MULTI-TASK RECOMMENDATION MODEL COMBINING SELF-ATTENTION MECHANISM AND KNOWLEDGE GRAPH

Researchers have got increasingly attention to obtain auxiliary information with the help of knowledge graph. Aimed at the problem that recommendation algorithms based on knowledge graph have single user representation and cannot fully mine hidden information, a recommendation model combining self-attention mechanism and knowledge graph (KSMR) is proposed. The context information of user interaction sequence was captured by self-attention mechanism to obtain the user vector fused with interest transfer, and the feature correction and re-extraction were realized by text CNNs. Alternating training was used to combine the knowledge graph embedding task and recommendation task to achieve the purpose of collaborative optimization. Experimental results on real datasets MovieLens-1M and Last.FM show that, the CTR (Click Through Rate) prediction performance of the model has obvious advantages over the comparison algorithms.