Sequential Recommendation Based on Multi-space Attribute Information Fusion
The goal of sequential recommendation is to model users'dynamic interests from their historical behaviors,and hence to make recommendations related to the users'interests.Recently,attribute information has been demonstrated to improve the performance of sequential recommendation.Many efforts have been made to improve the performance of sequential recommenda-tion based on attribute information fusion,and have achieved success,but there are still some deficiencies.First,they do not ex-plicitly model user preferences for attribute information or only model one attribute information preference vector,which cannot fully express user preferences.Second,the fusion process of attribute information in existing works does not consider the in-fluence of user personalized information.Aiming at the above-mentioned deficiencies,this paper proposes sequential recommenda-tion based on multi-space attribute information fusion(MAIF-SR),and proposes a multi-space attribute information fusion frame-work,fuse attribute information sequence in different attri-bute information spaces and model user preferences for different at-tribute information,fully expressing user preferences using multi-dimensional interests.A personalized attribute attention mecha-nism is designed to introduce user personalized information during the fusion process,enhance the personalized effect of the fusion information.Experimental results on two public data sets and one industrial private data set show that MAIF-SR is superior to other comparative sequential recommendation models based on attribute information fusion.
万方数据
维普
