Knowledge-Enhanced Graph Neural Network Model for Sequential Recommendation

Abstract

Existing sequential recommendation models based on the Graph Neural Network (GNN) focus on the structural information of users' interaction with items, and the learning of sequence preference involves the sequence of interactive items, which lack content information and user information, and does not mine a deep semantic relationship between items.A knowledge-enhanced GNN sequence recommendation model(KGGNN) is proposed.First, a knowledge graph is introduced and combined with users' interaction data to construct a collaborative knowledge graph, which can obtain the relevant auxiliary information of items and users.By transforming an interaction sequence into a directed sequence graph, the algorithm uses the Gated Graph Neural Network (GGNN) and relevant auxiliary information of the users to learn the structural information of the item node in the sequence.The model combines the vectors of the items as the global sequence preference using the attention mechanism, and the recently interactive item is considered as the current interest preference.The two preferences are fused to form the final sequence preference, which is used with the semantic relevant auxiliary information of the current item to predict the next item.The experimental results of Amazon Book, Last FM, and Yelp 2018 indicate that the auxiliary information can effectively help improve the accuracy of the hit rate(HIT@K) and normalized discounted cumulative gain(NDCG@K) metrics.Compared with GRU4Rec, NARM, SASRec, and other models, the aforementioned indicators have significantly improved.When the K value of the evaluation index is 10, compared with the KGSR model, HIT@10 is improved by 12.9%, 4.5%, and 6.9%, and NDCG@10 is improved by 29.4%, 5.7%, and 16.7%.