基于动态标签–资源网络图的信息资源推荐

Information Resource Recommendation Method Based on Dynamic Tag-Resource Network

【目的】解决推荐系统向目标用户推荐过时信息资源的问题。【方法】提出一种基于动态标签–资源网络图的个性化信息资源推荐方法。以资源拥有的共同标签作为连边,建立资源网络图以形成资源语义链,再由资源网络图的连边投影构建具有时间属性的标签网络图以刻画用户兴趣漂移,继而在标签网络图中匹配目标用户兴趣的动态标签,实现为用户推荐精准信息资源。【结果】在数据集Movie Lens上验证本方法能够跟踪、预测用户兴趣漂移,实施资源精准推荐,且平均绝对误差(MAE)较传统方法降低近15%。【局限】诸如信息检索中用户兴趣频繁变化的实时动态环境下的推荐暂未考虑。【结论】该方法可实现为兴趣动态变化的用户推荐更为精准的信息资源。

[Objective] To solve the problem that recommender systems recommend outdated information resources to the target user. [Methods] This paper proposes an individual recommendation method for information resource based on dynamic tag resources network graph. Firstly, resource network graph is established to form resource semantic relationships, using common tags in two resource objects as a link pairwise. Secondly, tag network graph with time is created to describe users＇ interest drifting using the links in resource network graph. Thirdly, top N information resource objects are recommended to target user from tag network graph by matching target users＇ dynamic tags describing users＇ interest drifting. [Results] In MovieLens data set, the experimental results show that this information recommendation method can trace and predict users＇ interest drifting, and recommend accurate resource to users, Mean Absolute Error （MAE） is lower than the traditional methods by about 15%. [Limitations] The method does not involve the problem that information resources are recommended under real-time dynamic environment such as information retrieval with users＇ interests drifting rapidly. [Conclusions] The proposed method can recommend more accurate information resource to users with interest drifting.