With the rapid development of e-commerce, recommendation system has become one of the main tools that assists users in decision-making, enhances user’s experience, and creates economic value. Since it is difficult to explain the implicit features generated by matrix factorization, explainable recommendation system has attracted more and more attention recently. In this paper, we propose an explainable fast recommendation model by combining fine-grained sentiment in review data (FSER, (Fast) Fine-grained Sentiment for Explainable Recommendation). We innovatively construct user-rating matrix, user-aspect sentiment matrix, and item aspect-descriptive word frequency matrix from the review-based data. And the three matrices are reconstructed by matrix factorization method. The reconstructed results of user-aspect sentiment matrix and item aspect-descriptive word frequency matrix can provide explanation for the final recommendation results. Experiments in the Yelp and Public Comment datasets demonstrate that, compared with several classical models, the proposed FSER model is in the optimal recommendation accuracy range and has lower sparseness and higher training efficiency than tensor models or neural network models; furthermore, it can generate explanatory texts and diagrams that have high interpretation quality.

The aim of explainable recommendation is not only to provide recommended items to users, but also to make users aware of why these items are recommended. Traditional recommendation methods infer user preferences for items using user–item rating information. However, the expressive power of latent representations of users and items is relatively limited due to the sparseness of the user–item rating matrix. Heterogeneous information networks (HIN) provide contextual information for improving recommendation performance and interpreting the interactions between users and items. However, due to the heterogeneity and complexity of context information in HIN, it is still a challenge to integrate this contextual information into explainable recommendation systems effectively. In this paper, we propose a novel framework—the dual-attention networks for explainable recommendation (DANER) in HINs. We first used multiple meta-paths to capture high-order semantic relations between users and items in HIN for generating similarity matrices, and then utilized matrix decomposition on similarity matrices to obtain low-dimensional sparse representations of users and items. Secondly, we introduced two-level attention networks, namely a local attention network and a global attention network, to integrate the representations of users and items from different meta-paths for obtaining high-quality representations. Finally, we use a standard multi-layer perceptron to model the interactions between users and items, which predict users’ ratings of items. Furthermore, the dual-attention mechanism also contributes to identifying critical meta-paths to generate relevant explanations for users. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of DANER on recommendation performance as compared with the state-of-the-art methods. A case study illustrates the interpretability of DANER.

Recently, with the triumph of deep learning, attention mechanism, and graph convolutional networks in their respective fields, using new representation learning techniques or introducing auxiliary information to improve the representation ability of embedding has become the core content of the recommendation algorithm research. Generally, most existing GNN-based recommendation methods recursively propagate embedding information on the graph structure and capture collaborative signals by exploring the high-level connectivity between users and items. Despite the great success, those methods do not consider the influence of temporal context on user preferences embedding information propagation, nor do they distinguish the contribution of different neighbor node information to the target node. In order to address the two problems, we propose a graph collaborative filtering model TAGCF combing time factors and attention based on the existing method. The model uses the time factor to integrate temporal information into the process of embedding information propagation and uses the attention mechanism to distinguish the influence of embedding information from different neighbors. The effectiveness of TAGCF, time information, and attention mechanism are verified through comparative experiments with multiple baseline methods on the two recommendation system datasets, MovieLens and Amazon-books.