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Methods and Metrics for Cold-Start Recommendations
- 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
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Cited by 319 (7 self)
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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.
Editors ’ address:
"... Copyright c © 2014 for the individual papers by the papers ’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors. ii Program Committee ..."
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Copyright c © 2014 for the individual papers by the papers ’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors. ii Program Committee
Collaborative Filtering based on Dynamic Community Detection
"... Abstract. With the increase of time-stamped data, the task of recom-mender systems becomes not only to fulfill users interests but also to model the dynamic behavior of their tastes. This paper proposes a novel architecture, called Dynamic Community-based Collaborative filtering (D2CF), that combine ..."
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Abstract. With the increase of time-stamped data, the task of recom-mender systems becomes not only to fulfill users interests but also to model the dynamic behavior of their tastes. This paper proposes a novel architecture, called Dynamic Community-based Collaborative filtering (D2CF), that combines both recommendation and dynamic community detection techniques in order to exploit the temporal aspect of the commu-nity structure in real-world networks and to enhance the existing community-based recommendation. The eciency of the proposed D2CF is dealt with a comparative study with a recommendation system based on static com-munity detection and item-based collaborative filtering. Experimental re-sults show a considerable improvement of D2CF recommendation accu-racy, whilst it addresses both of scalability and sparsity problems.
