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Web"],"published-print":{"date-parts":[[2014,3]]},"abstract":"<jats:p>User-based collaborative filtering systems suggest interesting items to a user relying on similar-minded people called neighbors. The selection and weighting of these neighbors characterize the different recommendation approaches. While standard strategies perform a neighbor selection based on user similarities, trust-aware recommendation algorithms rely on other aspects indicative of user trust and reliability. In this article we restate the trust-aware recommendation problem, generalizing it in terms of performance prediction techniques, whose goal is to predict the performance of an information retrieval system in response to a particular query. We investigate how to adopt the preceding generalization to define a unified framework where we conduct an objective analysis of the effectiveness (predictive power) of neighbor scoring functions. The proposed framework enables discriminating whether recommendation performance improvements are caused by the used neighbor scoring functions or by the ways these functions are used in the recommendation computation. We evaluated our approach with several state-of-the-art and novel neighbor scoring functions on three publicly available datasets. By empirically comparing four neighbor quality metrics and thirteen performance predictors, we found strong predictive power for some of the predictors with respect to certain metrics. This result was then validated by checking the final performance of recommendation strategies where predictors are used for selecting and\/or weighting user neighbors. As a result, we have found that, by measuring the predictive power of neighbor performance predictors, we are able to anticipate which predictors are going to perform better in neighbor-scoring-powered versions of a user-based collaborative filtering algorithm.<\/jats:p>","DOI":"10.1145\/2579993","type":"journal-article","created":{"date-parts":[[2014,4,1]],"date-time":"2014-04-01T09:06:54Z","timestamp":1396343214000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":44,"title":["Neighbor Selection and Weighting in User-Based Collaborative Filtering"],"prefix":"10.1145","volume":"8","author":[{"given":"Alejandro","family":"Bellog\u00edn","sequence":"first","affiliation":[{"name":"Universidad Aut\u00f3noma de Madrid"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pablo","family":"Castells","sequence":"additional","affiliation":[{"name":"Universidad Aut\u00f3noma de Madrid"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iv\u00e1n","family":"Cantador","sequence":"additional","affiliation":[{"name":"Universidad Aut\u00f3noma de Madrid"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2014,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-12275-0_34"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Bellog\u00edn A. Castells P. and \n      Cantador I\n  . \n  2011\n  . Predicting the performance of recommender systems: An information theoretic approach. In Proceedings of the 3rd International Conference on the Theory of Information Retrieval (ICTIR\u201911). G. Amati and F. Crestani Eds. Lecture Notes in Computer Science vol. \n  6931 Springer 27--39.   Bellog\u00edn A. Castells P. and Cantador I. 2011. Predicting the performance of recommender systems: An information theoretic approach. In Proceedings of the 3rd International Conference on the Theory of Information Retrieval (ICTIR\u201911) . G. Amati and F. Crestani Eds. Lecture Notes in Computer Science vol. 6931 Springer 27--39.","DOI":"10.1007\/978-3-642-23318-0_5"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 10th Conference on Open Research Areas in Information Retrieval (OAIR\u201913)","author":"Bellog\u00edn A.","unstructured":"Bellog\u00edn , A. , Castells , P. , and Cantador , I . 2013. Improving memory-based collaborative filtering by neighbour selection based on user preference overlap . In Proceedings of the 10th Conference on Open Research Areas in Information Retrieval (OAIR\u201913) . Le Centre de Hautes Etudes Internationales D\u2019Informatique Documentaire, 145--148. Bellog\u00edn, A., Castells, P., and Cantador, I. 2013. Improving memory-based collaborative filtering by neighbour selection based on user preference overlap. In Proceedings of the 10th Conference on Open Research Areas in Information Retrieval (OAIR\u201913). Le Centre de Hautes Etudes Internationales D\u2019Informatique Documentaire, 145--148."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1921591.1921593"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Carmel D. and Yom-Tov E. 2010. Estimating the query difficulty for information retrieval. In Synthesis Lectures of Information Concepts Retrieval and Services Morgan and Claypool.   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