计算机科学 ›› 2019, Vol. 46 ›› Issue (2): 202-209.doi: 10.11896/j.issn.1002-137X.2019.02.031
毛宇佳, 刘学军, 徐新艳, 张欣
MAO Yu-jia, LIU Xue-jun, XU Xin-yan, ZHANG Xin
摘要: 以多个用户为推荐对象的组推荐系统已成为研究热点。目前,组推荐系统大多考虑如何充分挖掘用户偏好来尽可能满足所有用户的需求,但这也造成了推荐列表规模过大的问题,从而导致群组成员无法快速做出决定。针对该问题,文中提出了一种缩小群组推荐列表的方法(Recommendation Method based on Sub-Group and Social Behavior,RMSGSB)。该方法通过划分子组来缩小群组规模并减少群组偏好属性数量,利用成员的社会行为,从容忍度与利他行为两方面为子组分配权重,以保证推荐公平性。在真实数据集上的实验对比结果表明,该算法具有更好的群组推荐效果。
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