A Novel Recommendation Model Based on Interactive Nearest Neighbor Sessions

For some session recommendation algorithms, only the information in the target session is modeled, ignoring the auxiliary role of the interactive nearest neighbor sessions to the target session, resulting in the potential collaborative information is not fully utilized. Therefore,we propose a novel recommendation model based on interactive nearest neighbor sessions(ARMBINNS). Firstly,a directed current session graph (DCSG) is constructed, which focuses on the conversion transitions between frequent items in the target session. The directed current session graph is modeled by graph neural network and soft attention mechanism to generate the session representation of user preference items. Then we search the interactive nearest neighbor sessions of the target session, select items from interactive nearest neighbor sessions and construct undirected interactive nearest neighbor graph (UING) with the target session. Similarly, the undirected interactive nearest neighbor graph is modeled by graph neural network and soft attention mechanism to generate a session representation with nearest neighbor information. Finally, rich session embedding is generated by combining the two types of session representation information through the fusion gating mechanism. Through experiments, it is verified that the proposed model has better recommendation performance compared with 9 advanced recommendation methods.