现有的基于信任的推荐算法通常假设用户是单一和同质的，没有充分挖掘信任关系信息，且相似关系和信任关系的融合缺乏高效的模型，极大地影响了推荐的准确性和可靠性.提出一种基于信任的推荐算法.首先，结合全局信任和局部信任，并利用信任的传播性质对信任关系进行建模；然后，设置推荐权重，综合考虑相似度和信任度来构建用户间的偏好关系，筛选出邻居；最后，将基于记忆的协同过滤思想和社交网络的信任关系融入概率矩阵分解模型，同时使用自适应权重动态决定各部分的影响程度，形成高效、统一的可信推荐模型Trust-PMF.该算法在FilmTrust，Epinions这两个数据集上与相关算法做了对比验证，结果证实了该算法的高效性.

The existing trust-based recommendation algorithms usually assume that users are homogeneous, and therefore can't fully mine the trust relationship information. Moreover, the lack of efficient model for integrating similar relationship and trust relationship greatly affects the accuracy and reliability of those models. To solve the issue, this paper first proposes a trust-based recommendation algorithm called Trust-PMF. It combines similarity with trust to build user' preference and selects the target user's neighbors. Then, the probability matrix factorization model is extended by integrating memory-based idea and trust information, and a dynamic adaptive weight is used to determine the degree of influence of each part to form a unified and efficient Trust-PMF model. Finally, experiment results on Filmtrust and Epinions data sets are presented to demonstrate that the proposed method outperforms the state-of-the-art methods.

TP311

国家自然科学基金（61070053）