Facebook Interactions Utilization for Addressing Recommender Systems Cold Start Problem across System Domain,ERICDATA高等教育知識庫
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篇名
Facebook Interactions Utilization for Addressing Recommender Systems Cold Start Problem across System Domain
並列篇名
Facebook Interactions Utilization for Addressing Recommender Systems Cold Start Problem across System Domain
作者 Muhammad Murad KhanRoliana IbrahimMuhammad YounasImran GhaniSeung Ryul Jeong
英文摘要
Recommender systems face cold start problem at the time of inception which researchers have addressed by transferring rating knowledge from auxiliary to target domain, hence laying the foundation of cross domain recommender systems (CDRS). Recently social media has been utilized as a potential auxiliary domain for recommender systems because they provide interactions such as likes, read, download, play, click etc., for related items. Among existing social media, Facebook provides rich social interactions, broadly grouped as private and public social interactions. Although research exists on private Facebook social interactions, Facebook public social interactions were not explored before. In this paper, we propose Facebook Direct Social Recommendation (Facebook DSR) approach which transforms Facebook’s public interactions related to a social page into rating matrix and generate recommendations for provided list of items. Relative ratings obtained from proposed algorithm and random approach were compared with Movielens ratings. It was observed that proposed approach outperformed random approach threefold with respect to mean absolute error (MAE) evaluation and 19.4 % with respect to precision evaluation for new user cold start scenario. Finally, this study highlights future directions.
起訖頁 859-868
關鍵詞 Cross domain recommender systemsFacebookSocial interactionsSystem domain transfer learningCold start problem
刊名 網際網路技術學刊  
期數 201805 (19:3期)
出版單位 台灣學術網路管理委員會
DOI 10.3966/160792642018051903021  複製DOI
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