Session-based Recommendation with Preference Interaction from Separate Adjacent Matrix
Improvements can be achieved by incorporating incoming and outgoing adjacent matrices to generate global and local preferences,and directly model the two preferences to build session rep-resentation.However,the incoming matrix and outgoing matrix of a session have no strong rele-vance,and their concatenation may introduce noise for building two preferences.Secondly,the global and local preferences can benefit from each other,and collaborative information of neighbor-hood sessions may help to improve recommendation performance.Therefore,a session-based recom-mendation with preference interaction from separate incoming adjacent matrix and outgoing adjacent matrix framework was proposed,which includes two parallel modules:an incoming session represen-tation encoder(ISE)and an outgoing session representation encoder(OSE).The ISE models ses-sion representation with incoming information through GNN and parallel co-attention mechanism.The OSE models session representation with outcome information through GNN and parallel co-atten-tion mechanism.Finally,a fusion gating mechanism is introduced to balance the importance of ses-sion representations resulting from ISE and OSE.The experimental results show that proposed model obviously outperforms other state-of-the-art methods on Yoochoose and Diginetica datasets.
万方数据
维普
