Abstract
•An end-to-end framework which naturally incorporates KG into the recommender system.•The first KG-based recommender system combining path-based method and propagation-based method.•A method that can automatically discover the most representative user preference is proposed.•Both High-order relationship and local domain features of users/items are learned and aggregated.•The proposed algorithm achieves competitive results on three real-world datasets.
Because a knowledge graph’s huge amount of item information can help recommender systems develop user/item representations, it has become the most important source of side information. Regardless of the numerous types of user/item representation approaches used in knowledge graph-based recommendation scenarios, they all have problems. In this paper, we propose a knowledge graph-based multi-context-aware recommendation algorithm for learning user/item representations that combines the advantages of path-based and propagation-based methods. A new concept (i.e., rule) is proposed first, which can be a useful way to characterize the user’s preferences. Next, based on user-item interactions, an automatic rule discovery algorithm is proposed that can automatically select the most representative user preferences templates in a given recommendation scenario based on the knowledge graph and user behaviors. Then, the learning of high-order connectivity between long-distance user-item pairs is realized according to these templates. After that, a feature representation method of the local neighborhood characteristics of users and items is introduced to compensate for the defect that the path-based method can only catch the high-order connectivity. The experimental results demonstrate MANN’s superiority over eight state-of-the-art baselines.