基于精确欧氏局部敏感哈希的协同过滤推荐算法

Collaborative filtering recommendation algorithm based on exact Euclidean locality-sensitive hashing

针对推荐系统中用户评分数据的海量高维与稀疏性,以及直接利用传统相似性度量方法来获取近邻的计算量大、结果不准等对推荐质量的影响,提出基于精确欧氏局部敏感哈希(E2LSH)的协同过滤推荐算法。首先利用精确欧氏局部敏感哈希算法对用户评分数据进行降维处理并构建索引,以快速获取目标用户的近邻用户;然后利用加权策略来预测用户评分,进而完成协同过滤推荐。实验结果表明,该算法能有效解决用户数据的海量高维与稀疏性问题,且运行效率高,具有较好的推荐质量。

In recommendation systems, recommendation results are affected by the matter that rating data is characterized by large volume, high dimensionality, extreme sparsity, and the limitation of traditional similarity measuring methods in finding the nearest neighbors, including huge calculation and inaccurate results. Aiming at the poor recommendation quality, this paper presented a new collaborative filtering recommendation algorithm based on Exact Euclidean Locality-Sensitive Hashing （E2LSH）. Firstly, E2LSH algorithm was utilized to lower dimensionality and construct index for large rating data. Based on the index, the nearest neighbor users of target user could be obtained with great efficiency. Then, a weighted strategy was applied to predict the user ratings to perform collaborative filtering recommendation. The experimental results on typical dataset show that the proposed method can overcome the bottleneck of high dimensionality and sparsity to some degree, with high running efficiency and good recommendation performance.