Response aware model-based collaborative filtering
Refereed conference paper presented and published in conference proceedings


全文

其它資訊
摘要Previous work on recommender systems mainly focus on fitting the ratings provided by users. However, the response patterns, i.e., some items are rated while others not, are generally ignored. We argue that failing to observe such response patterns can lead to biased parameter estimation and sub-optimal model performance. Although several pieces of work have tried to model users' response patterns, they miss the effectiveness and interpretability of the successful matrix factorization collaborative filtering approaches. To bridge the gap, in this paper, we unify explicit response models and PMF to establish the Response Aware Probabilistic Matrix Factorization (RAPMF) framework. We show that RAPMF subsumes PMF as a special case. Empirically we demonstrate the merits of RAPMF from various aspects.
著者Ling G., Yang H., Lyu M.R., King I.
會議名稱28th Conference on Uncertainty in Artificial Intelligence, UAI 2012
會議開始日15.08.2012
會議完結日17.08.2012
會議地點Catalina Island, CA
會議國家/地區美國
出版年份2012
月份12
日期1
頁次501 - 510
國際標準書號9780974903989
語言英式英語

上次更新時間 2020-02-09 於 00:45