基于从众心理矩阵和加权相似度的推荐算法

Recommendation algorithm based on conformity mental matrix and weighted similarity

为提高推荐算法的精度,提出一种基于从众心理矩阵和加权相似度的推荐算法。引入项目流行度阈值K,根据项目流行度对项目进行筛选,对高流行度项目的评分矩阵进行调整;通过用户兴趣度量函数度量用户的现阶段偏好项目,基于每个用户的现阶段偏好项目计算用户间的兴趣相似度;采用相似度加权融合的方式获取用户相似度。实验结果表明,该算法的推荐精度优于传统的协同过滤算法。

To improve the accuracy of recommendation algorithm, a recommendation algorithm based on crowd psychology matrix and weighted similarity was proposed. The project popularity threshold K was introduced, and the project was screened accor- ding to the popularity of the project, the score of the high prevalence project was adjusted. The user ’s current preference items were measured using the user interest metric function, and the interest similarity between users was calculated based on the current phase preference of each user. Similarity weighted fusion was adopted to get user similarity. Experimental results show that the proposed algorithm is superior to the traditional collaborative filtering algorithm in terms of recommendation accuracy.