改进非对称相似度和关联正则化的推荐算法

为了改善传统推荐系统中数据稀疏问题给推荐效果带来的影响，提出了改进非对称相似度和关联正则化的推荐算法。根据不同用户和不同项目之间的不对称关系，提出一种改进相关度计算式，用于预测评分。同时，由于社会化隐式关系的获取难度较大，利用传统相似度获取邻域集合作为用户社会关系，将关联正则化用于约束矩阵分解目标函数，缓解用户信息不对称造成的数据稀疏问题。最后在一些真实数据集上对算法进行验证，实验结果表明，与主流的推荐算法相比，该算法能够更加有效地预测实际评分。

Recommendation Algorithm for Improving Asymmetric Similarity and Associated Regularization

In order to improve the impact of data sparseness on the recommendation effect in the traditional recommendation system, a recommendation algorithm for improving asymmetric similarity and associated regularization is proposed. According to the asymmetry relationship between different users and different projects, an improved correlation calculation formula is proposed for predicting the score. At the same time, due to the difficulty in obtaining the implicit relationship of socialization, this paper uses the traditional similarity to obtain the neighborhood set as the user social relationship, and the regularization is used to constrain the matrix decomposition objective function to alleviate the data sparse problem caused by user information asymmetry. Finally, the algorithm is validated in some real dataset. The experimental results show that the algorithm can predict the actual score more effectively than the mainstream recommendation algorithms.