Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 462-467.

• Big Data & Data Mining • Previous Articles     Next Articles

Research on News Recommendation Methods Considering Geographical Location of News

YUAN Ren-jin, CHEN Gang   

  1. Institute of Geospatial Information,Information Engineering University,Zhengzhou 450052,China
  • Online:2019-02-26 Published:2019-02-26

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

CLC Number: 

  • TP391
[1]RESNICK P,IACOVOU N,SUCHAK M,et al.GroupLens:an open architecture for collaborative filtering of netnews[C]∥ACM Conference on Computer Supported Cooperative Work.ACM,1994:175-186.
[2]DAS A S,DATAR M,GARG A,et al.Google news personalization:scalable online collaborative filtering[C]∥International Conference on World Wide Web.ACM,2007:271-280.
[3]BILLSUS D,PAZZANI M J,CHEN J.A learning agent for wireless news access[C]∥Proceedings of the 5th International Conference on Intelligent user Interfaces.2000:33-36.
[4]IVÁN C,CASTELLS P.Ontology-Based Personalised and Context-Aware Recommendations of News Items[C]∥Ieee/wic/acm International Conference on Web Intelligence and Intelligent Agent Technology.IEEE Computer Society,2008:562-565.
[5]IJNTEMA W,GOOSSEN F,FRASINCAR F,et al.Ontology-based news recommendation[C]∥Edbt/icdt Workshops.ACM,2010:16.
[6]CANTADOR I,BELLOGÍN A,CASTELLS P.A multilayer ontology-based hybrid recommendation model[J].Ai Communications,2008,21(2-3):203-210.
[10]SON J W,KIM A Y,PARK S B.A location-based news article recommendation with explicit localized semantic analysis[C]∥International ACM SIGIR Conference on Reserach and Development in Information Retrieval.ACM,2013:293-302.
[11]YOON H G,SONG H J,PARK S B,et al.A personalized news recommendation using user location and news contents[J].Applied Mathematics & Information Sciences,2015,9(2):439-449.
[18]STEINBACH M,KARYPIS G,KUMAR V.A comparison of document clustering techniques[C]∥World Text Mining Conference.2000.
[19]KRISHNA B S V,PROFESSOR S,ENGINEERING M C O,et al.Comparative study of K-means and Bisecting k-means techniques in wordnet based on document clustering[J].Human Movement,2012,13(2):127-131.
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