Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
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
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 6th international conference on Intelligent user interfaces
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
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
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
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A study of mixture models for collaborative filtering
Information Retrieval
Unified relevance models for rating prediction in collaborative filtering
ACM Transactions on Information Systems (TOIS)
Introduction to Information Retrieval
Introduction to Information Retrieval
Probabilistic relevance ranking for collaborative filtering
Information Retrieval
Risky business: modeling and exploiting uncertainty in information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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
Relevance-based language modelling for recommender systems
Information Processing and Management: an International Journal
Bridging memory-based collaborative filtering and text retrieval
Information Retrieval
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Collaborative filtering is a major technique to make personalized recommendations about information items (movies, books, webpages etc) to individual users. In the literature, a common research objective is to predict unknown ratings of items for a user, on the condition that the user has explicitly rated a certain amount of items. Nevertheless, in many practical situations, we may only have implicit evidence of user preferences, such as "playback times of a music file" or "visiting frequency of a web-site". Most importantly, a more practical view of the recommendation task is to directly generate a top-N ranked list of items that the user is most likely to like. In this paper, we take these two concerns into account. Item ranking in recommender systems is considered as a task highly related to document ranking in text retrieval. Firstly, two practical item scoring functions are derived by adopting the generative language modelling approach of text retrieval. Secondly, to address the uncertainty associated with the score estimation, we introduce a risk-averse model that penalizes the less reliable scores. Our experiments on real data sets demonstrate that significant performance gains have been achieved.