Results 1 - 10 of 169

Latent dirichlet allocation

by David M. Blei, Andrew Y. Ng, Michael I. Jordan, John Lafferty - Journal of Machine Learning Research , 2003
"... We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, ..."
Abstract - Cited by 4365 (92 self) - Add to MetaCart
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model. 1.
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Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions

by Gediminas Adomavicius, Alexander Tuzhilin - IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2005
"... 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 vario ..."
Abstract - Cited by 1490 (23 self) - Add to MetaCart
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 multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
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Methods and Metrics for Cold-Start Recommendations

by Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, David M. Pennock - PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL , 2002
"... We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a nave Bayes classifier on the cold-start problem, where we wish to recommend items that no one in the community has yet rated. We ..."
Abstract - Cited by 330 (7 self) - Add to MetaCart
We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a nave Bayes classifier on the cold-start problem, where we wish to recommend items that no one in the community has yet rated. We systematically explore three testing methodologies using a publicly available data set, and explain how these methods apply to specific real-world applications. We advocate heuristic recommenders when benchmarking to give competent baseline performance. We introduce a new performance metric, the CROC curve, and demonstrate empirically that the various components of our testing strategy combine to obtain deeper understanding of the performance characteristics of recommender systems. Though the emphasis of our testing is on cold-start recommending, our methods for recommending and evaluation are general.
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Content-Boosted Collaborative Filtering for Improved Recommendations

by Prem Melville, Raymond J. Mooney, Ramadass Nagarajan - in Eighteenth National Conference on Artificial Intelligence , 2002
"... Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid rec ..."
Abstract - Cited by 326 (3 self) - Add to MetaCart
Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings.

A Survey of Collaborative Filtering Techniques

by Xiaoyuan Su, Taghi M. Khoshgoftaar , 2009
"... As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenge ..."
Abstract - Cited by 216 (0 self) - Add to MetaCart
As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

Collaborative filtering with privacy via factor analysis

by John Canny - In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval , 2002
"... Collaborative filtering is valuable in e-commerce, and for direct recommendations for music, movies, news etc. But today’s systems use centralized databases and have several disadvantages, including privacy risks. As we move toward ubiquitous computing, there is a great potential for individuals to ..."
Abstract - Cited by 210 (9 self) - Add to MetaCart
Collaborative filtering is valuable in e-commerce, and for direct recommendations for music, movies, news etc. But today’s systems use centralized databases and have several disadvantages, including privacy risks. As we move toward ubiquitous computing, there is a great potential for individuals to share all kinds of information about places and things to do, see and buy, but the privacy risks are severe. In this paper we introduce a peer-to-peer protocol for collaborative filtering which protects the privacy of individual data. A second contribution of this paper is a new collaborative filtering algorithm based on factor analysis which appears to be the most accurate method for CF to date. The new algorithm has other advantages in speed and storage over previous algorithms. It is based on a careful probabilistic model of user choice, and on a probabilistically sound approach to dealing with missing data. Our experiments on several test datasets show that the algorithm is more accurate than previously reported methods, and the improvements increase with the sparseness of the dataset. Finally, factor analysis with privacy is applicable to other kinds of statistical analyses of survey or questionaire data scientists (e.g. web surveys or questionaires).

Scalable collaborative filtering using cluster-based smoothing

by Gui-rong Xue, Chenxi Lin, Qiang Yang, Wensi Xi, Hua-jun Zeng, Yong Yu, Zheng Chen - In Proc. of SIGIR , 2005
"... Memory-based approaches for collaborative filtering identify the similarity between two users by comparing their ratings on a set of items. In the past, the memory-based approaches have been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. Alternatively, th ..."
Abstract - Cited by 114 (7 self) - Add to MetaCart
Memory-based approaches for collaborative filtering identify the similarity between two users by comparing their ratings on a set of items. In the past, the memory-based approaches have been shown to suffer from two fundamental problems: data sparsity and difficulty in scalability. Alternatively, the model-based approaches have been proposed to alleviate these problems, but these approaches tends to limit the range of users. In this paper, we present a novel approach that combines the advantages of these two kinds of approaches by introducing a smoothing-based method. In our approach, clusters generated from the training data provide the basis for data smoothing and neighborhood selection. As a result, we provide higher accuracy as well as increased efficiency in recommendations. Empirical studies on two datasets (EachMovie and MovieLens) show that our new proposed approach consistently outperforms other state-of-the-art collaborative filtering algorithms.
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Collaborative Filtering Recommender Systems

by J. Ben Schafer, Dan Frankowski, Jon Herlocker, Shilad Sen , 2007
"... 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 ..."
Abstract - Cited by 113 (2 self) - Add to MetaCart
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.
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Large-Scale Parallel Collaborative Filtering for the Netflix Prize

by Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, Rong Pan - Proc. 4th Int’l Conf. Algorithmic Aspects in Information and Management, LNCS 5034 , 2008
"... Abstract. Many recommendation systems suggest items to users by utilizing the techniques of collaborative filtering (CF) based on historical records of items that the users have viewed, purchased, or rated. Two major problems that most CF approaches have to resolve are scalability and sparseness of ..."
Abstract - Cited by 96 (1 self) - Add to MetaCart
Abstract. Many recommendation systems suggest items to users by utilizing the techniques of collaborative filtering (CF) based on historical records of items that the users have viewed, purchased, or rated. Two major problems that most CF approaches have to resolve are scalability and sparseness of the user profiles. In this paper, we describe Alternating-Least-Squares with Weighted-λ-Regularization (ALS-WR), a parallel algorithm that we designed for the Netflix Prize, a large-scale collaborative filtering challenge. We use parallel Matlab on a Linux cluster as the experimental platform. We show empirically that the performance of ALS-WR monotonically increases with both the number of features and the number of ALS iterations. Our ALS-WR applied to the Netflix dataset with 1000 hidden features obtained a RMSE score of 0.8985, which is one of the best results based on a pure method. Combined with the parallel version of other known methods, we achieved a performance improvement of 5.91 % over Netflix’s own CineMatch recommendation system. Our method is simple and scales well to very large datasets. 1
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Tag-aware recommender systems by fusion of collaborative filtering algorithms

by Karen H. L. Tso-sutter, Ro Balby Marinho, Lars Schmidt-thieme - In Proceedings of the 2nd ACM Symposium on Applied Computing , 1995
"... Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content informa ..."
Abstract - Cited by 84 (3 self) - Add to MetaCart
Recommender Systems (RS) aim at predicting items or ratings of items that the user are interested in. Collaborative Filtering (CF) algorithms such as user- and item-based methods are the dominant techniques applied in RS algorithms. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing popularity of the collaborative tagging systems, tags could be interesting and useful information to enhance RS algorithms. Unlike attributes which are “global ” descriptions of items, tags are “local ” descriptions of items given by the users. To the best of our knowledge, there hasn’t been any prior study on tagaware RS. In this paper, we propose a generic method that allows tags to be incorporated to standard CF algorithms, by reducing the three-dimensional correlations to three twodimensional correlations and then applying a fusion method to re-associate these correlations. Additionally, we investigate the effect of incorporating tags information to different CF algorithms. Empirical evaluations on three CF algorithms with real-life data set demonstrate that incorporating tags to our proposed approach provides promising and significant results.
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