中文摘要：

为了进一步提高推荐算法的准确率，更好地对用户间的信任关系进行建模，首先提出了一种信任关系强度敏感的社会化推荐算法（StrengthMF）．与以往的算法相比，该算法假设建立信任关系的两个用户之间并不一定存在着相似的兴趣爱好．在推荐过程中，StrengthMF算法通过共享的潜在用户特征空间来对信任关系强度和用户兴趣进行建模，通过进一步识别出那些与目标用户有着共同爱好的朋友来对求解的过程进行优化．为了验证算法所估计出的信任关系强度的准确性，接着又在SocialMF算法的基础上，提出了一种使用所估计的信任关系对其重新训练和学习的InfluenceMF算法．实验结果表明，与目前较为流行的方法相比，新方法能在RMSE和MAE上取得更好的推荐结果，其所推导出的信任关系强度能进一步提高已有推荐算法的性能．

英文摘要：

With the advent of social networks, trust-aware recommendation methods have been well studied. Most of these algorithms assume that trusted users will have similar tastes. However, this assumption ignores the fact that two users may establish a trust connection for the social purpose or simply for etiquette, which may not result in similar opinions on the same item. Motivated by this observation, a novel trust strength aware social recommendation method, StrengthMF, is firstly proposed. Compared with previous methods, this new approach assumes that a trust relation does not necessarily guarantee the similarity in preferences between two users. Specificly, StrengthMF learns the trust strength and distinguishes users with more similar interests through the shared user latent feature space, i. e. , the user latent feature space in trust network is the same in the rating matrix. This will allow us to acquire a better understanding of the relationship between trust relation and rating similarity. To validate the learned trust strength, InfluenceMF method is then proposed, which retrains SocialMF with estimated trust relations. Experimental results on real world product rating data set Epinions show that the proposed approaches outperform the state-of-the-art algorithms in terms of RMSE and MAE, and the learned trust strength can further improve traditional recommendation methods.