诺库维普

文献题目			去谷歌学术搜索
Convolutional Matrix Factorization for Document Context-Aware Recommendation
文献作者			Donghyun Kim 1 , Chanyoung Park 1 , Jinoh Oh 1 , Sungyoung Lee 2 , Hwanjo Yu ∗1
文献发表年限			2016
文献关键字
Collaborative Filtering; Document Modeling; Contexual Information; Deep learning; CNN
摘要描述
Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to improve rating prediction accuracy. In particular, when rating data is sparse, document modeling-based approaches have improved the accuracy by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they have difficulties in effectively utilizing contextual information of the documents, which leads to shallow understanding of the documents. This paper proposes a novel context-aware recommendation model, convolutional matrix fac- torization (ConvMF) that integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF). Consequently, ConvMF captures contextual information of documents and further enhances the rating prediction accuracy. Our extensive evaluations on three real-world datasets show that ConvMF significantly out- performs the state-of-the-art recommendation models even when the rating data is extremely sparse. We also demonstrate that ConvMF successfully captures subtle contextual difference of a word in a document. Our implementation and datasets are available at http://dm.postech.ac.kr/ConvMF.