A Survey of Recommendation Algorithms
---
paper_title: Content-based recommendation systems

paper_content:

This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.
---
paper_title: A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering

paper_content:

Personalized recommendation systems can help people to find interesting things and they are widely used with the development of electronic commerce. Many recommendation systems employ the collaborative filtering technology, which has been proved to be one of the most successful techniques in recommender systems in recent years. With the gradual increase of customers and products in electronic commerce systems, the time consuming nearest neighbor collaborative filtering search of the target customer in the total customer space resulted in the failure of ensuring the real time requirement of recommender system. At the same time, it suffers from its poor quality when the number of the records in the user database increases. Sparsity of source data set is the major reason causing the poor quality. To solve the problems of scalability and sparsity in the collaborative filtering, this paper proposed a personalized recommendation approach joins the user clustering technology and item clustering technology. Users are clustered based on users’ ratings on items, and each users cluster has a cluster center. Based on the similarity between target user and cluster centers, the nearest neighbors of target user can be found and smooth the prediction where necessary. Then, the proposed approach utilizes the item clustering collaborative filtering to produce the recommendations. The recommendation joining user clustering and item clustering collaborative filtering is more scalable and more accurate than the traditional one.
---
paper_title: User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop

paper_content:

Collaborative Filtering(CF) algorithms are widely used in a lot of recommender systems, however, the computational complexity of CF is high thus hinder their use in large scale systems. In this paper, we implement user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF. Experimental results show that a simple method that partition users into groups according to two basic principles, i.e., tidy arrangement of mapper number to overcome the initiation of mapper and partition task equally such that all processors finish task at the same time, can achieve linear speedup.
---
paper_title: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

paper_content:

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
---
paper_title: Hybrid Recommender Systems: Survey and Experiments

paper_content:

Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.
---

Title: A Survey of Recommendation Algorithms
Section 1: Introduction
Description 1: Provide an overview of information filtering systems, service recommender systems, and the challenges posed by big data on recommendation systems.

Section 2: An Overview Of Recommendation Algorithm
Description 2: Outline the main types of recommendation algorithms, including their methodologies and limitations.

Section 3: Content-based Recommendations
Description 3: Explain how content-based recommendation systems function, their techniques, and their limitations.

Section 4: Collaborative Recommendations
Description 4: Detail collaborative filtering techniques, separating them into item-based and user-based approaches, including their advantages and disadvantages.

Section 5: Knowledge-based Recommendations
Description 5: Describe how knowledge-based recommender systems operate, their requirements, and unique benefits and challenges.

Section 6: Demographic Recommendations
Description 6: Discuss how demographical data is used in recommendation systems, along with the benefits and limitations of this approach.

Section 7: Hybrid Recommendations
Description 7: Examine how hybrid recommendation systems combine various techniques, including descriptions of several hybridization methodologies.

Section 8: Conclusion
Description 8: Summarize the pros and cons of different recommendation methods and discuss the significance of hybrid approaches and cloud computing for improving recommendation systems.