An Improved Recommendation Model with Self-outer Product Enhanced Heterogeneous Information Network

Network embedding has gained a lot of attention in BigData research in recent years , and heterogeneous information network (HIN) have been applied to improve the performance of recommendation system with various mining methods. However, the existing extraction methods do not take into account the useful information implied by the interaction of different dimensions of these vectors themselves, so we propose an improved recommendation model with self-outer product enhanced HIN (HSopRec) ,with which the potential relationship originally implied in the HIN between users and items can be effectively extracted through self-outer product. Compared with other similar models based on HINs, the HSopRec model shows very good results on the world's open business data set Yelp.