This paper explores recommender systems in social networks which leverage information such as item rating, intra-item similarities, and trust graph. We demonstrate that item-rating information is more influential than other information types in a collaborative filtering approach. The trust graph-based approaches were found to be more robust to network adversarial attacks due to hard-to-manipulate trust structures. Intra-item information, although sub-optimal in isolation, enhances the consistency of predictions and lower-end performance when fused with other information forms. Additionally, the Weighted Average framework is introduced, enabling the construction of recommendation systems around any user-to-user similarity metric. All the codes are publicly available on GitHub.

翻译：本文探讨了社交网络中利用项目评分、项目间相似性和信任图等信息的推荐系统。我们证明在协同过滤方法中，项目评分信息比其他信息类型更具影响力。研究发现，基于信任图的方法由于信任结构难以操纵，对网络对抗攻击具有更强的鲁棒性。项目间信息虽单独使用效果欠佳，但与其他信息形式融合后能提升预测一致性并改善低端性能。此外，本文引入了加权平均框架，支持围绕任意用户间相似性度量构建推荐系统。所有代码已在GitHub上开源。