融合社交关系和标签信息的混合新闻推荐算法

针对传统新闻推荐的数据稀疏性和用户的兴趣爱好快速变化问题,提出了一种融合社交关系和标签信息的混合新闻推荐算法。首先,该算法充分利用社交网络中的社交关系和标签信息;然后使用概率主题模型(latent Dirichlet allocation,LDA)对用户兴趣进行建模;最后采用基于内容与协同过滤相结合的混合推荐算法来完成新闻推荐。实验结果表明,所提算法与已有的推荐算法相比较,在精确度上提升了10.7%、平均倒数排名上(mean reciprocal rank,MRR)提升了4.1%,在归一化折损累计增益(normalized discounted cumulative gain,NDCG)上提升了10%。该算法可在一定程度上提高新闻推荐算法的精度及推荐质量。

Hybrid news recommendation algorithm combining social relation and tag information

Concerning the problem that data sparsity and user preferences quickly change for traditional news recommendation,this paper proposed a hybrid news recommendation algorithm combining social relations and tag information.Firstly,the algorithm utilized the social relationship and hashtag information in the user′s social network.Then it applied the LDA topic mo-del to model user interest.Finally,the algorithm used a hybrid recommendation algorithm based on content and collaborative filtering to complete news recommendations.In the experiments,comparing with existing recommendation algorithms,the proposed algorithm can improve the precision by 10.7%,MRR by 4.1%,NDCG by 10%.The proposed algorithm can improve the accuracy and the quality of news recommendation algorithm effectively.