基于群偏好与用户偏好协同演化的群推荐方法

群推荐系统已经成为社交网络平台的重要工具,为群体用户提供兼顾个性化和整体满意度的产品和服务.现有群推荐方法大多是对个性化推荐方法的集成和聚合,忽略了群体和用户的交互影响以及群偏好和成员偏好的动态变化,从而无法保障群推荐系统的效果.为此,本文提出一种基于群偏好和用户偏好协同演化的群推荐方法,能够建模群体和用户的动态交互.具体而言,本文将用户偏好建模成其历史偏好和群影响的加权聚合结果,将群偏好建模成群历史偏好和新加入成员偏好的加权聚合结果,最终预测群体可能消费的产品列表和成员可能加入的群体列表.实验结果表明,本文所提模型在群体消费行为和用户加群行为的预测表现都优于基准算法,并兼具很好的鲁棒性.

Group recommendation method based on co-evolution of group preference and user preference

The group recommender system has become an important tool of social platforms to provide personalized and satisfied products or services for groups.However,existing methods of group recommendation mainly focus on improving the personalized recommendation methods,not only ignoring the interaction of users and groups,but also neglecting the dynamics of user preferences and group preferences.These interaction process and dynamic evolution are essential to group recommendation.Therefore,this paper proposes a dynamic group recommendation method based on the co-evolution of user preferences and group preferences to model the dynamic interaction between users and groups.Specifically,we model the user preferences as a weighted aggregation of user historical preferences and group influence,and model the group preferences as a weighted combination of group historical preferences and new members'preferences.Finally,we aim to predict users'joining behaviors and group consumption behaviors.We also carry out extensive experiments using real data to evaluate the effectiveness of our model.The experimental results show that the proposed model not only achieve better performances on predicting both joining and consumption behaviors,but also is robustness.