The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is mostly restricted to the collaborative filtering (CF) scenario, where the interaction contexts are not available.
To extend the advantages of graph convolutions to context-aware recommender system (CARS), which represents a generic type of models that can handle various side information, a research team led by Xiangnan HE published their new research on January 22nd, 2022 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.
The team develops a new model, GCM, which captures the interactions among multiple user behaviors via graph neural networks, and then models the interactions among features of individual behavior via factorization machine. To demonstrate the effectiveness of GCM, they test it on three public datasets. Extensive experiments also are conducted to verify the rationality of attributed graph and offer insights into how the representations benefit from such graph learning.
Organizing user behaviors with contextual information in graphs is a promising direction to build an effective context-aware recommender. It helps build strong representations for users and items. GCM simply unifies all context features as an edge, neglecting the dynamic characteristics of some contexts (e.g., time) and hardly capturing the dynamic preference of users. Future work could be done on building dynamic graphs based on contextual information instead of one static graph, or devising a dynamic graph neural network.
Frontiers of Computer Science is intended to facilitate effective communication and exchanges between scientists in China and abroad. It will reflect the significant advances that are currently being made in computer science. The multidisciplinary character of this field will be typified by providing the readers with a broad range of articles. They include original review articles, research papers written by individual researchers and research groups which appeal to the international community of academics and other professionals.
