Content-Based Recommendation Systems
Lecture notes in computer sciencePublished 14 May 2007
Michael J. Pazzani, Daniel Billsus
Citations2,444
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SJR score0.35
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TL;DR
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, which are used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale.
Abstract
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 re commend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.
Keywords
Computer Science
The Nature of Statistical Learning Theory
39,279 Citations1995Vladimir Vapnik
C4.5: Programs for Machine Learning
23,665 Citations1992J. R. Quinlan
A complete guide to the C4.5 system as implemented in C for the UNIX environment, which 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.
Machine LearningInduction of Decision Trees
14,815 Citations1986J. R. Quinlan
This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, which is described in detail.
Pattern classification and scene analysis
12,643 Citations1973Richard O. Duda, Peter E. Hart
Program electronic library and information systemsAn algorithm for suffix stripping
8,136 Citations1980Martin Porter
An algorithm for suffix stripping is described, which has been implemented as a short, fast program in BCPL, and performs slightly better than a much more elaborate system with which it has been compared.
Lecture notes in computer scienceText categorization with Support Vector Machines: Learning with many relevant features
7,925 Citations1998Thorsten Joachims
SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of di-erent learning tasks, eliminating the need for manual parameter tuning.
Programs for Machine Learning
5,794 Citations1994Steven L. Salzberg, Alberto M. Segre
In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.
ACM Transactions on Information SystemsEvaluating collaborative filtering recommender systems
5,762 Citations2004Jonathan L. Herlocker, Joseph A. Konstan +2 more
The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
A Comparative Study on Feature Selection in Text Categorization
4,766 Citations1997Yiming Yang, Jan Pedersen
DF thresholding, the simplest method with the lowest cost in computation, can be reliably used instead of IG or CHI when the computation of these measures are too expensive, and strong correlations between the DF, IG and CHI values of a term are found.
Elsevier eBooksFast Effective Rule Induction
3,767 Citations1995William W. Cohen
This paper evaluates the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems, and proposes a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5 and C 4.5rules with respect to error rates, but much more efficient on large samples.
Journal of the Royal Statistical Society Series A (General)Pattern Classification and Scene Analysis.
3,240 Citations1974Mairi Clarke, Richard O. Duda +1 more
A comparison of event models for naive bayes text classification
3,224 Citations1998Andrew McCallum, Kamal Nigam
It is found that the multi-variate Bernoulli performs well with small vocabulary sizes, but that the multinomial performs usually performs even better at larger vocabulary sizes--providing on average a 27% reduction in error over the multi -variateBernoulli model at any vocabulary size.
Machine LearningOn the Optimality of the Simple Bayesian Classifier under Zero-One Loss
3,072 Citations1997Pedro Domingos, Michael J. Pazzani
The Bayesian classifier is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption, and will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain.
Communications of the ACMFab
2,927 Citations1997Marko Balabanović, Yoav Shoham
It is explained how a hybrid system can incorporate the advantages of both methods while inheriting the disadvantages of neither, and how the particular design of the Fab architecture brings two additional benefits.
Lecture notes in computer scienceNaive (Bayes) at forty: The independence assumption in information retrieval
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The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval, and some of the variations used for text retrieval and classification are reviewed.
Lecture notes in computer scienceCollaborative Filtering Recommender Systems
2,027 Citations2007J. Ben Schafer, Dan Frankowski +2 more
This chapter introduces 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.
Information RetrievalAn Evaluation of Statistical Approaches to Text Categorization
1,946 Citations1999Yiming Yang
Analysis and empirical evidence suggest that the evaluation results on some versions of Reuters were significantly affected by the inclusion of a large portion of unlabelled documents, mading those results difficult to interpret and leading to considerable confusions in the literature.
Communications of the ACMInformation filtering and information retrieval
1,288 Citations1992Nicholas J. Belkin, W. Bruce Croft
Models of information retrieval and filtering, and lessons for filtering from retrieval research are presented; users see only the data that is extracted.
Machine LearningLearning and Revising User Profiles: The Identification of Interesting Web Sites
1,200 Citations1997Michael J. Pazzani, Daniel Billsus
The use of a naive Bayesian classifier is described, and it is demonstrated that it can incrementally learn profiles from user feedback on the interestingness of Web sites and may easily be extended to revise user provided profiles.
Lecture notes in computer scienceHybrid Web Recommender Systems
1,125 Citations2007Robin Burke
This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies and finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.
Learning Collaborative Information Filters
1,015 Citations1998Daniel Billsus, Michael J. Pazzani
This work proposes a representation for collaborative filtering tasks that allows the application of virtually any machine learning algorithm, and identifies the shortcomings of current collaborative filtering techniques and proposes the use of learning algorithms paired with feature extraction techniques that specifically address the limitations of previous approaches.
Induction of Decision Trees
999 Citations2003Quinlan
Recommendation as classification: using social and content-based information in recommendation
955 Citations1998Chumki Basu, Haym Hirsh +1 more
These sulfoxides and sulfones are useful as analgesics, non-addicting narcotic antagonists and anti-diarrheal agents and having agonist activity at opiate receptors are disclosed herein.
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951 Citations2018James Allan, Jaime Carbonell +3 more
Information and ComputationExponentiated Gradient versus Gradient Descent for Linear Predictors
880 Citations1997Jyrki Kivinen, Manfred K. Warmuth
The bounds suggest that the losses of the algorithms are in general incomparable, but EG(+/-) has a much smaller loss if only a few components of the input are relevant for the predictions, which is quite tight already on simple artificial data.
Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier.
683 Citations1996Pedro Domingos, Michael J. Pazzani
It is shown that the simple Bayesian classi er (SBC) does not in fact assume attribute independence, and can be optimal even when this assumption is violated by a wide margin, and the previously-assumed region of optimality is a second-order in nitesimal fraction of the actual one.
Web Watcher: A Tour Guide for the World Wide Web.
681 Citations1997Thorsten Joachims, Dayne Freitag +1 more
The learning algorithms used by WebWatcher, experimental results showing their e ectiveness, and lessons learned from this case study in Web tour guide agents are described.
Journal of the ACMAutomatic Indexing: An Experimental Inquiry
552 Citations1961M. E. Maron
The design, execution and evaluation of a modest experimental study aimed at testing empirically one statistical technique for automatic indexed documents according to their subject content are described.
Training algorithms for linear text classifiers
516 Citations1996David Lewis, Robert E. Schapire +2 more
This work proposes that two machine learning algorithms, the Widrow-Hoff and EG algorithms, be used in training linear text classifiers for IR tasks, and theoretical analysis provides performance guarantees and guidance on parameter settings for these algorithms.
Communications of the ACMPersonalized information delivery
504 Citations1992Peter W. Foltz, Susan Dumais
T h e score for e ach new T M was t he cos ine b e t w e e n t he T M vec tor a n d the nearest i n t e r e s t vector .
Improving Text Classification by Shrinkage in a Hierarchy of Classes
478 Citations2022Andrew McCallum, Roni Rosenfeld +2 more
This paper shows that the accuracy of a naive Bayes text classi(cid:12)er can be significantly improved by taking advantage of a hierarchy of classes, and adopts an established statistical technique called shrinkage that smoothes parameter estimates of a data-sparse child with its parent in order to obtain more robust parameter estimates.
Learning Rules that Classify E-Mail
400 Citations1996William W. Cohen
Two methods for learning text classifiers are compared on classification problems that might arise in filtering and filing personM e-mail messages: a "traxiitionM IR" method based on TF-IDF weighting, and a new method for learning sets of "keyword-spotting rules" based on the RIPPER rule learning algorithm.
Learning to classify text from labeled and unlabeled documents
330 Citations1998Kamal Nigam, Andrew McCallum +2 more
It is shown that the accuracy of text classifiers trained with a small number of labeled documents can be improved by augmenting this small training set with a large pool of unlabeled documents, and an algorithm is introduced based on the combination of Expectation-Maximization with a naive Bayes classifier.
TiVo
194 Citations2004Kamal Ali, Wijnand van Stam
The TiVo television show collaborative recommendation system is described, which has been fielded in over one million TiVo clients for four years, and internal studies have shown its recommendations to be useful even for multiple user households.
International Journal of Electronic CommerceApplication of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts
191 Citations2001Jong Woo Kim, Byung Hun Lee +3 more
This paper proposes a marketing rule-extraction technique for personalized recommendation on Internet storefronts using machine learning techniques, and especially decisiontree induction techniques.
Computing & Control Engineering JournalThe emergence of ZigBee in building automation and industrial controls
186 Citations2005Donald F. Egan
The ZigBee Alliance has identified six application spaces for ZigBee: consumer electronics, PC and peripherals, residential/light commercial control, industrial control, building automation and personal healthcare, which deals mainly with industrial control and building automation.
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133 Citations2006Michael Mandel, Graham E. Poliner +1 more
In comparing a number of representations for songs, the statistics of mel-frequency cepstral coefficients to perform best in precision-at-20 comparisons and it is shown that by choosing training examples intelligently, active learning requires half as many labeled examples to achieve the same accuracy as a standard scheme.
A learning agent for wireless news access
117 Citations2000Daniel Billsus, Michael J. Pazzani +1 more
A user interface for wireless information devices designed to facilitate learning about users' individual interests in daily news stories is described, which can adapt to users'individual interests, reduce the amount of information that needs to be transmitted, and help users access relevant information with minimal effort.
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115 Citations1998Kamal Nigam, Andrew McCallum +2 more
It is shown that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents, and the use of unlabelled data reduces classification error by up to 30%.
Communications of the ACMArchitecting personalized delivery of multimedia information
109 Citations1992Shoshana Loeb
In this article, the information-filtering process is viewed as dependent on the application domain in which it operates and on the context inWhich it is used.
Joins that generalize: text classification using WHIRL
105 Citations1998William W. Cohen, Haym Hirsh
It is shown that although WHIRL is designed for more general similarity-based reasoning tasks, it is competitive with mature inductive classification systems on these classification tasks and generally achieves lower generalization error than C4.5, RIPPER, and several nearest-neighbor methods.