基于信息熵和改进相似度协同过滤算法

为了减少协同过滤算法存在的噪音数据以及数据稀疏性问题，提高算法准确性，本文提出一种基于信息熵和改进相似度的协同过滤算法，使用用户信息熵模型来判断噪音数据，排除噪音数据对实验结果的干扰；使用面向稀疏数据的改进相似度计算方法，使用全部评分数据而不是依靠共同的评分项来计算，对缓解稀疏数据对推荐结果的精确性影响有很大帮助。实验结果表明，该算法能在一定程度上排除噪音数据对结果的影响，缓解数据稀疏对推荐结果精确性的干扰，提高该推荐算法的精确性，且缓解了传统推荐系统算法中常见的一些问题，与传统的协同过滤算法相比，该算法的精确性更高。

A Collaborative Filtering Algorithm Based on Information Entropy and Improved Similarity

In order to reduce the noisy data and data sparsity problems in the collaborative filtering algorithm, and improve the accuracy of the algorithm, a collaborative filtering algorithm based on information entropy and improved similarity is proposed. The user information entropy model is used to judge the noise data to eliminate the interference of the noise data on the experimental results; the improved similarity calculation method for sparse data is used, and all the score data are used. Rather than relying on common scoring items to calculate, it is of great help to alleviate the impact of sparse data on the accuracy of the recommended results. Experimental results show that the algorithm can eliminate the influence of noisy data on the results to a certain extent, alleviate the interference of data sparseness on the accuracy of recommendation results, improve the accuracy of the recommendation algorithm, and alleviate some common problems in traditional recommendation system algorithms. Compared with the traditional collaborative filtering algorithms, the accuracy of the algorithm is higher.