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
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Content-Independent Task-Focused Recommendation
IEEE Internet Computing
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
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
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Expert Systems with Applications: An International Journal
Bayesian video search reranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Real time google and live image search re-ranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Error-based collaborative filtering algorithm for top-N recommendation
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Reranking collaborative filtering with multiple self-contained modalities
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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Collaborative recommendation (CR) approaches have proven effective for the Top-N recommendation task. We introduce a novel approach, Rerank-CR, that further improves the Top-N results of an arbitrary CR algorithm using a post-processing step involving Bayesian reranking. The defining characteristic of Rerank-CR is that reranking is self contained, meaning that it requires no external resources, but rather makes use of information derivable from the original user-item matrix. Rerank-CR achieves top performance when used for incorporating collection-level information reflecting global tendencies as constraints into conventional CR, which we refer to as 'connecting with the collective'. Because information about the preferences of the collective is derived directly from the dataset, Rerank-CR has no need of an explicit model of rating styles within a certain community. Further, it is possible to adapt the domain of application (e.g., change to a different cultural setting) without explicit intervention. We evaluate Rerank-CR with experiments that demonstrate the ability of the basic Rerank-CR concept to improve an initial Top-N recommendation list and also the additional improvement achieved by 'multimodal' Rerank-CR, which integrates the collective modality. Additional experiments confirm that the performance of Rerank-CR is significant across different datasets.