Using statistical testing in the evaluation of retrieval experiments
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A re-unification of two competing models for document retrieval
Journal of the American Society for Information Science - Special topic issue: youth issues in information science
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
On Relevance, Probabilistic Indexing and Information Retrieval
Journal of the ACM (JACM)
Road sign classification using Laplace kernel classifier
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Understanding and improving automated collaborative filtering systems
Understanding and improving automated collaborative filtering systems
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Learning User Similarity and Rating Style for Collaborative Recommendation
Information Retrieval
A new unified probabilistic model
Journal of the American Society for Information Science and Technology
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
On Event Spaces and Probabilistic Models in Information Retrieval
Information Retrieval
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A generative theory of relevance
A generative theory of relevance
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A Hybrid User and Item-Based Collaborative Filtering with Smoothing on Sparse Data
ICAT '06 Proceedings of the 16th International Conference on Artificial Reality and Telexistence--Workshops
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
IEEE Transactions on Computers
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A user-item relevance model for log-based collaborative filtering
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Improving kernel Fisher discriminant analysis for face recognition
IEEE Transactions on Circuits and Systems for Video Technology
Probabilistic relevance ranking for collaborative filtering
Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Measuring predictive capability in collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Language Models of Collaborative Filtering
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
A hybrid collaborative filtering recommendation mechanism for P2P networks
Future Generation Computer Systems
A multimedia recommender integrating object features and user behavior
Multimedia Tools and Applications
Aggregating preference graphs for collaborative rating prediction
Proceedings of the fourth ACM conference on Recommender systems
Proceedings of the 1st ACM international workshop on Connected multimedia
Overlay management for fully distributed user-based collaborative filtering
EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
Collaborative error-reflected models for cold-start recommender systems
Decision Support Systems
Role of emotional features in collaborative recommendation
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Performance prediction in recommender systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
Predicting the performance of recommender systems: an information theoretic approach
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
A new criteria for selecting neighborhood in memory-based recommender systems
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Improving the performance of recommender system by exploiting the categories of products
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Using past-prediction accuracy in recommender systems
Information Sciences: an International Journal
Cluster searching strategies for collaborative recommendation systems
Information Processing and Management: an International Journal
Knowledge-Based Systems
Relevance-based language modelling for recommender systems
Information Processing and Management: an International Journal
Understanding Similarity Metrics in Neighbour-based Recommender Systems
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Probabilistic collaborative filtering with negative cross entropy
Proceedings of the 7th ACM conference on Recommender systems
Integrating collaborative filtering and matching-based search for product recommendations
Journal of Theoretical and Applied Electronic Commerce Research
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Collaborative filtering aims at predicting a user's interest for a given item based on a collection of user profiles. This article views collaborative filtering as a problem highly related to information retrieval, drawing an analogy between the concepts of users and items in recommender systems and queries and documents in text retrieval. We present a probabilistic user-to-item relevance framework that introduces the concept of relevance into the related problem of collaborative filtering. Three different models are derived, namely, a user-based, an item-based, and a unified relevance model, and we estimate their rating predictions from three sources: the user's own ratings for different items, other users' ratings for the same item, and ratings from different but similar users for other but similar items. To reduce the data sparsity encountered when estimating the probability density function of the relevance variable, we apply the nonparametric (data-driven) density estimation technique known as the Parzen-window method (or kernel-based density estimation). Using a Gaussian window function, the similarity between users and/or items would, however, be based on Euclidean distance. Because the collaborative filtering literature has reported improved prediction accuracy when using cosine similarity, we generalize the Parzen-window method by introducing a projection kernel. Existing user-based and item-based approaches correspond to two simplified instantiations of our framework. User-based and item-based collaborative filterings represent only a partial view of the prediction problem, where the unified relevance model brings these partial views together under the same umbrella. Experimental results complement the theoretical insights with improved recommendation accuracy. The unified model is more robust to data sparsity because the different types of ratings are used in concert.