基于改进LSH的协同过滤推荐算法

协同过滤是个性化推荐系统中应用较为成功与广泛的技术之一,影响协同过滤推荐质量的关键在于获取目标用户的k近邻用户,然后基于k近邻对其未评价的项目进行评分预测与推荐。针对用户评分数据的规模大、维度高、高度稀疏以及直接进行相似性度量的实时性差等对推荐性能的影响,提出一种基于LSH的协同过滤推荐算法,并对其进行改进。该算法基于p稳态分布的局部敏感哈希对用户评分数据进行降维与索引,并采用多探寻的机制对其进行改进,缓解多个哈希表对内存的压力,快速获取目标用户的近邻用户集合,然后采用加权方法来预测用户评分并产生推荐。标准数据集上的实验结果表明,该方法能有效克服评分数据的高维稀疏,并在保证一定推荐精度的前提下,大幅度提高推荐效率和降低内存消耗。

Collaborative Filtering Recommendation Algorithm Based on Improved Locality-sensitive Hashing

Collaborative filtering is one of the key technologies widely applied in personalized recommendation system with great success.The critical step of collaborative filtering is to get k nearest neighbors (kNNs),which is utilized to predict user ratings and recommend.In order to improve the recommendation quality which is affected by the matter that rating data is characterized by its large scalability,high dimensionality,extreme sparsity,and the lower real-time ability by direct similarity measuring method in finding the nearest neighbors,we proposed a collaborative filtering re-commendation algorithm based on locality-sensitive hashing,and improved it.The algorithm applies locality-sensitive ha-shing technology based on p-state distribution to get lower dimensionality and index for large rating data.Then a multi-probe mechanism is utilized to improve the algorithm with great efficiency in obtaining the approximate nearest users of target user.Then,a weighted method is used to predict the user ratings,and finally perform collaborative filtering recommendation.Experiment results on typical dataset show that the proposed algorithm can overcome the limitation of high dimensionality and sparsity in some degree,and has good recommendation performance,high efficiency and less memory consumption.