Learning Item Attributes and User Interests for Knowledge Graph Enhanced Recommendation

Knowledge Graphs (KGs) manifest great potential in recommendation. This is ascribable to the rich attribute information contained in KG, such as the price attribute of goods, which is further integrated into item and user representations and improves recommendation performance as side information. However, existing knowledge-aware methods leverage attribute information at a coarse-grained level in two aspects: (1) item representations don't accurately learn the distributional characteristics of different attributes, and (2) user representations don't sufficiently recognize the pattern of user preferences towards attributes. In this paper, we propose a novel attentive knowledge graph attribute network(AKGAN) to learn item attributes and user interests via attribute information inKG. Technically, AKGAN adopts a novel graph neural network framework, which has a different design between the first layer and the latter layer. The first layer merges one-hop neighbors' attribute information by concatenation operation to avoid breaking down the independence of different attributes, and the latter layer recursively propagates attribute information without weight decrease of high-order significant neighbors. With one attribute placed in the corresponding range of element-wise positions, AKGAN employs a novel interest-aware attention unit, which releases the limitation that the sum of attention weight is 1, to model the complexity and personality of user interests. Experimental results on three benchmark datasets show that AKGAN achieves significant improvements over the state-of-the-art methods. Further analyses show that AKGAN offers interpretable explanations for user preferences towards attributes.