Word-Driven and Context-Aware Review Modeling for Recommendation
Qianqian Wang, Si Li, Guang Chen · 2018
Recently, convolutional neural networks(CNNs) has been demonstrated to effectively model reviews in recommender systems, due to the learning of contextual features such as surrounding words and word order for reviews. However, CNNs with max-pooling fails to capture the count information of contextual features, since the feature map generated by a convolution filter can only get a max feature value with max-pooling. If the max feature value appears more than once in the feature map, CNNs will lose the count information of the contextual feature. The count information is quite critical for modeling reviews, for example, ten "a good quality" is more credible than one "a good quality" for representing item properties, and five "the scenery is" shows that a user may pay more attention to the scenery than one "the scenery is" does. Our model, called WCN-MF, extends CNNs by introducing a new module named Deep Latent Dirichlet Allocation (DLDA) to capture the count information of contextual features. DLDA is inspired by the fact that contextual features consist of words, hence, we can first capture the count information of words, and second generate contextual features with words. By combining DLDA with CNNs, we can get a word-driven and context-aware review representation. Further, we incorporate the review representation with Matrix Factorization for recommendation. Our evaluations on three real- world datasets reveal that our model can significantly outperform the state-of-the-art recommendation models.