Top-N news recommendations in digital newspapers

News recommendation is a very active research field. The number of online journals has increased in recent years owing to the increasing popularity of the Internet. In this context, it is important to offer user tools that facilitate faster and more accurate access to articles of interest in digital newspapers. We present two probabilistic models based on latent variables that recommend relevant news to users according to profiles of their visits to the newspaper website. As input, the models consider news content and categories, according to a predefined classification, of those news previously accessed. The experimental results show good performance with respect to baseline models in a data set of news extracted from a digital journal edition.

[1]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[2]  Gloria Bordogna,et al.  An Incremental Hierarchical Fuzzy Clustering Algorithm Supporting News Filtering , 2006 .

[3]  Susan T. Dumais,et al.  Newsjunkie: providing personalized newsfeeds via analysis of information novelty , 2004, WWW '04.

[4]  Antal van den Bosch,et al.  Comparing and evaluating information retrieval algorithms for news recommendation , 2007, RecSys '07.

[5]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

[6]  Mark Claypool,et al.  Combining Content-Based and Collaborative Filters in an Online Newspaper , 1999, SIGIR 1999.

[7]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[8]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[9]  Korris Fu-Lai Chung,et al.  An empirical study of a cross-level association rule mining approach to cold-start recommendations , 2008, Knowl. Based Syst..

[10]  Ahmad M. Ahmad Wasfi.  Collecting user access patterns for building user profiles and collaborative filtering , 1998, IUI '99.

[11]  Jesús Bobadilla,et al.  A new collaborative filtering metric that improves the behavior of recommender systems , 2010, Knowl. Based Syst..

[12]  Jiahui Liu,et al.  Personalized news recommendation based on click behavior , 2010, IUI '10.

[13]  Michael J. Pazzani,et al.  User Modeling for Adaptive News Access , 2000, User Modeling and User-Adapted Interaction.

[14]  Ken Lang,et al.  NewsWeeder: Learning to Filter Netnews , 1995, ICML.

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

[16]  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.

[17]  Masataka Goto,et al.  An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[18]  Thomas Hofmann,et al.  Latent Class Models for Collaborative Filtering , 1999, IJCAI.

[19]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.

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

[21]  Thomas Hofmann,et al.  Learning What People (Don't) Want , 2001, ECML.

[22]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[23]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[24]  Tomonari Kamba,et al.  ANATAGONOMY: a personalized newspaper on the World Wide Web , 1997, Int. J. Hum. Comput. Stud..

[25]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[26]  Alejandro Bellogín,et al.  News@hand: A Semantic Web Approach to Recommending News , 2008, AH.

[27]  John Riedl,et al.  An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms , 2002, Information Retrieval.

[28]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[29]  V. Rao Vemuri,et al.  Information filtering via hill climbing, wordnet, and index patterns , 1997, Inf. Process. Manag..

[30]  Bradley N. Miller,et al.  Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system , 1998, CSCW '98.

[31]  Antonio Hernando,et al.  Collaborative filtering adapted to recommender systems of e-learning , 2009, Knowl. Based Syst..

[32]  Enrique Herrera-Viedma,et al.  Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries , 2010, Knowl. Based Syst..

[33]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[34]  Ting-Peng Liang,et al.  Discovering user interests from Web browsing behavior: an application to Internet news services , 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences.

[35]  David M. Pennock,et al.  Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments , 2001, UAI.

[36]  Sung Joo Park,et al.  MONERS: A news recommender for the mobile web , 2007, Expert Syst. Appl..

[37]  Sung Jin Hur,et al.  Improved trust-aware recommender system using small-worldness of trust networks , 2010, Knowl. Based Syst..

[38]  Yoav Shoham,et al.  Content-Based, Collaborative Recommendation By combining both collaborative and content-based filtering systems, Fab may eliminate many of the weaknesses found in each approach. , 1997 .