基于社交关系和条件补全的协同过滤推荐算法

摘要

针对传统协同过滤算法中存在数据稀疏、数据冗余和算法效率低等问题,提出一种基于社交关系和条件补全的协同过滤推荐算法.该算法将社交关系数据应用到矩阵补全过程中,减小原始矩阵的稀疏度,同时提高补全数据的精确度;在项目相似性计算时,条件性地选择参与计算的向量数据,减少数据的冗余度,并降低算法的时间复杂度.实验结果表明,改进算法的推荐准确率明显提高.%Aiming at the problems that traditional collaborative filtering algorithm existed data sparseness,data redundancy and low efficiency,we proposed a collaborative filtering recommendation algorithm based on social relation and condition completion.The algorithm applied the data of social relationship into the process of matrix completion to reduce the sparse degree of the original matrix and improve the accuracy of the data completion.The vector data involved in computation was conditionally chosen to reduce the redundancy of the data and the time complexity of the algorithm in the computation of the project similarity.The experimental results show that the accuracy of recommendation of the proposed algorithm is obviously improved.