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Internet Technol."],"published-print":{"date-parts":[[2017,8,31]]},"abstract":"<jats:p>The data sparsity problem has attracted significant attention in collaborative filtering-based recommender systems. To alleviate data sparsity, several previous efforts employed hybrid approaches that incorporate auxiliary data sources into recommendation techniques, like content, context, or social relationships. However, due to privacy and security concerns, it is generally difficult to collect such auxiliary information. In this article, we focus on the pure collaborative filtering methods without relying on any auxiliary data source. We propose an improved memory-based collaborative filtering approach enhanced by a novel similarity reinforcement mechanism. It can discover potential similarity relationships between users or items by making better use of known but limited user-item interactions, thus to extract plentiful historical rating information from similar neighbors to make more reliable and accurate rating predictions. This approach integrates user similarity reinforcement and item similarity reinforcement into a comprehensive framework and lets them enhance each other. Comprehensive experiments conducted on several public datasets demonstrate that, in the face of data sparsity, our approach achieves a significant improvement in prediction accuracy when compared with the state-of-the-art memory-based and model-based collaborative filtering algorithms.<\/jats:p>","DOI":"10.1145\/3062179","type":"journal-article","created":{"date-parts":[[2017,6,28]],"date-time":"2017-06-28T12:27:07Z","timestamp":1498652827000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Mitigating Data Sparsity Using Similarity Reinforcement-Enhanced Collaborative Filtering"],"prefix":"10.1145","volume":"17","author":[{"given":"Yan","family":"Hu","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences and Wayne State University, Beijing, China"}]},{"given":"Weisong","family":"Shi","sequence":"additional","affiliation":[{"name":"Wayne State University, Detroit, MI"}]},{"given":"Hong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Xiaohui","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Software, Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2017,6,27]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.1016\/j.ins.2007.07.024"},{"volume-title":"Proceedings of the 5th International Conference on Educational Data Mining. 95--102","author":"Bergner Yoav","unstructured":"Yoav Bergner , Stefan Droschler , Gerd Kortemeyer , Saif Rayyan , Daniel Seaton , and David E. 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