Heterogeneous Graph Neural Network for Personalized Session-Based Recommendation with User-Session Constraints
This work addresses the challenge of personalizing recommendations in session-based systems for users, though it appears incremental by building on existing graph neural network approaches.
The paper tackled the problem of generating high-quality user representations for session-based recommendation by using a heterogeneous attention network to model session relationships and applying constraints to align user preferences with session representations, resulting in outperforming other methods on multiple real-world datasets.
The recommendation system provides users with an appropriate limit of recent online large amounts of information. Session-based recommendation, a sub-area of recommender systems, attempts to recommend items by interpreting sessions that consist of sequences of items. Recently, research to include user information in these sessions is progress. However, it is difficult to generate high-quality user representation that includes session representations generated by user. In this paper, we consider various relationships in graph created by sessions through Heterogeneous attention network. Constraints also force user representations to consider the user's preferences presented in the session. It seeks to increase performance through additional optimization in the training process. The proposed model outperformed other methods on various real-world datasets.