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.

中文翻译：

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基于用户兴趣和信任值的评级预测推荐方法

协作过滤推荐算法是个性化推荐系统中研究最广泛的推荐算法之一。针对传统协同过滤推荐算法中存在的数据稀疏性，导致推荐精度不高，推荐效率低的问题，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，考虑到传统的得分相似度计算过分依赖于共同的得分项，将Bhattacharyya相似度计算引入到传统的计算公式中。其次，增加信任权重，以准确计算直接信任值；引入信任转移机制，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度进行整合，并通过信任权重方法生成预测结果。实验表明，该算法可以有效提高推荐的预测精度。