集成组内标签与用户链接关系的Flickr组推荐模型

摘要

In Flickr,one of the most popular photo service websites,groups are superior for photos'propagation and they can gather photos of similar themes,which brings convenience to users' easy browsing.Therefore,many researchers are studying how to help users to find out which groups they may be interested in.In this paper,a probabilistic matrix factorization (PMF)based model is proposed for Flickr group recommendation by employing the information of users' contacts and tags in group.The complexity analysis indicates that the proposed model is efficient and it can be applied to large datasets.The experimental results on a Flickr dataset show the effectiveness of the proposed model.Finally,a Flickr group recommendation system is developed based on the proposed model.%知名图片分享网站Flickr中的组,在增加图片曝光率、方便用户浏览图片等方面发挥重要作用.因而,如何协助用户便捷地搜寻其感兴趣的组成为研究者关注的热点之一.针对此问题,文中利用Flickr中辅助用户选择组的元素,即其关注的用户链接关系及组内的内容标签信息,提出一种组推荐模型.该模型基于概率矩阵分解的方法,具有较低的复杂度.在Flickr数据集上的实验表明,该模型可为用户提供较高质量的推荐结果.最后,基于该模型设计一个Flickr组推荐系统.