顾及事件地理位置的新闻推荐方法研究

摘要/Abstract

摘要： 为研究新闻事件发生地对新闻推荐系统性能的影响,提出了一种顾及事件地理位置的新闻推荐算法。首先,设计了提取新闻事件发生地的相关算法;其次,结合向量空间模型、TF-IDF算法和word2vec工具构建了新闻特征向量;接着,着重讨论了用户兴趣模型的构建问题;最后,运用余弦相似度方法计算用户兴趣模型与候选新闻集之间的相似性,从而完成推荐。实验结果表明,设计的新闻事件发生地抽取算法的性能较好,准确率达到93.6%,以此为基础构建的新闻推荐算法与协同过滤推荐算法相比仅考虑新闻内容的推荐算法在F值上有所提高。

Abstract: In order to research the impact of news event place on the recommendation performance of news recommendation system,a News recommendation algorithm Considering Geographical Position(NCGP) method is proposed.Firstly,an algorithm was designed to extract the place of news event.Secondly,the vector space model,TF-IDF algorithm and word2vec tool were used to construct the news feature vector.Then,constructing the user interest model was discussed deeply.Finally,the cosine similarity method was used to calculate the similarity between the user interest model and the candidate news set to complete the recommendation.The experimental results show that the performance of the proposed news event place extraction algorithm is better,and the precision can reach 93.6%,besides,the F-value of NCGP is improved compared with the collaborative filtering recommendation algorithm and the recommendation algorithm that only considers news content.

Key words: Geographical location, Information extraction, Recommendation system, User interest model, Vector space model

中图分类号:

TP391

袁仁进, 陈刚. 顾及事件地理位置的新闻推荐方法研究[J]. 计算机科学, 2018, 45(11A): 462-467. https://doi.org/

YUAN Ren-jin, CHEN Gang. Research on News Recommendation Methods Considering Geographical Location of News[J]. Computer Science, 2018, 45(11A): 462-467. https://doi.org/

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