Two-Level Graph Path Reasoning for Conversational Recommendation with User Realistic Preference

Conversational recommender systems model user dynamic preferences and recommend items based on multi-turn interactions. Though the conversational recommender system has achieved good performance, it has two limitations. On the one hand, researchers usually random select an anchor item from user's historical interactions to simulate the interaction with the real user, but some items in the historical interactions do not fit the user realistic preferences (item noise). On the other hand, it pays too much attention to user dynamic preferences, but nurses some static preferences that are difficult to change over a short period. In fact, when there is no explicit attribute preference in user's conversation, the user static preferences can also be used to make recommendations. To address the aforementioned issues, a novel method that combines graph path reasoning with multi-turn conversation is proposed, called Graph Path reasoning for conversational Recommendation (GPR). In GPR, a soft-clustering is designed to classify items and then set operations are utilized to filter the noise in the user's historical interactions. To capture user dynamic preferences and take account of the user inherent static preferences, GPR asks questions about attributes in the attribute-level reasoning and asks whether the items fit user static preferences in the item-level reasoning on a heterogeneous graph. In the multi-turn of two-level graph path reasoning, a reinforcement learning is used to obtain the optimal path and accurately recommend items to users. Extensive experiments conducted on two benchmark datasets verify that GPR can significantly improve recommendation performance and reduce the turn of path reasoning.