Content-Based News Recommendation

The information overloading is one of the serious problems nowadays. We can see it in various domains including business. Especially news represent area where information overload currently prevents effective information gathering on daily basis. This is more significant in connection to the web and news web-based portals, where the quality of the news portal is commonly measured mainly by the amount of news added to the site. Then the most renowned news portals add hundreds of new articles daily. The classical solution usually used to solve the information overload is a recommendation, especially personalized recommendation. In this paper we present an approach for fast content-based news recommendation based on cosine-similarity search and effective representation of the news. We experimented with proposed method in an environment of largest electronic Slovakia newspaper and present results of the experiments.

[1]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[3]  Pavol Návrat,et al.  Full Text Search Engine as Scalable k-Nearest Neighbor Recommendation System , 2010, IFIP AI.

[4]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[5]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[6]  James L. Peterson,et al.  Computer-based readability indexes , 1982, ACM '82.

[7]  Jignesh M. Patel,et al.  Estimating the selectivity of tf-idf based cosine similarity predicates , 2007, SGMD.

[8]  Christos Bouras,et al.  Personalization Mechanism for Delivering News Articles on the User's Desktop , 2009, 2009 Fourth International Conference on Internet and Web Applications and Services.

[9]  Bamshad Mobasher,et al.  Intelligent Techniques for Web Personalization , 2005, Lecture Notes in Computer Science.

[10]  Hiroshi Mamitsuka,et al.  PURE: a PubMed article recommendation system based on content-based filtering. , 2007, Genome informatics. International Conference on Genome Informatics.

[11]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[12]  P. Brusilovsky,et al.  NewsMe: A Case Study for Adaptive News Systems with Open User Model , 2007, Third International Conference on Autonomic and Autonomous Systems (ICAS'07).

[13]  Luis Gravano,et al.  Efficient summarization-aware search for online news articles , 2007, JCDL '07.

[14]  Arbee L. P. Chen,et al.  Enabling personalized recommendation on the Web based on user interests and behaviors , 2001, Proceedings Eleventh International Workshop on Research Issues in Data Engineering. Document Management for Data Intensive Business and Scientific Applications. RIDE 2001.

[15]  Bamshad Mobasher,et al.  Intelligent Techniques for Web Personalization: IJCAI 2003 Workshop, ITWP 2003, Acapulco, Mexico, August 11, 2003, Revised Selected Papers , 2005 .

[16]  Juan Enrique Ramos,et al.  Using TF-IDF to Determine Word Relevance in Document Queries , 2003 .

[17]  P. Schönemann.  On artificial intelligence , 1985, Behavioral and Brain Sciences.

[18]  Chih-Ming Chen,et al.  Intelligent Location-Based Mobile News Service System with Automatic News Summarization , 2009, 2009 International Conference on Environmental Science and Information Application Technology.

[19]  R. D. Santos,et al.  News Recommendation , 2012 .

[20]  Georges Gardarin,et al.  Keywords Extraction, Document Similarity and Categorization , 1990 .