基于用户非对称相似性的协同过滤推荐算法

针对传统协同过滤推荐算法的数据稀疏以及用户关系衡量不准确的问题,提出了基于用户非对称相似关系的推荐算法.利用用户的潜在特征的样本数量,结合奇异值矩阵分解,计算用户之间非对称的相似度,明确用户间关系.仿真结果表明,随着邻居数量的增加,该算法的平均绝对误差始终优于传统算法,误差值在邻居数量为40~60之间值为最小,约为0.682,传统算法平均绝对误差值约为0.758,可以看出该算法判断用户关系较为准确,预测评分比传统算法更接近实际评分.

Collaborative filtering recommendation algorithm based on asymmetric similarity of users

Aiming at data sparseness and inaccurate user relationship in traditional collaborative filtering recommendation algorithm, an improvement recommendation algorithm based on asymmetric similarity relationship of users is proposed.By using the sample number of potential features and the decomposition of singular value matrix, the asymmetric similarity between users is calculated, and the relation between users is defined.The simulation results show that , the Mean Absolute Error of the algorithm is superior to the traditional algorithm with the increase of the number of neighbours,the minimal value is about 0.682 between the number of neighbors is 40-60. The value of Mean Absolute Error is about 0.758 of the traditional algorithm. It can be seen that the algorithm to determine the user relationship is more accurate, predictive score is relatively close to the actual score.