Review rating prediction is commonly approached from the perspective of either Collaborative Filtering (CF) or Sentiment Classification (SC). CF-based approach usually resorts to matrix factorization based on user–item interaction, and does not fully utilize the valuable review text features. In contrast, SC-based approach is focused on mining review content, but can just incorporate some user- and product-level features, and fails to capture sufficient interactions between them represented typically in a sparse matrix as CF can do. In this paper, we propose a novel, extensible review rating prediction model called InterSentiment by bridging the user-product interaction model and the sentiment model based on deep learning. InterSentiment is a specific instance of our proposed Deep Learning based Collaborative Filtering framework. The proposed model aims to learn the high-level representations combining user-product interaction and review sentiment, and jointly project them into the rating scores. Results of experiments conducted on IMDB and two Yelp datasets demonstrate clear advantage of our proposed approach over strong baseline methods.

评论评级预测通常是从协作过滤（CF）或情感分类（SC）的角度进行的。基于CF的方法通常诉诸于基于用户与项目交互的矩阵分解，而没有充分利用有价值的评论文本功能。相比之下，基于SC的方法侧重于挖掘评论内容，但是仅可以合并一些用户和产品级别的功能，并且无法像CF一样捕获以稀疏矩阵表示的它们之间的足够交互。在本文中，我们通过将基于深度学习的用户-产品交互模型和情感模型相结合，提出了一种新颖的，可扩展的评论评分预测模型InterSentiment。InterSentiment是我们提议的基于深度学习的协作过滤框架的特定实例。该模型旨在学习结合用户-产品交互和评论情绪的高级表示，并将它们共同投影到评分中。在IMDB和两个Yelp数据集上进行的实验结果表明，与强基准方法相比，我们提出的方法具有明显优势。