摘要： 在协同过滤算法中,用户之间的相似性计算影响推荐系统的质量,尤其是在数据稀疏的情况下得到的用户之间的关系同实际情况偏离较大,影响推荐精度。针对上述问题,提出一种新的相似性计算算法。利用用户评分结构特征的稳定性,同时结合评分结构间的偏好距离,重新计算用户间偏好相似度。在MovieLens数据集上的实验结果表明,与传统基于用户的相关相似性协同过滤算法及余弦相似性算法相比,该算法的推荐精度平均提高3.94%和2.99%。

关键词: 协同过滤, 推荐系统, 数据稀疏, 评分结构, 偏好距离

Abstract: The quality of the recommendation system is affected by the computing of the similarity between users in the collaborative filtering algorithm. Especially in the case of data sparsity,compared with the actual situation, the resulting relationship between users deviates too much, affecting the accuracy of recommendation. Aiming at the problem, a new similarity calculation method is proposed.Using the stability of a user’s score structural features, combined with the preference distance between the score structure, the characteristics of the two parts are fused to recalculate the similarity between user preferences. On the whole, this method makes full use of the user’s score features and score data space. Experimental results on the MovieLens dataset show that compared with the traditional User-based Similarity(UBS)collaborative filtering algorithm and Cosine(COS)similarity algorithm,the recommendation accuracy of the proposed algorithm has an average increase of 3.94% and 2.99%.

Key words: collaborative filtering, recommendation system, data sparsity, grade structure, preference distance

中图分类号: 

• TP311