基于异构网络面向多标签系统的推荐模型研究

Multi-Dimensional Tag Recommender Model via Heterogeneous Networks

标签成为信息组织的重要方式之一,随着推荐系统的蓬勃发展,标签推荐成为学者们研究的重要问题之一.目前存在各种各样的标签系统,其功能千差万别,标签数据信息越来越复杂.目前研究往往针对特定类型标签数据,缺乏既综合考虑标签数据中不同类型对象的复杂信息又能适用于多种标签系统数据的标签推荐模型.构建了标签推荐模型Hn MTR,该模型首先针对标签数据中不同类型对象构建异构网络模型,其次对异构网络模型中不同类型顶点进行同空间映射,使不同类型的顶点和边可在同一空间进行量化比较;最后基于同空间映射后网络,引入多参数马尔可夫模型进行标签评分和推荐.通过基于豆瓣、Delicious和Meetup这3个标签系统数据实验,其结果表明,Hn MTR模型平均准确率比目前主流算法提高10%以上,取得了较好的推荐结果.

Tagging has become one of the most significant methods for information organization. With the proliferation of recommending systems, tag recommendation problem has attracted more and more attention from researchers. Currently, while a variety of tagging systems exist, as the system function becomes more and more complex, the information of tagging data generated by tagging system becomes increasingly complex. In this paper, a tagging system is modeled as a heterogeneous network. To learn the importance of different types of nodes and edges, a general graph-based model, called HnMTR, is proposed. First, HnMTR maps different heterogeneous objects into a unified space so that objects from different dimensions can be directly compared. Then multivariate Markov model is applied to the mapped network to rank tag nodes. Highly ranked tags are recommended for the user. Experiments on three real world datasets with different tagging behavior demonstrate that the proposed method outperforms the state-of-the-art methods significantly.