基于精确欧氏局部敏感哈希的改进协同过滤推荐算法

Improved Collaborative Filtering Recommendation Algorithm Based on Exact Euclidean Locality Sensitive Hashing

针对经典协同过滤推荐算法中用户评分数据的规模大、高稀疏度以及直接进行相似度计算实时性差等问题,提出基于p-stable分布的分层精确欧氏局部敏感哈希(E2LSH)算法。利用E2LSH算法查找相似用户,在得到相似用户后使用加权平均方法对用户未评分项目进行评分预测,从而提高推荐结果的准确性。实验结果表明,与基于局部敏感哈希的协同过滤推荐算法相比,该算法具有较高的运行效率及推荐准确率。

Aiming at the large scale and high sparsity degree of user rating data and poor real-time capability of direct similarity calculation,this paper proposes a layered Exact Euclidean Locality Sensitive Hashing（E2LSH） algorithm based on p-stable distribution.It finds similar users to improve computing efficiency by using E2LSH algorithm,and uses weighted mean method to predict score for not rated items to improve the accuracy of recommendation results after getting the similar users.Experimental results show that,compared with the collaborative filtering recommendation algorithm based on Locality Sensitive Hashing（LSH）,this algorithm has higher efficiency and recommendation accuracy.