一种改进的偏好融合组推荐方法

近年来，组推荐系统已经逐渐成为推荐系统领域的研究热点之一.在电影电视和旅游推荐中，用户常常是参与活动的一组人，这就需要为多个用户形成的群组进行推荐.作为解决群组推荐问题的有效手段，组推荐系统将单个用户推荐扩展为群组推荐，目前已经应用在新闻、音乐、电影、餐饮等诸多领域.现有的组推荐融合方法主要是模型融合与推荐融合，其效用好坏目前仍没有定论，并且它们各有自己的优缺点.模型融合存在着群组成员间的公平性问题，推荐融合忽视了群组成员间的交互.提出一种改进的偏好融合组推荐方法，它结合了两种融合方法的优点.同时根据实验得出了"群组偏好与个人偏好具有相似性"的结论，并将它结合在改进方法中.最后，通过在Movielens数据集上的实验分析，验证了该方法的有效性，证明了它能够有效地提高推荐准确率.

Enhanced Group Recommendation Method Based on Preference Aggregation

Group recommender systems have recently become one of the most prevalent topics in recommender systems. As an effective solution to the problem of group recommendation, Group recommender systems have been utilized in news, music, movies, food, and so forth through extending individual recommendation to group recommendation. The existing group recommender systems usually employ aggregating preference strategy or aggregating recommendation strategy, but the effectiveness of both two methods is not well solved yet, and they respectively have their own advantages and disadvantages. Aggregating preference strategy possesses a fairness problem between group members, whereas aggregating recommendation strategy pays less attention to the interaction between group members. This paper proposes an enhanced group recommendation method based on preference aggregation, incorporating simultaneously the advantages of the aforesaid two aggregation methods. Further, the paper demonstrates that group preference and personal preference are similar, which is also considered in the proposed method. Experimental results show that the proposed method outperforms baselines in terms of effectiveness based on Movielens dataset.