Review-based Multi-intention Contrastive Learning for Recommendation

Real recommendation systems contain various features, which are often high-dimensional, sparse, and difficult to learn effectively. In addition to numerical features, user reviews contain rich semantic information including user preferences, which are used as auxiliary features by researchers. The methods of supplementing data features based on reviews have certain effects. However, most of them simply concatenate review representations and other features together, without considering that the text representation contains a lot of noise information. In addition, the important intentions contained in user reviews are not modeled effectively. In order to solve the above problems, we propose a novel Review-based Multi-intention Contrastive Learning (RMCL) method. In detail, RMCL proposes an intention representation method based on mixed Gaussian distribution hypothesis. Further, RMCL adopts a multi-intention contrastive strategy, which establishes a fine-grained connection between user reviews and item reviews. Extensive experiments on five real-world datasets demonstrate significant improvements of our proposed RMCL model over the state-of-the-art methods.