A user multi-preference recommendation system based on knowledge graph
A user multi-preference recommendation system based on knowledge graph(KG)was proposed in this paper,by which user preferences from three different perspectives:user relationship level,entity level,and fine-grained high-order user were modeled.Firstly,the relationship vectors in KG were combined to construct relationship level intentions,and the differences between different intentions were maximized through independence.Relationship level intentions were guided to learn relationship level preferences.Secondly,an entity preference graph(EPG)based on the frequency of user interaction with entities was constructed,and the user's entity level preferences were learned.Thirdly,relationship level intent and entity level preference were performed to guide the learning of user representations,respectively.In addition,the relationship entity information flow was directly constructed from KG for user representation and mining of high-order fine-grained preferences.Experiments were conducted on two benchmark datasets,which verified the effectiveness and feasibility of this method.
NETL
NSTL
万方数据
