基于项目和信任的协同过滤推荐算法

为解决冷启动用户的推荐问题，对TrustWalker算法在相似度计算、可能性项目选择和预测评分等方面进行改进，提出一种基于项目和信任的协同过滤推荐算法CoTrustWalker。采用云模型相似度方法计算项目间的相似度，通过选择最相似的若干个项目的聚合结果作为随机游走的返回结果，从而提高推荐结果的稳定性。实验结果表明，CoTrustWalker算法在小规模数据集上与TrustWalker算法相比，其推荐质量和推荐速度均有较大提高。

Collaborative Filtering Recommendation Algorithm Based on Item and Trust

To solve recommendation problem for cold-start users, this paper proposes a collaborative filtering algorithm CoTrustWalker that combines item-based collaborative filtering and trust-based recommendation, which improves TrustWalker algorithm in similarity calculation, the possibility of item selection and prediction score. CoTrustWalker algorithm adopts the cloud model similarity method to compute the item similarities, so as to enhance the stability of recommendation results by selecting the most similar polymerization results of some items. Experimental results show that CoTrustWalker algorithm improves the quality of recommendation and the speed compared with TrustWalker algorithm.