基于用户角色与行为的协同过滤推荐算法

摘要

针对传统协同过滤推荐算法中存在评分数据稀疏性问题,以稀疏的用户打分来确定用户间的相似性可能并不准确.为此,提出了以用户行为对应一定分值代替空缺评分的方法来修正用户I-U评分矩阵,并基于用户角色以权重系数K来约束最近邻的计算.实验表明,改进的算法具有更优的推荐质量.%Aiming at sparsity of score data in the traditional collaborative filtering algorithm, the similarity among users base on this sparse ratings may not be accurate. For this reason, a collaborative filtering algorithm based on fixed I-U score matrix and weighted coefficient K to constrain the nearest neighbor calculation was proposed. The fixed I-U score matrix was presented by a certain percentile of user behavior instead of vacancies scoring. The weighted coefficient K was based on user role. Experiments show that the improved algorithm has better recommendation quality.