Collaborative filtering incorporating review text and co-clusters of hidden user communities and item groups
Refereed conference paper presented and published in conference proceedings

替代計量分析
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其它資訊
摘要Most collaborative filtering (CF) algorithms only make use of the rating scores given by users for items. However, it is often the case that each rating score is associated with a piece of review text. Such review texts, which are capable of providing us valuable information to reveal the reasons why users give a certain rating, have not been exploited and they are usually ignored by most CF algorithms. Moreover, the underlying relationship buried in users and items has not been fully exploited. Items we would recommend can often be characterized into hidden groups (e.g. comedy, horror movie and action movie), and users can also be organized as hidden communities. We propose a new generative model to predict user's ratings on previously unrated items by considering review texts as well as hidden user communities and item groups relationship. Regarding the rating scores, traditional algorithms would not perform well on uncovering the community and group information of each user and each item since the user-item rating matrix is dyadic involving the mutual interactions between users and items. Instead, co-clustering, which is capable of conducting simultaneous clustering of two variables, is able to take advantage of such user-item relationships to better predict the rating scores. Additionally, co-clustering would be more effective for modeling the generation of review texts since different user communities would discuss different topics and vary their own wordings or expression patterns when dealing with different item groups. Besides, by modeling as a mixed membership over community and group respectively, each user or item can belong to multiple communities or groups with varying degrees. We have conducted extensive experiments to predict the missing rating scores on 22 real word datasets. We also investigate the performance of discovering the topics in the review texts in order to reveal the topics usually discussed by each user community for each item group. The experimental results demonstrate the superior performance of our proposed model comparing with the state-of-the-art methods.
著者Xu Y., Lam W., Lin T.
會議名稱23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
會議開始日03.11.2014
會議完結日07.11.2014
會議地點Shanghai
會議國家/地區中國
詳細描述organized by ACM,
出版年份2014
月份1
日期1
頁次251 - 260
國際標準書號9781450325981
語言英式英語
關鍵詞Co-clustering, Collaborative filtering, Item group, Topic model, User community

上次更新時間 2021-19-01 於 00:45