An Effective News Recommendation Method for Microblog User

Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. Traditional systems strive to satisfy their user by tracing users' reading history and choosing the proper candidate news articles to recommend. However, most of news websites hardly require any user to register before reading news. Besides, the latent relations between news and microblog, the popularity of particular news, and the news organization are not addressed or solved efficiently in previous approaches. In order to solve these issues, we propose an effective personalized news recommendation method based on microblog user profile building and sub class popularity prediction, in which we propose a news organization method using hybrid classification and clustering, implement a sub class popularity prediction method, and construct user profile according to our actual situation. We had designed several experiments compared to the state-of-the-art approaches on a real world dataset, and the experimental results demonstrate that our system significantly improves the accuracy and diversity in mass text data.

[1]  Zhihua Wei,et al.  Feature Selection on Chinese Text Classification Using Character N-Grams , 2008, RSKT.

[2]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[3]  Marcos André Gonçalves,et al.  An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations , 2010, J. Assoc. Inf. Sci. Technol..

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

[5]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[6]  Fuchun Sun,et al.  An Effective Dimension Reduction Approach to Chinese Document Classification Using Genetic Algorithm , 2009, ISNN.

[7]  Samir Khuller,et al.  The Budgeted Maximum Coverage Problem , 1999, Inf. Process. Lett..

[8]  Qi Gao,et al.  Analyzing user modeling on twitter for personalized news recommendations , 2011, UMAP'11.

[9]  Barry Smyth,et al.  Using twitter to recommend real-time topical news , 2009, RecSys '09.

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

[11]  Abdelwadood Mesleh,et al.  Chi Square Feature Extraction Based Svms Arabic Language Text Categorization System , 2007 .

[12]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[13]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

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

[15]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[16]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[17]  Alessandro Micarelli,et al.  User Profiles for Personalized Information Access , 2007, The Adaptive Web.

[18]  Kalina Bontcheva,et al.  GATE: an Architecture for Development of Robust HLT applications , 2002, ACL.

[19]  Xiangliang Zhang,et al.  K-AP: Generating Specified K Clusters by Efficient Affinity Propagation , 2010, 2010 IEEE International Conference on Data Mining.

[20]  Wei Chu,et al.  A contextual-bandit approach to personalized news article recommendation , 2010, WWW '10.

[21]  James D. Hamilton.  Time Series Analysis , 1994 .

[22]  Fei Wang,et al.  Feature Extraction by Maximizing the Average Neighborhood Margin , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Michael J. Pazzani,et al.  A personal news agent that talks, learns and explains , 1999, AGENTS '99.

[24]  Gary Geunbae Lee,et al.  Information gain and divergence-based feature selection for machine learning-based text categorization , 2006, Inf. Process. Manag..

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

[26]  Gang Wang,et al.  Feature selection with conditional mutual information maximin in text categorization , 2004, CIKM '04.

[27]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[28]  A. M. Madni,et al.  Recommender systems in e-commerce , 2014, 2014 World Automation Congress (WAC).

[29]  Balaji Padmanabhan,et al.  SCENE: a scalable two-stage personalized news recommendation system , 2011, SIGIR.

[30]  Chung-Hsien Wu,et al.  Meaningful term extraction and discriminative term selection in text categorization via unknown-word methodology , 2002, TALIP.

[31]  Wei Chu,et al.  Personalized recommendation on dynamic content using predictive bilinear models , 2009, WWW '09.

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

[33]  Best Clive,et al.  EMM, Europe Media Monitor. , 2003 .

[34]  Aristides Gionis,et al.  From chatter to headlines: harnessing the real-time web for personalized news recommendation , 2012, WSDM '12.

[35]  Songbo Tan,et al.  Using DragPushing to Refine Concept Index for Text Categorization , 2006, Journal of Computer Science and Technology.