摘要
提出一种分段组合两种推荐算法的后融合方法。首先选择两种基本算法对用户参与小组的历史信息进行挖掘,进而为用户推荐可能感兴趣的小组,然后对两种基本算法产生的推荐列表小组进行分段,接着利用所设计的分段函数组合预测概率,通过最远邻方法优化推荐小组,最后按照组合后的预测概率重新排序产生新的推荐列表。实验表明该方法提高了推荐的准确率,有利于用户更快速地定位感兴趣的小组。
Online community has become a popular social media platform,where users can share information,exchange opinions,and so on. Groups,as the core pattern of online community,have attracted a large amount of users. With the number of groups increasing,it is difficult for users to find out which group they may be interested in. Many researchers are paying attention to personalized group recommendation system to get over this problem. In this paper,we propose a novel framework for a group recommendation by integrating two different methods with a piecewise function. The framework contains four steps: dividing groups recommended by two different recommendation algorithms into three patterns,utilizing a piecewise function to compute the probability with which users will be interested in groups,filtering the groups by the furthest neighbor method,and reranking those groups. Experimental results on Douban show the effectiveness of our method by improving the accuracy of top-n recommdation. Finally,we develop a group recommendation system based on the proposed method.
引文
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