Neural gaussian mixture model for review-based rating prediction
Dong Liang Deng, Liping Jing, Jian Ming Yu, Shaolong Sun, Haofei Zhou · 2018
Review has been proven to be an important information in recommendation. Different from the overall user-item rating matrix, it can provide textual information that exhibits why a user likes an item or not. Recently, more and more researchers have paid attention on review-based rating prediction. There are two challenging issues: how to extract representative features to characterize users / items from reviews and how to leverage them for recommendation system. In this paper, we propose a Neural Gaussian Mixture Model (NGMM) for review-based rating prediction task. Among it, the review textual information is used to construct two parallel neural networks for users and items respectively, so that the users' preferences and items' properties can be sufficiently extracted and represented as two latent vectors. A shared layer is introduced on the top to couple these two networks together and model user-item rating based on the features learned from reviews. Specifically, each rating is modeled via a Gaussian mixture model, where each Gaussian component has zero variance, the mean described by the corresponding component in user's latent vector and the weight indicated by the corresponding component in item's latent vector. Extensive experiments are conducted on five real-world Amazon review datasets. The experimental results have demonstrated that our proposed NGMM model achieves the state-of-the-art performance in review-based rating prediction task.