FROG: Effective Friend Recommendation in Online Games via Modality-aware User Preferences
This addresses the need for better friend recommendations in online games, though it appears incremental as it builds on existing approaches by addressing specific limitations.
The paper tackled the problem of friend recommendation in online games by proposing FROG, a model that effectively incorporates multi-modal user features and structural information, demonstrating superiority in offline and online evaluations at Tencent.
Due to the convenience of mobile devices, the online games have become an important part for user entertainments in reality, creating a demand for friend recommendation in online games. However, none of existing approaches can effectively incorporate the multi-modal user features (e.g., images and texts) with the structural information in the friendship graph, due to the following limitations: (1) some of them ignore the high-order structural proximity between users, (2) some fail to learn the pairwise relevance between users at modality-specific level, and (3) some cannot capture both the local and global user preferences on different modalities. By addressing these issues, in this paper, we propose an end-to-end model FROG that better models the user preferences on potential friends. Comprehensive experiments on both offline evaluation and online deployment at Tencent have demonstrated the superiority of FROG over existing approaches.