一种基于社会信任潜在因子模型的推荐方法

TBLFM:Trust Based Latent Factor Model for Social Recommendation

随着社交网络的快速发展、社交网络用户规模的不断扩大,如何为用户推荐感兴趣的信息变得越发困难。传统的推荐方法利用用户兴趣的历史数据来预测用户未来感兴趣的项目,忽视了社交网络中的信任关系,导致推荐方法的推荐质量不高。针对上述问题,提出了基于社会信任潜在因子模型的推荐方法。该方法引入社会信任来度量社交网络中朋友之间的隐含信任关系,根据社会信任程度来选择用户信任的朋友,对用户信任的朋友与目标用户的共同兴趣进行潜在因子分析,构建基于社会信任的潜在因子模型,实现目标用户的前k个项目推荐。真实数据集上的对比实验结果表明,基于社会信任潜在因子模型的推荐方法在推荐质量上优于现有的推荐方法。

Recently online social networks have become the major platform with millions of registered users on the Web. The amount of information is increasing so quickly that users can＇t handle the information overload without the support of recommendation methods. Traditional recommendation methods have a limited performance in the context of social recommendation due to not considering the social network information, such as trust. Trust-based methods at- tempt to introduce a trust metric during the social recommendation. However, most of these methods are based on the explicit trust statements expressed by users, which are not available in the social networks such as Facebook, Twitter and Sina Weibo. This paper presented a trust metric to quantitatively measure the recommendation trust between pairs of users by aggregating the implicit trust and trust propagation values. We proposed a trust-based latent factor model, which incorporates the pairwise recommendation trust values into the probabilistic model for top-k item recommenda- tion. The experiments on Sina Weibo demonstrate that our method outperforms the traditional recommendation methods and trust-based methods.