Multi-semantic User Preference Learning Based On Attribute Motif

The sparsity of explicit user behaviors and the multi-semantics of behavioral patterns are the real problems that plague user preference learning.In order to solve this problem,an important method is to compensate for the sparsity of explicit behavior data by introducing auxiliary information(such as user / item attributes,relationship between users,etc.),and characterize and model the multi-semantics of behavior patterns through heterogeneous information networks(HIN).However,in order to learn user preferences,the existing HIN-based recommendation methods mainly capture the high-order connections between heterogeneous nodes through predefined patterns(such as meta-paths etc.),which is extremely dependent on the relevant domain knowledge;Secondly,the multi-semantics of user-item association patterns has not been fully studied.To address the above challenges faced by existing recommendation methods,this thesis proposes a novel HIN-based recommendation method.The thesis introduces attribute motifs,which are recurring higher-order substructures in HIN,to reveal meaningful selection patterns in HIN,so as to assist user preference learning and recommendation.The main work of this thesis is:(1)Because the frequent substructures in HIN can reflect the user's behavior patterns,especially the selection patterns(i.e.how users choose items),different from predefined patterns,this thesis proposes to use statistically significant substructures(i.e.attribute motifs)to capture meaningful selection patterns in HIN.The discovery of user selection patterns in HIN can better capture users' preferences and then assist in recommendation decisions.(2)In order to effectively mine and represent the multi semantic high-order association information between nodes in HIN,this thesis constructs a motif-based adjacency matrix to preserve the high-order semantic association between nodes based on different selection patterns,and designs a framework based on graph neural network to efficiently model the multi semantic association information and collaborative filtering signals,so as to realize user preference learning and recommendation.(3)In order to better model the multi-semantics for user and item association patterns and integrate them to accurately learn user preferences.Based on the above framework,the thesis introduces the neighbor-level and semantic-level attention mechanism modules.These two levels of attention mechanisms can learn the importance of different neighborhoods and association patterns,and can be used to fuse multi semantic information,so as to improve the recommendation performance.(4)Experiments on three datasets show that the method in this thesis is superior to the latest CF-based and HIN-based methods.In addition,analysis of experimental results also verifies the effectiveness of using attribute motifs to capture user's selection patterns.