融合用户信任和影响力的top-N推荐算法

针对现有基于信任的推荐方法通常直接利用社交网络的二值信任关系来提高推荐质量，较少考虑用户间信任强度的差异和潜在影响的问题,提出结合用户信任和影响力的混合推荐算法进行top-N项目推荐. 采用自动编码器对用户行为进行无监督的初始特征优化，将高维、稀疏的用户行为压缩成低维、稠密的用户及项目特征向量；提出融合用户交互信息、偏好度和信任的新型信任度量模型，发掘社交网络中用户间的隐含信任关系，重构社会信任网络；将社会信任网络的拓扑结构和用户的交互信息融入结构洞算法，通过改进的结构洞算法来识别网络中的影响力用户，提高top-N项目推荐性能. 实验在FilmTrust、Epinions、Ciao这3个标准数据集上进行对比验证，实验结果证明了所提算法的有效性.

Top-N recommendation algorithm combining user trust and influence

A hybrid recommendation algorithm with the incorporation of user trust and social influence was proposed for top-N item recommendation, in view of the existing trust-aware recommendation systems, which directly use the binary trust relationship of social networks to improve the quality of recommendation, and less consider the difference of trust intensity and potential impact between users. The auto-encoder is used to perform unsupervised initial feature optimization on user behavior, and the high-dimensional and sparse user behaviors are compressed into low dimensional and dense users and item feature vectors. A novel trust value measurement model that combines user interaction information, preferences, and trust is brought up to explore the implicit trust relationship between users in social networks and reconstruct the social trust network. The improved structure hole algorithm is used to identify the influential users in the network and improve the top-N item recommendation performance, which integrates the topological structure of the social trust network and the user's interactive information. Comparison verification was conducted on three standard datasets, FilmTrust, Epinions and Ciao, and experimental results demonstrated the effectiveness of the proposed algorithm.