Chen Hailong, Sun Haijiao, Cheng Miao, Yan Wuyue
Department of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang 150000, China.
Comput Intell Neurosci. 2021 Mar 6;2021:6677920. doi: 10.1155/2021/6677920. eCollection 2021.
Collaborative filtering recommendation algorithm is one of the most researched and widely used recommendation algorithms in personalized recommendation systems. Aiming at the problem of data sparsity existing in the traditional collaborative filtering recommendation algorithm, which leads to inaccurate recommendation accuracy and low recommendation efficiency, an improved collaborative filtering algorithm is proposed in this paper. The algorithm is improved in the following three aspects: firstly, considering that the traditional scoring similarity calculation excessively relies on the common scoring items, the Bhattacharyya similarity calculation is introduced into the traditional calculation formula; secondly, the trust weight is added to accurately calculate the direct trust value and the trust transfer mechanism is introduced to calculate the indirect trust value between users; finally, the user similarity and user trust are integrated, and the prediction result is generated by the trust weighting method. Experiments show that the proposed algorithm can effectively improve the prediction accuracy of recommendations.
协同过滤推荐算法是个性化推荐系统中研究最多、应用最广泛的推荐算法之一。针对传统协同过滤推荐算法存在的数据稀疏问题，导致推荐准确率不准确、推荐效率低下，本文提出了一种改进的协同过滤算法。该算法在以下三个方面进行了改进：首先，考虑到传统评分相似度计算过度依赖共同评分项目，将巴氏相似度计算引入传统计算公式；其次，添加信任权重以准确计算直接信任值，并引入信任传递机制来计算用户之间的间接信任值；最后，将用户相似度和用户信任进行整合，并通过信任加权方法生成预测结果。实验表明，所提算法能够有效提高推荐的预测准确率。
