基于多社交数据源的协同推荐方法研究

Research on Collaborative Recommendation Method Based on Multiple Data Sources of Social Network

协同过滤推荐作为一种有效的推荐方法,普遍存在数据稀疏性和冷启动问题,利用社交网络的多项数据源对协同推荐方法进行了改进。为了克服评分矩阵的稀疏性问题,提出结合用户评分相似度和用户信任度选择推荐邻居,同时对用户相似度计算进行了改进;提出了一种简单有效的信任推理方法,能够识别出用户间隐含的间接信任关系,进一步缓解了数据稀疏性问题;为了解决推荐系统的冷启动问题,提出综合利用项目的类型属性信息和领域专家信息进行联合推荐。实验结果表明,提出的改进策略非常有效,在精度和召回率方面都较已有方法具有明显改善。

As an effective recommendation method, collaborative filtering typically has the data sparsity and cold-start problems. It was proposed that using multiple data sources of social network to overcome the above problems. First of all, both the rating similarity and the social trust between users were considered to resolve the data sparsity problem. Then a simple and effective trust reasoning method was proposed to identify the implicit trust relationship between users. In order to solve the cold-start problem, information of the category of items and domain experts was used for joint recommendation. Experimental results show that the proposed algorithm has significantly better precision and recall than existing methods.