A Tri-Attention Neural Network Model-BasedRecommendation

الملخص EN

Heterogeneous information network (HIN), which contains various types of nodes and links, has been applied in recommender systems.

Although HIN-based recommendation approaches perform better than the traditional recommendation approaches, they still have the following problems: for example, meta-paths are manually selected, not automatically; meta-path representations are rarely explicitly learned; and the global and local information of each node in HIN has not been simultaneously explored.

To solve the above deficiencies, we propose a tri-attention neural network (TANN) model for recommendation task.

The proposed TANN model applies the stud genetic algorithm to automatically select meta-paths at first.

Then, it learns global and local representations of each node, as well as the representations of meta-paths existing in HIN.

After that, a tri-attention mechanism is proposed to enhance the mutual influence among users, items, and their related meta-paths.

Finally, the encoded interaction information among the user, the item, and their related meta-paths, which contain more semantic information can be used for recommendation task.

Extensive experiments on the Douban Movie, MovieLens, and Yelp datasets have demonstrated the outstanding performance of the proposed approach.