综合用户特征及专家信任的协作过滤推荐算法

Collaborative Filtering Recommendation Algorithm Based on User Characteristics and Expert Opinions

协作过滤推荐算法是推荐系统中应用最广泛的算法之一。通过分析传统协作过滤算法中由数据稀疏性导致的推荐精度不高的问题,在基于专家信任的协作过滤推荐算法的基础上,提出了一种综合用户特征及专家信任的协作过滤推荐算法。该算法分析了用户的不同特征,比较了用户与专家的相似度,通过计算用户-专家相似度矩阵,有效降低了数据集的稀疏性,提高了预测的准确性。在MovieLens数据集上的实验结果表明,改进的算法能够有效缓解冷启动问题,明显提高了系统的推荐精度。

Collaborative filtering recommendation algorithm is one of the most widely used algorithms in recommender system.After analyzing the low precision problem caused by sparse data in conventional collaboration filtering algorithms,this paper proposed an collaboration filtering algorithm which integrates user characteristics and expert opinions.The algorithm analyzes user characteristics,compares the similarity between users and experts,and then calculates the similarity matrix.Our algorithm reduces the sparsity of dataset and improves the accuracy of prediction.Our experimental results based on the MovieLens dataset show that,by using our algorithm,performance on the cold start problem and relevant accuracy of recommendation has greatly improved.