Survey of Data Mining Approaches to User Modeling for Adaptive Hypermedia
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paper_title: Predictive Statistical Models for User Modeling

paper_content:

The limitations of traditional knowledge representation methods for modeling complex human behaviour led to the investigation of statistical models. Predictive statistical models enable the anticipation of certain aspects of human behaviour, such as goals, actions and preferences. In this paper, we motivate the development of these models in the context of the user modeling enterprise. We then review the two main approaches to predictive statistical modeling, content-based and collaborative, and discuss the main techniques used to develop predictive statistical models. We also consider the evaluation requirements of these models in the user modeling context, and propose topics for future research.
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paper_title: Adaptable and Adaptive Information Access for All Users, Including the Disabled and the Elderly

paper_content:

The tremendously increasing popularity of the World Wide Web indicates that hypermedia is going to be the leading online information medium for the years to come and will most likely be the standard gateway to the “information highway”. Visitors of web sites are generally heterogeneous and have different needs, and this trend is likely even to increase in the future. The aim of the AVANTI project is to cater hypermedia information to these different needs by adapting the content and the presentation of web pages to each individual user. The special needs of elderly and handicapped users are also considered to some extent. Our experience from this research is that adaptation and user modeling techniques that have so far almost exclusively focused on adapting interactive software systems to “normal” users also prove useful for adaptation to users with special needs.
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paper_title: User as Student: Towards an Adaptive Interface for Advanced Web-Based Applications

paper_content:

This paper discusses the problems of developing adaptive self-explaining interfaces for advanced World-Wide Web (WWW) applications. Two kinds of adaptation are considered: incremental learning and incremental interfaces. The key problem for these kinds of adaptation is to decide which interface features should be explained or enabled next. We analyze possible ways to implement incremental learning and incremental interfaces on the WWW and suggest a “user as student” approach. With this approach, the order of learning or enabling of interface features is determined by adaptive sequencing, a popular intelligent tutoring technology, which is based on the pedagogical model of the interface and user knowledge about it. We describe in detail how this approach was implemented in the InterBook system, a shell for developing Web-based adaptive electronic textbooks.
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paper_title: Data Mining Practical Machine Learning Tools And Techniques With Java Implementations

paper_content:

Thank you for reading data mining practical machine learning tools and techniques with java implementations. As you may know, people have look hundreds times for their favorite novels like this data mining practical machine learning tools and techniques with java implementations, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their laptop.
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paper_title: Generic user modeling systems

paper_content:

This chapter reviews research results in the field of Generic User Modeling Systems. It describes the purposes of such systems, their services within user-adaptive systems, and the different design requirements for research prototypes and commercial deployments. It discusses the architectures that have been explored so far, namely shell systems that form part of the application, central server systems that communicate with several applications, and possible future agent-based user modeling systems. Major implemented research prototypes and commercial systems are briefly described.
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paper_title: Machine Learning for User Modeling

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At first blush, user modeling appears to be a prime candidate for straightforward application of standard machine learning techniques. Observations of the user's behavior can provide training examples that a machine learning system can use to form a model designed to predict future actions. However, user modeling poses a number of challenges for machine learning that have hindered its application in user modeling, including: the need for large data sets; the need for labeled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.
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paper_title: Some methods for classification and analysis of multivariate observations

paper_content:

The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special
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paper_title: Low-complexity fuzzy relational clustering algorithms for web mining

paper_content:

This paper presents new algorithms-fuzzy c-medoids (FCMdd) and robust fuzzy c-medoids (RFCMdd)-for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. A comparison of FCMdd with the well-known relational fuzzy c-means algorithm (RFCM) shows that FCMdd is more efficient. We present several applications of these algorithms to Web mining, including Web document clustering, snippet clustering, and Web access log analysis.
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paper_title: Effective personalization based on association rule discovery from web usage data

paper_content:

To engage visitors to a Web site at a very early stage (i.e., before registration or authentication), personalization tools must rely primarily on clickstream data captured in Web server logs. The lack of explicit user ratings as well as the sparse nature and the large volume of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Web usage mining techniques such as clustering that rely on offline pattern discovery from user transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy. In this paper we propose effective and scalable techniques for Web personalization based on association rule discovery from usage data. Through detailed experimental evaluation on real usage data, we show that the proposed methodology can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy.
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paper_title: Automating Personal Categorization Using Artificial Neural Networks

paper_content:

Organizations as well as personal users invest a great deal of time in assigning documents they read or write to categories. Automatic document classification that matches user subjective classification is widely used, but much challenging research still remain to be done. The self-organizing map (SOM) is an artificial neural network (ANN) that is mathematically characterized by transforming high-dimensional data into two-dimensional representation. This enables automatic clustering of the input, while preserving higher order topology. A closely related method is the Learning Vector Quantization (LVQ) algorithm, which uses supervised learning to maximize correct data classification. This study evaluates and compares the application of SOM and LVQ to automatic document classification, based on a subjectively predefined set of clusters in a specific domain. A set of documents from an organization, manually clustered by a domain expert, was used in the experiment. Results show that in spite of the subjective nature of human categorization, automatic document clustering methods match with considerable success subjective, personal clustering, the LVQ method being more advantageous.
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paper_title: Symbolic data analysis with the k–means algorithm for user profiling

paper_content:

We propose to simplify human-machine interaction by automating device settings that are normally made manually. We present here a classification scheme of user behaviours based on an adaptation of the K-means algorithm to symbolic data representing user behaviours. This classification enables a system to derive prototypical behaviours and to control device settings automatically.
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paper_title: Low-complexity fuzzy relational clustering algorithms for web mining

paper_content:

This paper presents new algorithms-fuzzy c-medoids (FCMdd) and robust fuzzy c-medoids (RFCMdd)-for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. A comparison of FCMdd with the well-known relational fuzzy c-means algorithm (RFCM) shows that FCMdd is more efficient. We present several applications of these algorithms to Web mining, including Web document clustering, snippet clustering, and Web access log analysis.
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paper_title: On Mining Web Access Logs

paper_content:

Abstract : The proliferation of information on the world wide web has made the personalization of this information space a necessity. One possible approach to web personalization is to mine typical user profiles from the vast amount of historical data stored in access logs. In the absence of any a priori knowledge, unsupervised classification or clustering methods seem to be ideally suited to analyze the semi-structured log data of user accesses. In this paper, we define the notion of a user session , as well as a dissimilarity measure between two web sessions that captures the organization of a web site. To extract a user access profile, we cluster the user sessions based on the pair-wise dissimilarities using a robust fuzzy clustering algorithm that we have developed. We report the results of experiments with our algorithm and show that this leads to extraction of interesting user profiles. We also show that it outperforms association rule based approaches for this task.
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paper_title: On Mining Web Access Logs

paper_content:

Abstract : The proliferation of information on the world wide web has made the personalization of this information space a necessity. One possible approach to web personalization is to mine typical user profiles from the vast amount of historical data stored in access logs. In the absence of any a priori knowledge, unsupervised classification or clustering methods seem to be ideally suited to analyze the semi-structured log data of user accesses. In this paper, we define the notion of a user session , as well as a dissimilarity measure between two web sessions that captures the organization of a web site. To extract a user access profile, we cluster the user sessions based on the pair-wise dissimilarities using a robust fuzzy clustering algorithm that we have developed. We report the results of experiments with our algorithm and show that this leads to extraction of interesting user profiles. We also show that it outperforms association rule based approaches for this task.
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paper_title: User Intention Modeling in Web Applications Using Data Mining

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The problem of inferring a user's intentions in Machine–Human Interaction has been the key research issue for providing personalized experiences and services. In this paper, we propose novel approaches on modeling and inferring user's actions in a computer. Two linguistic features – keyword and concept features – are extracted from the semantic context for intention modeling. Concept features are the conceptual generalization of keywords. Association rule mining is used to find the proper concept of corresponding keyword. A modified Naive Bayes classifier is used in our intention modeling. Experimental results have shown that our proposed approach achieved 84% average accuracy in predicting user's intention, which is close to the precision (92%) of human prediction.
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paper_title: Effective personalization based on association rule discovery from web usage data

paper_content:

To engage visitors to a Web site at a very early stage (i.e., before registration or authentication), personalization tools must rely primarily on clickstream data captured in Web server logs. The lack of explicit user ratings as well as the sparse nature and the large volume of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Web usage mining techniques such as clustering that rely on offline pattern discovery from user transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy. In this paper we propose effective and scalable techniques for Web personalization based on association rule discovery from usage data. Through detailed experimental evaluation on real usage data, we show that the proposed methodology can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy.
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paper_title: Effective Prediction of Web-user Accesses: A Data Mining Approach

paper_content:

The problem of predicting web-user accesses has recently attracted significant attention. Several algorithms have been proposed, which find important applications, like user profiling, recommender systems, web prefetching, design of adaptive web sites, etc. In all these applications the core issue is the developement of an effective prediction algorithm. In this paper, we focus on web-prefetching, because of its importance in reducing user perceived latency present in every Web-based application. The proposed method can be easily extended to the other aforementioned applications. Prefetching refers to the mechanism of deducing forthcoming page accesses of a client, based on access log information. We examine a method that is based on a new type of association patterns, which differently from existing approaches, considers all the specific characteristics of the Web-user navigation. Experimental results indicate its superiority over existing methods. Index Terms — Prediction, Web Log Mining, Prefetching, Association Rules, Data Mining.
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paper_title: User Modeling for Efficient Use of Multimedia Files

paper_content:

It is very common that a user likes to collect many multimedia files of their interests from the web or other sources for his/her daily use, such as in emails, presentations, and technical documents. This paper presents algorithms to learn user models, in particular, user intention models and preference models from the usage of these files. Such usages include downloading, inserting, and sending multimedia files. A user intention model predicts when the user may want to involve some multimedia objects in his currently working environment (e.g., an email) and provides more convenient and accurate help to the user. A user preference model describes the types and classes of the user's favorite multimedia files and helps an offline crawler to autonomously collect more useful multimedia files for the user. The algorithms have been implemented in our media agents system and shown their effectiveness in user modeling.
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paper_title: Mining association rules between sets of items in large databases

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We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
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paper_title: From Computational Intelligence to Web Intelligence: An Ensemble from Potpourri

paper_content:

The advent of the internet has changed the world in possibly more significant ways than any other event in the history of humanity. Is internet access and use beyond the reach of ordinary people with ordinary intelligence? Ignoring for the moment economic issues of access for all citizenry, what is it about internet access and use that hinders more widespread acceptability? We explore several issues, not exclusive, that attempt to provoke and poke at answers to these simple questions. Largely speculative, as invited talks ought to be, we explore 3 topics, well studied but as yet generally unsolved, in computational intelligence and explore their impact on web intelligence. These topics are machine translation, machine learning, and user interface design. Conclusion will be mine; readers will draw general conclusions.
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paper_title: The application of nearest neighbor algorithm on creating an adaptive on-line learning system

paper_content:

The purpose of this research is the development of an online learning system by the application of nearest neighbor algorithm for providing adaptive learning materials on the course of introduction to computer networks. There are three major tasks included in the research: (1) categorization of the materials of computer networks; (2) application of the nearest neighbor algorithm for retrieving the most adequate materials for different students when they are learning via the World Wide Web (WWW); and (3) identification of changes in thought of students' knowledge on computer networks after taking the on-line course. Compared with previous research into adaptive learning, the application of the nearest neighbor algorithm could retrieve more adequate materials from the library based on previous similar cases. Therefore, the learning performance of each individual could be improved.
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paper_title: Learning a Model of a Web User's Interests

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There are many recommender systems that are designed to help users find relevant information on the web. To produce recommendations that are relevant to an individual user, many of these systems first attempt to learn a model of the user's browsing behavior. This paper presents a novel method for learning such a model from a set of annotated web logs--i.e., web logs that are augmented with the user's assessment of whether each webpage is an information content (IC) page (i.e., contains the information required to complete her task). Our systems use this to learn what properties of a webpage, within a sequence, identify such IC-pages, and similarly what "browsing properties" characterize the words on such pages ("IC-words"). As these methods deal with properties of web pages (or of words), rather than specific URLs (words), they can be used anywhere throughout the web; i.e., they are not specific to a particular website, or a particular task. This paper also describes the enhanced browser, aie, that we designed and implemented for collecting these annotated web logs, and an empirical study we conducted to investigate the effectiveness of our approach. This empirical evidence shows that our approach, and our algorithms, work effectively.
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paper_title: Machine Learning for User Modeling

paper_content:

At first blush, user modeling appears to be a prime candidate for straightforward application of standard machine learning techniques. Observations of the user's behavior can provide training examples that a machine learning system can use to form a model designed to predict future actions. However, user modeling poses a number of challenges for machine learning that have hindered its application in user modeling, including: the need for large data sets; the need for labeled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.
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paper_title: Discovering Prediction Rules in AHA! Courses

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In this paper we are going to show how to discover interesting prediction rules from student usage information to improve adaptive web courses. We have used AHA! to make courses that adapt both the presentation and the navigation depending on the level of knowledge that each particular student has. We have performed several modifications in AHA! to specialize it and power it in the educational area. Our objective is to discover relations between all the picked-up usage data (reading times, difficulty levels and test results) from student executions and show the most interesting to the teacher so that he can carry out the appropriate modifications in the course to improve it.
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paper_title: Statistical Data Analysis in the Computer Age

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Most of our familiar statistical methods, such as hypothesis testing, linear regression, analysis of variance, and maximum likelihood estimation, were designed to be implemented on mechanical calculators. Modern electronic computation has encouraged a host of new statistical methods that require fewer distributional assumptions than their predecessors and can be applied to more complicated statistical estimators. These methods allow the scientist to explore and describe data and draw valid statistical inferences without the usual concerns for mathematical tractability. This is possible because traditional methods of mathematical analysis are replaced by specially constructed computer algorithms. Mathematics has not disappeared from statistical theory. It is the main method for deciding which algorithms are correct and efficient tools for automating statistical inference.
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paper_title: C4.5: Programs for Machine Learning

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From the Publisher: ::: Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation. ::: ::: C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies. ::: ::: This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.
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paper_title: Initializing the student model using stereotypes and machine learning

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In this paper we describe the method for initializing the student model in a Web-based Algebra Tutor, which is called Web-EasyMath. The system uses an innovative combination of stereotypes and the distance weighted k-nearest neighbor algorithm to initialize the model of a new student. In particular, the student is first assigned to a stereotype category concerning her/his knowledge level based on her/his performance on a preliminary test. The system then initializes all aspects of the student model using the distance weighted k-nearest neighbor algorithm among the students that belong to the same stereotype category with the new student. The basic idea of the application of the algorithm is to weigh the contribution of each of the neighbor students according to their distance from the new student; the distance between students is calculated based on a similarity measure. In the case of Web-EasyMath the similarity measure is estimated taking into account the school class students belong to, their degree of carefulness while solving exercises as well as their proficiency in using simple arithmetic operations.
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paper_title: Modelling of Novices' Control Skills With Machine Learning

paper_content:

We report an empirical study on the application of machine learning to the modelling of novice controllers’ skills in balancing a pole (inverted pendulum) on top of a cart. Results are presented on the predictive power of the models, and the extent to which they were tailored to each controller. The behaviour of the participants in the study and the behaviour of an interpreter executing their models are compared with respect to the amount of time they were able to keep the pole and cart under control, the degree of stability achieved, and the conditions of failure. We discuss the results of the study, the limitations of the methodology in relation to learner modelling, and we point out future directions of research.
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paper_title: From computational intelligence to Web intelligence

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The authors explore three topics in computational intelligence: machine translation, machine learning and user interface design and speculate on their effects on Web intelligence. Systems that can communicate naturally and learn from interactions will power Web intelligence's long term success. The large number of problems requiring Web-specific solutions demand a sustained and complementary effort to advance fundamental machine-learning research and incorporate a learning component into every Internet interaction. Traditional forms of machine translation either translate poorly, require resources that grow exponentially with the number of languages translated, or simplify language excessively. Recent success in statistical, nonlinguistic, and hybrid machine translation suggests that systems based on these technologies can achieve better results with a large annotated language corpus. Adapting existing computational intelligence solutions, when appropriate for Web intelligence applications, must incorporate a robust notion of learning that will scale to the Web, adapt to individual user requirements, and personalize interfaces.
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paper_title: Automatic Web-Page Classification by Using Machine Learning Methods

paper_content:

This paper describes automatic Web-page classification by using machine learning methods. Recently, the importance of portal site services is increasing including the search engine function on World Wide Web. Especially, the portal site such as Yahoo! service, which hierarchically classifies Web-pages into many categories, is becoming popular. However, the classification of Web-page into each category relies on man power, which costs much time and care. To alleviate this problem, we propose techniques to generate attributes by using co-occurrence analysis and to classify Web-page automatically based on machine learning. We apply these techniques to Web-pages on Yahoo! JAPAN and construct decision trees, which determine appropriate category for each Web-page. The performance of this proposed method is evaluated in terms of error rate, recall, and precision. The experimental evaluation demonstrates that this method provides acceptable accuracy with the classification of Web-page into top level categories on Yahoo! JAPAN.
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paper_title: Predicting student help-request behavior in an intelligent tutor for reading

paper_content:

This paper describes our efforts at constructing a fine-grained student model in Project LISTEN's intelligent tutor for reading. Reading is different from most domains that have been studied in the intelligent tutoring community, and presents unique challenges. Constructing a model of the user from voice input and mouse clicks is difficult, as is constructing a model when there is not a well-defined domain model. We use a database describing student interactions with our tutor to train a classifier that predicts whether students will click on a particular word for help with 83.2% accuracy. We have augmented the classifier with features describing properties of the word's individual graphemes, and discuss how such knowledge can be used to assess student skills that cannot be directly measured.
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paper_title: An Exploratory Technique for Investigating Large Quantities of Categorical Data

paper_content:

SUMMARY The technique set out in the paper, CHAID, is an offshoot of AID (Automatic Interaction Detection) designed for a categorized dependent variable. Some important modifications which are relevant to standard AID include: built-in significance testing with the consequence of using the most significant predictor (rather than the most explanatory), multi-way splits (in contrast to binary) and a new type of predictor which is especially useful in handling missing information.
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paper_title: Learning Interaction Models in a Digital Library Service

paper_content:

We present the exploitation of an improved version of the Learning Server for modeling the user interaction in a digital library service architecture. This module is the basic component for providing the service with an added value such as an essential extensible form of interface adaptivity. Indeed, the system is equipped with a web-based visual environment, primarily intended to improve the user interaction by automating the assignment of a suitable interface depending on data relative to the previous experience with the system, coded in log files. The experiments performed show that accurate interaction models can be inferred automatically by using up-to-date learning algorithms.
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paper_title: Data Mining Practical Machine Learning Tools And Techniques With Java Implementations

paper_content:

Thank you for reading data mining practical machine learning tools and techniques with java implementations. As you may know, people have look hundreds times for their favorite novels like this data mining practical machine learning tools and techniques with java implementations, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their laptop.
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paper_title: An Algorithm for Finding Nearest Neighbors

paper_content:

An algorithm that finds the k nearest neighbors of a point, from a sample of size N in a d-dimensional space, with an expected number of distance calculations is described, its properties examined, and the validity of the estimate verified with simulated data.
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paper_title: An introduction to computing with neural nets

paper_content:

Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech and image recognition. These models are composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. Computational elements or nodes are connected via weights that are typically adapted during use to improve performance. There has been a recent resurgence in the field of artificial neural nets caused by new net topologies and algorithms, analog VLSI implementation techniques, and the belief that massive parallelism is essential for high performance speech and image recognition. This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification. These nets are highly parallel building blocks that illustrate neural net components and design principles and can be used to construct more complex systems. In addition to describing these nets, a major emphasis is placed on exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components. Single-layer nets can implement algorithms required by Gaussian maximum-likelihood classifiers and optimum minimum-error classifiers for binary patterns corrupted by noise. More generally, the decision regions required by any classification algorithm can be generated in a straightforward manner by three-layer feed-forward nets.
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paper_title: Adaptive user modeling for filtering electronic news

paper_content:

A prototype system for the fine-grained filtering of news items has been developed and a pilot test has been conducted. The system is based on an adaptive user model that integrates stereotypes and artificial neural networks. The stereotypes are based on newspaper sections and sub-sections, along with editor specified and user specified keywords. Eight subjects trained the system over six days of news papers (986 news items) and then tested the system on a seventh day (171 news items). Five users were simply asked to 'read the news' while three users developed 'corporate' profiles with explicit information needs. The evaluations suggests that such an integrated adaptive user model did, in fact, reflect the difference between the two different types of task. In both cases, the results also reflect the quality of the training of the adaptive neural network by the user in creating the user profile.
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paper_title: Statistical machine learning for tracking hypermedia user behavior

paper_content:

We consider the classification and tracking of user navigation patterns for closed world hypermedia. We use a number of statistical machine learning models and compare them on different instances of the classification/tracking problem using a home made access log database. We conclude on the potential and limitations of these methods for user behavior identification and tracking.
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paper_title: Predicting student help-request behavior in an intelligent tutor for reading

paper_content:

This paper describes our efforts at constructing a fine-grained student model in Project LISTEN's intelligent tutor for reading. Reading is different from most domains that have been studied in the intelligent tutoring community, and presents unique challenges. Constructing a model of the user from voice input and mouse clicks is difficult, as is constructing a model when there is not a well-defined domain model. We use a database describing student interactions with our tutor to train a classifier that predicts whether students will click on a particular word for help with 83.2% accuracy. We have augmented the classifier with features describing properties of the word's individual graphemes, and discuss how such knowledge can be used to assess student skills that cannot be directly measured.
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paper_title: Pattern recognition using neural networks: theory and algorithms for engineers and scientists

paper_content:

Part I FUNDAMENTALS OF PATTERN RECOGNITION 0. Basic Concepts of Pattern Recognition 1. Decision Theoretic Algorithms 2. Structural Pattern Recognition Part II INTRODUCTORY NEURAL NETWORKS 3. Artificial Neural Network Structures 4. Supervised Training via Error Backpropogation: Derivations 5. Acceleration and Stabilization of Supervised Gradient Training of MLPs Part III ADVANCED FUNDAMENTALS OF NEURAL NETWORKS 6. Supervised Training via Strategic Search 7. Advances in Network Algorithms for Recognition 8. Using Hopfield Recurrent Neural Networks Part IV NEURAL, FEATURE, AND DATA ENGINEERING 9. Neural Engineering and Testing of FANNs 10. Feature and Data Engineering
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paper_title: G.: A Connectionist Model of Spatial Knowledge Acquisition in a Virtual Environment

paper_content:

This paper proposes the use of neural networks as a tool for studying navigation within virtual worlds. Results indicate that network learned to predict the next step for a given trajectory, acquiring also basic spatial knowledge in terms of landmarks and configuration of spatial layout. In addition, the network built a spatial representation of the virtual world, e.g. cognitive-like map, which preserves the topology but lacks metric accuracy. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction are discussed.
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paper_title: Using a Learning Agent with a Student Model

paper_content:

In this paper we describe the application of machine learning to the problem of constructing a student model for an intelligent tutoring system. The proposed system learns on a per student basis how long an individual student requires to solve the problem presented by the tutor. This model of relative problem difficulty is learned within a "two-phase" learning algorithm. First, data from the entire student population are used to train a neural network. Second, the system learns how to modify the neural network's output to better fit each individual student's performance. Both components of the model proved useful in improving its accuracy. This model of time to solve a problem is used by the tutor to control the complexity of problems presented to the student.
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paper_title: An introduction to Support Vector Machines

paper_content:

This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. The book also introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc. Their first introduction in the early 1990s lead to a recent explosion of applications and deepening theoretical analysis, that has now established Support Vector Machines along with neural networks as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and application of these techniques. The concepts are introduced gradually in accessible and self-contained stages, though in each stage the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally the book will equip the practitioner to apply the techniques and an associated web site will provide pointers to updated literature, new applications, and on-line software.
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paper_title: Adapting to the user’s internet search strategy

paper_content:

World Wide Web search engines typically return thousands of results to the users. To avoid users browsing through the whole list of results, search engines use ranking algorithms to order the list according to predefined criteria. In this paper, we present Toogle, a front-end to the Google search engine for both desktop browsers and mobile phones. For a given search query, Toogle first ranks results using Google's algorithm and, as the user browses through the result list, uses machine learning techniques to infer a model of her search goal and to adapt accordingly the order in which the results are presented. We describe preliminary experimental results that show the effectiveness of Toogle.
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paper_title: Category Based Customization Approach for Information Retrieval

paper_content:

This paper proposes an customization technique to supporting interactive document retrieval in unorganized open information space like WWW. We assume that taxonomical thought is one of the most important and skilled operations for us when we organize or store information. The proposed methodology, therefore, handles hierarchical categories of documents. The system can be customized through users' modification of categories. The features of the proposed approach are (1) visualization of document categories for interaction, (2) initialization of categories by hierarchical clustering method, (3) customization of categories by support vector machine techniques, (4) additional attributes for individual implicit cognitive aspects.
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paper_title: A training algorithm for optimal margin classifiers

paper_content:

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.
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paper_title: The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users

paper_content:

The Lumiere Project centers on harnessing probability and utility to provide assistance to computer software users. We review work on Bayesian user models that can be employed to infer a user's needs by considering a user's background, actions, and queries. Several problems were tackled in Lumiere research, including (1) the construction of Bayesian models for reasoning about the time-varying goals of computer users from their observed actions and queries, (2) gaining access to a stream of events from software applications, (3) developing a language for transforming system events into observational variables represented in Bayesian user models, (4) developing persistent profiles to capture changes in a user's expertise, and (5) the development of an overall architecture for an intelligent user interface. Lumiere prototypes served as the basis for the Ofice Assistant in the Microsoft Office '97 suite of productivity applications.
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paper_title: User Intention Modeling in Web Applications Using Data Mining

paper_content:

The problem of inferring a user's intentions in Machine–Human Interaction has been the key research issue for providing personalized experiences and services. In this paper, we propose novel approaches on modeling and inferring user's actions in a computer. Two linguistic features – keyword and concept features – are extracted from the semantic context for intention modeling. Concept features are the conceptual generalization of keywords. Association rule mining is used to find the proper concept of corresponding keyword. A modified Naive Bayes classifier is used in our intention modeling. Experimental results have shown that our proposed approach achieved 84% average accuracy in predicting user's intention, which is close to the precision (92%) of human prediction.
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paper_title: Pre-sending Documents on the WWW: A Comparative Study

paper_content:

Users' waiting time for information on the WWW may be reduced by pre-sending documents they are likely to request, albeit at a possible expense of additional transmission costs. In this paper, we describe a prediction model which anticipates the documents a user is likely to request next, and present a decision-theoretic approach for pre-sending documents based on the predictions made by this model. We introduce two evaluation methods which measure the immediate and the eventual benefit of pre-sending a document. We use these evaluation methods to compare the performance of our decision-theoretic policy to that of a naive pre-sending policy, and to identify the domain parameter configurations for which each of these policies provides a clear overall benefit to the user.
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paper_title: Adaptive Web Navigation for Wireless Devices

paper_content:

Visitors who browse the web from wireless PDAs, cell phones, and pagers are frequently stymied by web interfaces optimized for desktop PCs. Simply replacing graphics with text and reformatting tables does not solve the problem, because deep link structures can still require minutes to traverse. ::: ::: In this paper we develop an algorithm, MINPATH, that automatically improves wireless web navigation by suggesting useful shortcut links in real time. MINPATH finds shortcuts by using a learned model of web visitor behavior to estimate the savings of shortcut links, and suggests only the few best links. We explore a variety of predictive models, including Naive Bayes mixture models and mixtures of Markov models, and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort.
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paper_title: Machine Learning for User Modeling

paper_content:

At first blush, user modeling appears to be a prime candidate for straightforward application of standard machine learning techniques. Observations of the user's behavior can provide training examples that a machine learning system can use to form a model designed to predict future actions. However, user modeling poses a number of challenges for machine learning that have hindered its application in user modeling, including: the need for large data sets; the need for labeled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.
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paper_title: Predictive Statistical Models for User Modeling

paper_content:

The limitations of traditional knowledge representation methods for modeling complex human behaviour led to the investigation of statistical models. Predictive statistical models enable the anticipation of certain aspects of human behaviour, such as goals, actions and preferences. In this paper, we motivate the development of these models in the context of the user modeling enterprise. We then review the two main approaches to predictive statistical modeling, content-based and collaborative, and discuss the main techniques used to develop predictive statistical models. We also consider the evaluation requirements of these models in the user modeling context, and propose topics for future research.
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paper_title: Link prediction and path analysis using Markov chains

paper_content:

Abstract The enormous growth in the number of documents in the World Wide Web increases the need for improved link navigation and path analysis models. Link prediction and path analysis are important problems with a wide range of applications ranging from personalization to Web server request prediction. The sheer size of the World Wide Web coupled with the variation in users' navigation patterns makes this a very difficult sequence modelling problem. In this paper, the notion of probabilistic link prediction and path analysis using Markov chains is proposed and evaluated. Markov chains allow the system to dynamically model the URL access patterns that are observed in navigation logs based on the previous state. Furthermore, the Markov chain model can also be used in a generative mode to automatically obtain tours. The Markov transition matrix can be analysed further using eigenvector decomposition to obtain `personalized hubs/authorities'. The utility of the Markov chain approach is demonstrated in many domains: HTTP request prediction, system-driven adaptive Web navigation, tour generation, and detection of `personalized hubs/authorities' from user navigation profiles. The generality and power of Markov chains is a first step towards the application of powerful probabilistic models to Web path analysis and link prediction.
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paper_title: Relational Markov models and their application to adaptive web navigation

paper_content:

Relational Markov models (RMMs) are a generalization of Markov models where states can be of different types, with each type described by a different set of variables. The domain of each variable can be hierarchically structured, and shrinkage is carried out over the cross product of these hierarchies. RMMs make effective learning possible in domains with very large and heterogeneous state spaces, given only sparse data. We apply them to modeling the behavior of web site users, improving prediction in our P ROTEUS architecture for personalizing web sites. We present experiments on an e-commerce and an academic web site showing that RMMs are substantially more accurate than alternative methods, and make good predictions even when applied to previously-unvisited parts of the site.
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paper_title: Assessing Temporally Variable User Properties With Dynamic Bayesian Networks

paper_content:

Bayesian networks have been successfully applied to the assessment of user properties which remain unchanged during a session. However, many properties of a person vary over time, thus raising new questions of network modeling. In this paper we characterize different types of dependencies that occur in networks that deal with the modeling of temporally variable user properties. We show how existing techniques of applying dynamic probabilistic networks can be adapted for the task of modeling the dependencies in dynamic Bayesian networks. We illustrate the proposed techniques using examples of emergency calls to the fire department of the city of Saarbrucken. The fire department officers are experienced in dealing with emergency calls from callers whose available working memory capacity is temporarily limited. We develop a model which reconstructs the officers’ assessments of a caller’s working memory capacity.
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paper_title: Prefetching Hyperlinks

paper_content:

This paper develops a new method for prefetching Web pages into the client cache. Clients send reference information to Web servers, which aggregate the reference information in near-real-time and then disperse the aggregated information to all clients, piggybacked on GET responses. The information indicates how often hyperlink URLs embedded in pages have been previously accessed relative to the embedding page. Based on knowledge about which hyperlinks are generally popular, clients initiate prefetching of the hyperlinks and their embedded images according to any algorithm they prefer. Both client and server may cap the prefetching mechanism's space overhead and waste of network resources due to speculation. The result of these differences is improved prefetching: lower client latency (by 52.3%) and less wasted network bandwidth (24.0%).
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Title: Survey of Data Mining Approaches to User Modeling for Adaptive Hypermedia

Section 1: INTRODUCTION
Description 1: In this section, introduce the concept of adaptive hypermedia (AH), personalization, user modeling (UM), and the role of data mining and machine learning techniques in the automatic creation of user models for AH services.

Section 2: UM
Description 2: Define a user model, its relevance for AH, and the elements it should capture about user behavior. Also, provide a basic overview of automatic user model generation.

Section 3: UM and AH
Description 3: Describe how AH systems use user models to personalize content and the basic architecture of an AH system, including the recommendation and classification tasks.

Section 4: Automatic Generation of User Models
Description 4: Outline the steps in the automatic generation of user models, including data collection, preprocessing, pattern discovery, and validation and interpretation.

Section 5: Data Mining and Its Relevance to UM
Description 5: Discuss how data mining techniques help in the phase of pattern discovery for user modeling and the importance of choosing suitable methods based on the data and tasks at hand.

Section 6: UNSUPERVISED APPROACHES TO UM
Description 6: Review the main unsupervised techniques like clustering (hierarchical, nonhierarchical, fuzzy) and association rules, highlighting their application, basic algorithms, and limitations for user modeling.

Section 7: SUPERVISED APPROACHES TO UM
Description 7: Explore supervised learning techniques such as decision trees/classification rules, k-NN, neural networks, and SVMs, explaining their application, basic algorithms, and limitations for user modeling.

Section 8: CRITERIA FOR THE SELECTION OF THE TECHNIQUES
Description 8: Provide guidelines to help decide which data mining/machine learning technique to use for developing AH applications based on data nature, task type, and readability requirements.

Section 9: CONCLUSION AND DISCUSSION
Description 9: Summarize the findings of the paper, discuss the lack of standardization in user model design, propose future research directions including hybrid systems, and discuss the potential improvements in user modeling for AH systems.