FROG模型融合多模态信息与好友图结构，提升游戏好友推荐效果。
FROG: Effective Friend Recommendation in Online Games via Modality-aware User Preferences
- 通过模态感知机制捕捉用户在图文等多模态上的偏好差异
- 在腾讯线上部署中显著优于现有方法，提升推荐准确率
- 适合需要精准社交推荐的游戏平台或社交应用
由于移动设备的便捷性，在线游戏已成为用户娱乐的重要方式，催生了游戏内好友推荐的需求。然而，现有方法难以有效结合用户的多模态特征（如图像和文本）与好友关系图的结构信息，主要受限于：(1) 忽视用户间的高阶结构邻近性；(2) 无法在模态层面学习用户间的成对相关性；(3) 无法同时建模不同模态下的局部与全局偏好。针对这些问题，本文提出端到端模型FROG，更有效地建模潜在好友的用户偏好。在腾讯的离线评估与线上部署实验均表明，FROG显著优于现有方法。
原文摘要 · Abstract (English)
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.
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