基于物品的协同过滤推荐算法以及基于三部图的资源扩散算法存在信息利用率不高, 标签数据稀疏等问题. 为了解决这些问题, 提出改进的标签可重叠社区推荐模型, 旨在充分利用已有信息和标签为用户进行个性化推荐, 缩短用户查找资源的时间, 提高推荐质量. 以影视推荐为例, 该模型将电影相关信息转化为标签, 通过用户、电影以及标签之间的关系构建完全三部图, 引入资源扩散算法和 K-means 算法发现并划分标签的可重叠社区, 进而基于标签的可重叠社区为用户进行个性化推荐. 在 hetrec2011-movielens-2k 数据集上的实验表明, 与基于物品的协同过滤推荐算法以及基于三部图的资源扩散算法相比较, 所提出的推荐模型准确率和召回率均有提升.

The item-based collaborative filtering recommendation algorithm and the resource diffusion algorithm based on the tripartite graph have some problems, such as low information utilization rate and sparseness of tag data. In order to solve these problems, an improved community recommendation model based on overlapping tags is proposed, which aims to make full use of the existing information and tags to personalize recommendation for users, shorten the time for users to find resources, and improve the quality of recommendation. Taking movie recommendation as an example, the proposed model transforms movie related information into tags, constructs complete tripartite graph through the relationship among users, movies and tags, introduces resource diffusion algorithm and K-means algorithm to find and divide overlapping communities of tags, and then makes personalized recommendation for users based on the overlapping communities of tags. Experiments on the hetrec 2011-movies-2k dataset show that the accuracy and recall rate of the proposed recommendation model are improved compared with the item-based collaborative filtering recommendation algorithm and the resource diffusion algorithm based on tripartite graph.