipes, while for collecting the short-term preferences, the user is asked to select the ingredients she would like to include in the recipe to be prepared. Based on the combined exploitation of both types of preferences, a set of personalized recommendations is generated. We conducted a user study measuring the usability of the proposed interaction. The results of the study show that the majority of users rates the quality of the recommendations high and the system achieves usability scores above the standard benchmark.
---
paper_title: Intelligent menu planning: recommending set of recipes by ingredients

paper_content:

With the growth of recipe sharing services, online cooking recipes associated with ingredients and cooking procedures are available. Many recipe sharing sites have devoted to the development of recipe recommendation mechanism. However, there is a need for users to plan menu of meals by ingredients. While most research on food related research has been on recipe recommendation and retrieval, little research has been done on menu planning. In this paper, we investigate an intelligent menu planning mechanism which recommending sets of recipes by user-specified ingredients. Those recipes which are well-accompanied and contain the query ingredients are returned. We propose a graph-based algorithm for menu planning. The proposed approach constructs a recipe graph to capture the co-occurrence relationships between recipes from collection of menus. A menu is generated by approximate Steiner Tree Algorithm on the constructed recipe graph. Evaluation of menu collections from Food.com shows that the proposed approach achieves encouraging results.
---
paper_title: A recipe based on-line food store

paper_content:

Recent research in the area of information retrieval hypothesizes that people benefit from social clues, so called social navigation, when they try to navigate information spaces [7]. We have designed an on-line grocery store building upon those ideas manifested in several different ways. The most central feature is that the system uses a combination of content-based and collaborative filtering as the basis for recipe recommendations. This filtering process can in turn be controlled by editors, whose role is to control the content of the “recipe clubs”. Other types of social clues are also present, such as displaying how many users that have chosen a recipe. Finally, the system shows information about other users currently present in the system, and allows users to get in direct contact through chat.
---
paper_title: Intelligent food planning: personalized recipe recommendation

paper_content:

As the obesity epidemic takes hold across the world many medical professionals are referring users to online systems aimed at educating and persuading users to alter their lifestyle. The challenge for many of these systems is to increase initial adoption and sustain participation for sufficient time to have real impact on the life of its users. In this work we present some preliminary investigation into the design of a recipe recommender, aimed at educating and sustaining user participation, which makes tailored recommendations of healthy recipes. We concentrate on the two initial dimensions of food recommendations: data capture and food-recipe relationships and present a study into the suitability of varying recommender algorithms for the recommendation of recipes.
---
paper_title: Interaction Design in a Mobile Food Recommender System

paper_content:

One of the most important steps in building a recommender system is the interaction design process, which defines how the recommender system interacts with a user. It also shapes the experience the user gets, from the point she registers and provides her preferences to the system, to the point she receives recommendations generated by the system. A proper interaction design may improve user experience and hence may result in higher usability of the system, as well as, in higher satisfaction. In this paper, we focus on the interaction design of a mobile food recommender system that, through a novel interaction process, elicits users’ long-term and short-term preferences for recipes. User’s long-term preferences are captured by asking the user to rate and tag familiar recipes, while for collecting the short-term preferences, the user is asked to select the ingredients she would like to include in the recipe to be prepared. Based on the combined exploitation of both types of preferences, a set of personalized recommendations is generated. We conducted a user study measuring the usability of the proposed interaction. The results of the study show that the majority of users rates the quality of the recommendations high and the system achieves usability scores above the standard benchmark.
---
paper_title: Dealing with Malnutrition: A Meal Planning System for Elderly

paper_content:

Malnutrition is a serious problem among people of old age. To overcome malnutrition, a change of food consumption behaviour is necessary, which needs to be based on specialist advice from health-care professionals. Changing food-related behaviour, however, is known to be difficult. Our approach to this problem is to provide an intelligent meal planning system to be used by the elderly person in his or her home. The system provides recommendations of suitable food recipes, taking into account the advice of the care givers (e.g. in terms of dietary restrictions, suitable energy and fat levels, etc). We describe the requirements, design, algorithms, and user interface of the system, and discuss ongoing and future work.
---
paper_title: A recipe recommendation system based on automatic nutrition information extraction

paper_content:

In this paper, we propose a goal-oriented recipe recommendation system that utilizes information about nutrition on the Internet. Our system enables users without knowledge about nutrition to search easily for recipes with natural language to improve specific health conditions. The natural language includes 'I want to cure my acne' and 'I want to recover from my fatigue'. To do that, we created a co-occurrence database that listed the co-occurrence of 45 common nutrients with nouns such as cold, acne, bone etc. Then we created a recipe database by collecting 800,000 recipes from www.cookpad.com the system and analyzed each recipe to calculate the amount of a nutrient in a dish. We compared the results of our system to the results we obtained by calculating the nutrient information manually. Evaluation was done on 1000 dishes. We measured the effectiveness of the system using F-Measure and the average F-measure was 0.64 respectively.
---
paper_title: Bringing the "healthy" into Food Recommenders

paper_content:

Lifestyle-related illnesses such as diabetes and obesity are a major problem in the modern world but can be prevented and sometimes even reversed through good nutrition [3]. Food recommender systems have been touted as a potential means to assist people nourish themselves more healthily e.g. [2, 5]. Anecdotally it makes sense to utilise food recommenders as part of a strategy for behavioural change because if you can suggest a change that is less painful then it seems more likely that the user will accept that change and stick with it. If we are interested in recommending meals to provide a balanced diet, however, such systems have a major limitation: the way they work means they learn user preferences for ingredients and food styles, which, of course, leads to users who like and tend to eat fatand calorie-laden meals being recommended fatand calorie-laden meals an outcome not conducive to improving nutritional habits. In this position statement we briefly outline two ways in which the recommendation problem can be reformulated to also encompass nutritional aspects and not just user preferences.
---
paper_title: The Harris Benedict equation reevaluated: resting energy requirements and the body cell mass

paper_content:

The Harris Benedict equations (HBE) were derived from indirect calorimetric data obtained in 239 normal subjects. Using these data and additional data published by Benedict, which were obtained from subjects spanning a wider age range (n = 98), the present study evaluated the relationship between measured resting energy expenditure and age, sex, and predicted body cell mass (BCM). When the additional subjects from the subsequently published series are included, the regression equations, standard error of the estimate, and 95% confidence limits are similar to the original equations. The HBE estimate resting energy expenditure of a normal subject with a precision of 14%. Resting energy expenditure is directly related to the size of the BCM and is independent of age and sex. The variables of height, weight, age, and sex in the HBE reflect the relationship between body weight and the BCM. Indirect calorimetry and body composition measurements were performed in both normally nourished and malnourished patients (n = 74) to assess the accuracy of the HBE in malnourished patients. Malnutrition is associated with an increase in resting oxygen consumption (VO2) which becomes apparent only when VO2 is expressed as a function of the BCM. There is no difference in resting VO2 between the sexes when expressed as a function of BCM. A regression equation was derived from the Harris Benedict data to predict resting VO2 from age, height, weight, and sex. Predicted VO2 was not significantly different from measured VO2 for the normally nourished patients (n = 33) whereas in the malnourished (n = 41) predicted VO2 underestimated the measured value. The HBE accurately predict resting energy expenditure in normally nourished individuals with a precision of +/- 14%, but are unreliable in the malnourished patient.
---
paper_title: Biases in Decision Making

paper_content:

Decisions are typically taken on the basis of heuristics that are a door opener for different types of decision biases. Such biases can be interpreted as a tendency to decide in certain simplified ways which can often lead to suboptimal decision outcomes. Recommender systems support users in different types of decision making tasks and thus should be aware of such biases. In this paper we provide a short overview of different types of decision biases and their impacts on recommender systems. We also discuss some issues for future work.
---
paper_title: Polylens: A recommender system for groups of user

paper_content:

We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email
---
paper_title: Group-based recipe recommendations: analysis of data aggregation strategies

paper_content:

Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.
---
paper_title: Group Recommender Systems: New Perspectives in the Social Web

paper_content:

An increasingly important type of recommender systems comprises those that generate suggestions for groups rather than for individuals. In this chapter, we revise state of the art approaches on group formation, modelling and recommendation, and present challenging problems to be included in the group recommender system research agenda in the context of the Social Web.
---
paper_title: Recipe recommendation: accuracy and reasoning

paper_content:

Food and diet are complex domains for recommender technology, but the need for systems that assist users in embarking on and engaging with healthy living programs has never been more real. One key to sustaining long term engagement with eHealth services is the provision of tools, which assist and train users in planning correctly around the areas of diet and exercise. These tools require an understanding of user reasoning as well as user needs and are ideal application areas for recommender and personalization technologies. Here, we report on a large scale analysis of real user ratings on a set of recipes in order to judge the applicability and practicality of a number of personalization algorithms. Further to this, we report on apparent user reasoning patterns uncovered in rating data supplied for recipes and suggest ways to exploit this reasoning understanding in the recommendation process.
---
paper_title: Group recommender systems: a critiquing based approach

paper_content:

Group recommender systems introduce a whole set of new challenges for recommender systems research. The notion of generating a set of recommendations that will satisfy a group of users, with potentially competing interests, is challenging in itself. In addition to this we must consider how to record and combine the preferences of many different users as they engage in simultaneous recommendation dialogs. In this paper we introduce a group recommender system that is designed to provide assistance to a group of friends trying to plan a skiing vacation.
---
paper_title: Critiquing-based recommenders: survey and emerging trends

paper_content:

Critiquing-based recommender systems elicit users' feedback, called critiques, which they made on the recommended items. This conversational style of interaction is in contract to the standard model where users receive recommendations in a single interaction. Through the use of the critiquing feedback, the recommender systems are able to more accurately learn the users' profiles, and therefore suggest better recommendations in the subsequent rounds. Critiquing-based recommenders have been widely studied in knowledge-, content-, and preference-based recommenders and are beginning to be tried in several online websites, such as MovieLens. This article examines the motivation and development of the subject area, and offers a detailed survey of the state of the art concerning the design of critiquing interfaces and development of algorithms for critiquing generation. With the help of categorization analysis, the survey reveals three principal branches of critiquing based recommender systems, using respectively natural language based, system-suggested, and user-initiated critiques. Representative example systems will be presented and analyzed for each branch, and their respective pros and cons will be discussed. Subsequently, a hybrid framework is developed to unify the advantages of different methods and overcome their respective limitations. Empirical findings from user studies are further presented, indicating how hybrid critiquing supports could effectively enable end-users to achieve more confident decisions. Finally, the article will point out several future trends to boost the advance of critiquing-based recommenders.
---
paper_title: Interactive Food Recommendation for Groups

paper_content:

We present a prototype of a novel interactive food recommender for groups of users that supports groups in planning their meals through a conversational process based on critiquing. The system comprises two novel elements: a user interface and interaction design based on tagging and critiquing, and a utility function incorporating healthiness and diet compliance factors.
---
paper_title: Group Recommender Systems: Combining individual models

paper_content:

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.
---
paper_title: Choicla: towards domain-independent decision support for groups of users

paper_content:

Group recommendation technologies have been successfully applied in domains such as interactive television, music, and tourist destinations. Existing technologies are focusing on specific domains and do not offer the possibility of supporting different kinds of decision scenarios. The Choicla group decision support environment advances the state of the art by supporting decision scenarios in a domain-independent fashion. In this paper we give an overview of the Choicla environment and report the results of a first user study which focused on system usability.
---
paper_title: Dealing with Malnutrition: A Meal Planning System for Elderly

paper_content:

Malnutrition is a serious problem among people of old age. To overcome malnutrition, a change of food consumption behaviour is necessary, which needs to be based on specialist advice from health-care professionals. Changing food-related behaviour, however, is known to be difficult. Our approach to this problem is to provide an intelligent meal planning system to be used by the elderly person in his or her home. The system provides recommendations of suitable food recipes, taking into account the advice of the care givers (e.g. in terms of dietary restrictions, suitable energy and fat levels, etc). We describe the requirements, design, algorithms, and user interface of the system, and discuss ongoing and future work.
---
paper_title: Group-based recipe recommendations: analysis of data aggregation strategies

paper_content:

Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.
---
paper_title: A recipe recommendation system based on automatic nutrition information extraction

paper_content:

In this paper, we propose a goal-oriented recipe recommendation system that utilizes information about nutrition on the Internet. Our system enables users without knowledge about nutrition to search easily for recipes with natural language to improve specific health conditions. The natural language includes 'I want to cure my acne' and 'I want to recover from my fatigue'. To do that, we created a co-occurrence database that listed the co-occurrence of 45 common nutrients with nouns such as cold, acne, bone etc. Then we created a recipe database by collecting 800,000 recipes from www.cookpad.com the system and analyzed each recipe to calculate the amount of a nutrient in a dish. We compared the results of our system to the results we obtained by calculating the nutrient information manually. Evaluation was done on 1000 dishes. We measured the effectiveness of the system using F-Measure and the average F-measure was 0.64 respectively.
---
paper_title: Challenges for Nutrition Recommender Systems

paper_content:

Obesity or being overweight in general often leads to other more severe diseases. With more than half of the population in the west- ern countries being overweight or even obese, systems are developed to help users make healthier food choices by recommending healthier recipes or food items according to the user's needs or likes and dislikes. But there are many challenges designing such systems. In this paper we try to iden- tify the difficulties encountered and discuss ways to deal withthem.
---
paper_title: Intelligent food planning: personalized recipe recommendation

paper_content:

As the obesity epidemic takes hold across the world many medical professionals are referring users to online systems aimed at educating and persuading users to alter their lifestyle. The challenge for many of these systems is to increase initial adoption and sustain participation for sufficient time to have real impact on the life of its users. In this work we present some preliminary investigation into the design of a recipe recommender, aimed at educating and sustaining user participation, which makes tailored recommendations of healthy recipes. We concentrate on the two initial dimensions of food recommendations: data capture and food-recipe relationships and present a study into the suitability of varying recommender algorithms for the recommendation of recipes.
---
paper_title: Deriving a recipe similarity measure for recommending healthful meals

paper_content:

A recipe recommender system may stimulate healthful and varied eating, when the presented recipes fit the lifestyle of the user. As consumers face the barrier to change their eating and cooking behavior, we aim for a strategy to provide more healthful variations to routine recipes. In this paper, a similarity measure for recipes is derived by taking a user-centered approach. Such a measure can be used to recommend healthier alternatives to commonly selected meals, which are perceived to be similar. Recipes presented using this strategy may fit the demand for health and variation within the boundaries of a busy lifestyle. Having derived and evaluated a recipe similarity measure, we explore its use through an at-home trial.
---
paper_title: Interactive Food Recommendation for Groups

paper_content:

We present a prototype of a novel interactive food recommender for groups of users that supports groups in planning their meals through a conversational process based on critiquing. The system comprises two novel elements: a user interface and interaction design based on tagging and critiquing, and a utility function incorporating healthiness and diet compliance factors.
---
paper_title: Food Log by snapping and processing images

paper_content:

We present the current status of FoodLog, a multimedia Internet application that enables easy capture and archival of information regarding our daily meals. The primary purpose of FoodLog is to facilitate dietary management support with minimum manual recording of information. It analyzes image archives that belong to a user to identify images of meals. Further image analysis determines the nutritional composition of these meals and stores the data to form a log. The user can view the data from this log in different formats, and also edit the data to correct any mistakes that occurred during image analysis. This application was recently opened to the public, and had accumulated approximately 25000 images during the first two months since its launch. We present the current status of this application, and discuss our future plans to extend it to allow interaction between users and more effective dietary management.
---
paper_title: Towards Open Configuration

paper_content:

Configuration technologies are typically applied in closed settings where one (or a small group of) knowledge engineer(s) is in charge of knowledge base development and maintenance. In such settings it is also assumed that only single users configure the corresponding products and services. Nowadays, a couple of scenarios exist that require more openness: it should be possible to cooperatively develop knowledge bases and to jointly configure products and services, even by adding new features or constraints in a flexible fashion. We denote this integration of groups of users into configuration-related tasks as open configuration. In this paper we introduce features of open configuration environments and potential approaches to implement these features.
---
paper_title: Challenges for Nutrition Recommender Systems

paper_content:

Obesity or being overweight in general often leads to other more severe diseases. With more than half of the population in the west- ern countries being overweight or even obese, systems are developed to help users make healthier food choices by recommending healthier recipes or food items according to the user's needs or likes and dislikes. But there are many challenges designing such systems. In this paper we try to iden- tify the difficulties encountered and discuss ways to deal withthem.
---
paper_title: Deriving a recipe similarity measure for recommending healthful meals

paper_content:

A recipe recommender system may stimulate healthful and varied eating, when the presented recipes fit the lifestyle of the user. As consumers face the barrier to change their eating and cooking behavior, we aim for a strategy to provide more healthful variations to routine recipes. In this paper, a similarity measure for recipes is derived by taking a user-centered approach. Such a measure can be used to recommend healthier alternatives to commonly selected meals, which are perceived to be similar. Recipes presented using this strategy may fit the demand for health and variation within the boundaries of a busy lifestyle. Having derived and evaluated a recipe similarity measure, we explore its use through an at-home trial.
---
paper_title: System response time and user satisfaction: An experimental study of browser-based applications

paper_content:

With rapid advances in hardware speed and data communication bandwidth, one might not expect to have to deal with issues such as response time and system performance. But these issues remain a very real concern today. Lengthy system response times may cause lower satisfaction and poor productivity among users. Lowered user satisfaction may lead to discontinued use of an application, especially in discretionary applications such as those found on the Internet. The intent of this experimental research is to (1) substantiate that slow system response time leads to dissatisfaction; (2) assess the point at which users may become dissatisfied with system response time; (3) determine a threshold at which dissatisfaction may lead to discontinued use of the application, and (4) determine if experience influences response time tolerance. The results showed that indeed satisfaction does decrease as response time increases. However, instant response was not perceived as making the system easier to use or learn. It also showed that for discretionary applications, there appears to be a level of intolerance in the 12-second response range. Background and Introduction With rapid advances in hardware speed and data communication bandwidth, one might not expect to have to deal with issues such as response time and system performance. But these issues remain a very real concern today. As systems become more powerful, their applications become more complex as users expect more functionality and richer information. While response time is recognized as a component in some application usability and human factors studies (Goodwin, 1987; Nielsen, 1994; Shneiderman, 1991; Shneiderman, 1998) users today face similar system response time issues that faced users a decade ago. As web developers experience slow response times due to lengthy downloads of graphic intensive material, they are redesigning the sites to minimize this overhead. This will not be an acceptable long-term solution to accommodating electronic commerce. The network may have pockets of high-speed access and adequate bandwidth, but overall high-speed access is unavailable and many of the sites may not have been designed to handle the traffic and data volume
---
paper_title: Using Low-Cost Sensing to Support Nutritional Awareness

paper_content:

Nutrition has a big impact on health, including major diseases such as heart disease, osteoporosis, and cancer. This paper presents an application designed to help people keep track of the nutrional content of foods they have eaten. Our work uses shopping receipts to generate suggestions about healthier food items that could help to supplement missing nutrients. We present our system design: a capture and access application that, based on shopping receipt data, provides access to ambiguous suggestions for more nutritious purchases. We also report results from one formative user study suggesting that receipts may provide enough information to extend our work by also estimating what people are actually eating, as opposed to simply what they are purchasing.
---
paper_title: A Survey of Explanations in Recommender Systems

paper_content:

This paper provides a comprehensive review of explanations in recommender systems. We highlight seven possible advantages of an explanation facility, and describe how existing measures can be used to evaluate the quality of explanations. Since explanations are not independent of the recommendation process, we consider how the ways recommendations are presented may affect explanations. Next, we look at different ways of interacting with explanations. The paper is illustrated with examples of explanations throughout, where possible from existing applications.
---
paper_title: Interactive Food Recommendation for Groups

paper_content:

We present a prototype of a novel interactive food recommender for groups of users that supports groups in planning their meals through a conversational process based on critiquing. The system comprises two novel elements: a user interface and interaction design based on tagging and critiquing, and a utility function incorporating healthiness and diet compliance factors.
---
paper_title: Polylens: A recommender system for groups of user

paper_content:

We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email
---
paper_title: A Consensus-Driven Group Recommender System

paper_content:

Recommender systems aim at filtering large amounts of information for users, providing them with those pieces of information which better meet their preferences or needs. Such systems have been traditionally used in diverse areas, such as e-commerce or tourism. Within this context, group recommender systems address the problem of generating recommendations for groups of users who might have different interests. Although different aggregation processes have been extensively utilized in real-life applications to generate group recommendations, such processes do not guarantee that the list of products recommended to the group reflect a high agreement level among its members' individual preferences. Given the need for considering the added value of obtaining group recommendations under a high agreement level, this paper presents a novel group recommender system methodology that attempts to reach a high level of consensus among individual recommendations of group members. To do this, and inspired by existing group decision-making approaches in the literature, a consensus reaching process is carried out to bring such individual recommendations closer to each other before delivering the group recommendations.
---
paper_title: Group-based recipe recommendations: analysis of data aggregation strategies

paper_content:

Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.
---
paper_title: Deriving a recipe similarity measure for recommending healthful meals

paper_content:

A recipe recommender system may stimulate healthful and varied eating, when the presented recipes fit the lifestyle of the user. As consumers face the barrier to change their eating and cooking behavior, we aim for a strategy to provide more healthful variations to routine recipes. In this paper, a similarity measure for recipes is derived by taking a user-centered approach. Such a measure can be used to recommend healthier alternatives to commonly selected meals, which are perceived to be similar. Recipes presented using this strategy may fit the demand for health and variation within the boundaries of a busy lifestyle. Having derived and evaluated a recipe similarity measure, we explore its use through an at-home trial.
---
paper_title: Towards Open Configuration

paper_content:

Configuration technologies are typically applied in closed settings where one (or a small group of) knowledge engineer(s) is in charge of knowledge base development and maintenance. In such settings it is also assumed that only single users configure the corresponding products and services. Nowadays, a couple of scenarios exist that require more openness: it should be possible to cooperatively develop knowledge bases and to jointly configure products and services, even by adding new features or constraints in a flexible fashion. We denote this integration of groups of users into configuration-related tasks as open configuration. In this paper we introduce features of open configuration environments and potential approaches to implement these features.
---
paper_title: Group Recommender Systems: Combining individual models

paper_content:

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple criteria.
---

Title: An Overview of Recommender Systems in the Healthy Food Domain

Section 1: Introduction
Description 1: Write about the significance of healthy eating habits, the challenges in dietary intake, and the role of recommender systems in promoting healthy food choices.

Section 2: Recommender Systems
Description 2: Discuss the concept of recommender systems, their benefits, and the different fields in which they are applied.

Section 3: Recommendation Techniques for Individuals
Description 3: Provide an overview of various recommendation techniques including collaborative filtering, content-based systems, knowledge-based systems, and hybrid systems.

Section 4: Recommendation Techniques for Groups
Description 4: Present the aspects of group-based recommendation including aggregation strategies, group formation, group recommendation approaches, and group decision making.

Section 5: Food Recommender Systems
Description 5: Discuss the application of recommender systems in the food domain and categorize different types of food recommender systems.

Section 6: Type 1: Considering User Preferences
Description 6: Explore systems focusing on recommending food items or menus based on user tastes and preferences.

Section 7: Type 2: Considering Nutritional Needs of Users
Description 7: Examine systems designed to recommend food items while addressing the nutritional needs and health problems of users.

Section 8: Type 3: Balancing Between User Preferences and Nutritional Needs of Users
Description 8: Discuss systems that balance user preferences with nutritional needs to provide optimized food recommendations.

Section 9: Type 4: Food Recommender Systems for Groups
Description 9: Analyze systems that generate food recommendations tailored to groups, considering group preferences and decision-making dynamics.

Section 10: Research Challenges
Description 10: Identify and discuss the key challenges in food recommender systems related to user information, recommendation algorithms, changing eating behaviors, explanations provision, and group decision making.

Section 11: Conclusion
Description 11: Summarize the findings, contributions, and future directions for food recommender systems in the healthy food domain.