Abstract: Most of the trust-aware recommendation methods think that all friends with trust relationship have an impact on user ratings, which ignores the diversity of trust and the change of user interest with the field. This paper proposes a social recommendation algorithm based on domain-sensitive interest circle DSC-PMF. The DSC-PMF algorithm uses the Domain-Sensitive Interest Circle（DSC） model and a Probabilistic Matrix Factorization（PMF） strategy to recommend items to users in a specific domain. The DSC model only considers the influence of friends with similar interests, and measures the influence of different friends by decomposing trust value. It also defines “the user’s domain sensitivity” to assess different willingness to be influenced. According to the experimental results on the Yelp datasets, the proposed algorithm not only reduces MAE and RMSE, but also improves the recommendation accuracy of the system.

Key words: domain sensitivity, trust relationship, probabilistic matrix factorization, social recommendation

摘要： 为了更好地融入信任关系对用户评分的影响，并考虑用户兴趣随领域变化的特点，提出了一种基于领域敏感兴趣圈的社会化推荐算法DSC-PMF。DSC-PMF算法通过构造领域敏感兴趣圈（DSC）模型，并结合概率矩阵分解（PMF）推荐算法，对用户进行推荐。DSC模型仅考虑兴趣相似朋友的影响，用信任划分的方法度量了不同朋友的影响程度，同时引入用户领域敏感度来衡量用户受朋友影响的意愿程度。通过在Yelp数据集上的多组对比实验，该算法不仅降低了MAE和RMSE，还提高了系统推荐准确率。

关键词: 领域敏感度, 信任关系, 概率矩阵分解, 社会化推荐