基于用户信任和张量分解的社会网络推荐

社会化网络中的推荐系统可以在浩瀚的数据海洋中给用户推荐相关的信息。社会网络中用户之间的信任关系已经被用于推荐算法中，但是目前的基于信任的推荐算法都是单一的信任模型。提出了一种基于主题的张量分解的用户信任推荐算法，用来挖掘用户在不同的物品选取的时候对不同朋友的信任程度。由于社交网络更新速度快，鉴于目前的基于信任算法大都是静态算法，提出了一种增量更新的张量分解算法用于用户信任的推荐算法。实验结果表明：所提出的基于主题的用户信任推荐算法比现有算法具有更好的准确性，并且增量更新的推荐算法可以大幅度提高推荐算法在训练数据增加后的模型训练效率，适合更新速度快的社会化网络中的推荐任务。

Social Recommendations Based on User Trust and Tensor Factorization

In social networks, recommender systems can help users to deal with information overload and provide personalized recommendations to them. The trust relationship of users is used in the social networks' recommender systems. But the state-of-art algorithms only use the single trust relationship which cannot capture the trust to user's friends when looking for different items. This paper proposes a topic-based trust recommendation algorithm using tensor factorization model. As the social information changes rapidly, the state-of-art algorithms often need redo factorization. To address the issue, the paper also presents an effective incremental method to adaptively update its previous factorized components rather than re-computing them on the whole dataset when the data changes. Experiments show that the proposed method can achieve better performance and the incremental method is suitable for the rapid changes in the social networks.