Explainable Discrete Collaborative Filtering

Using hashing to learn the binary codes of users and items significantly improves the efficiency and reduces the space consumption of the recommender system. However, existing hashing-based recommender systems remain black boxes without any explainable outputs that illustrate why the system recommends the items. In this paper, we present a new end-to-end discrete recommendation framework based on the multi-task learning to simultaneously perform explainable and efficient recommendation. Toward this goal, an Explainable Discrete Collaborative Filtering (EDCF) method is proposed to preserve the user-item interaction features and semantic text features into binary hash codes by adaptively exploiting the correlations between the preference prediction task and the explanation generation task. At the online recommendation stage, EDCF makes efficient top-K recommendation by calculating the Hamming distances between the feature hash codes, and simultaneously generates natural language explanations for recommendation results through the explanation generation module. To obtain the hash codes directly from the end-to-end neural network, we introduce an attentive TextCNN and an Adaptive Tanh layer in the preference prediction task. For explanation generation, Long Short-Term Memory is employed to generate the explanations for recommendation results from the binary hash codes of user and item. Experiments demonstrate the superiority of the proposed method.