标题： 通过融合协同过滤和社会关系提高推荐质量 显示英文标题

标题： Improving Recommendation Quality by Merging Collaborative Filtering and Social Relationships

摘要： 矩阵分解技术已被成功应用于提高基于协同过滤系统（CFSs）生成的建议质量。 基于矩阵分解的传统CFSs处理用户提供的评分，并最近被扩展以纳入人口统计学方面，如年龄和性别。 在本文中，我们提出将基于矩阵分解的CFS与关于社会友谊的信息相结合，以向用户提供更准确的物品推荐和排名。 所提出的方法已在真实的生活在线社交网络上进行了评估；实验结果表明其优于现有的CFSs。 还提供了与相关文献的详细比较。 显示英文摘要

摘要： Matrix Factorization techniques have been successfully applied to raise the quality of suggestions generated by Collaborative Filtering Systems (CFSs). Traditional CFSs based on Matrix Factorization operate on the ratings provided by users and have been recently extended to incorporate demographic aspects such as age and gender. In this paper we propose to merge CFS based on Matrix Factorization and information regarding social friendships in order to provide users with more accurate suggestions and rankings on items of their interest. The proposed approach has been evaluated on a real-life online social network; the experimental results show an improvement against existing CFSs. A detailed comparison with related literature is also present.