We use cookies to improve your experience with our site.

Indexed in:

SCIE, EI, Scopus, INSPEC, DBLP, CSCD, etc.

Submission System
(Author / Reviewer / Editor)
Advanced Search
Volume 26 Issue 5
September  2011
Turn off MathJax
Article Contents
Abstract
References
Related Articles
Lei Li, Ding-Ding Wang, Shun-Zhi Zhu, Tao Li. Personalized News Recommendation: A Review and an Experimental Investigation[J]. Journal of Computer Science and Technology, 2011, 26(5): 754-766. DOI: 10.1007/s11390-011-0175-2
Citation: Lei Li, Ding-Ding Wang, Shun-Zhi Zhu, Tao Li. Personalized News Recommendation: A Review and an Experimental Investigation[J]. Journal of Computer Science and Technology, 2011, 26(5): 754-766. DOI: 10.1007/s11390-011-0175-2
shu

Personalized News Recommendation: A Review and an Experimental Investigation

  • 1. School of Computing and Information Sciences, Florida International University, Miami, Florida 33199, U.S.A.
    2. Department of Computer Science and Technology, Xiamen University of Technology, Xiamen 361024, China

Funds: This work is partially supported by the National Science Foundation of US under Grant Nos. IIS-0546280 and CCF-0830659, and the National Natural Science Foundation of China under Grant No. 61070151.
More Information
  • Author Bio:

    Lei Li received his M.S. degree in software engineering from Beihang University in 2008. He is currently a Ph.D. candidate in School of Computing and Information Sciences at Florida International University. His research interests include data mining and machine learning.

    Ding-Ding Wang received her Bachelor's degree from the Department of Computer Science, University of Science and Technology of China in 2003, and her Ph.D. degree in computer science in 2009 from Florida International University. She is currently a postdoctoral researcher in the Center for Computational Science at University of Miami. Her research interests are data mining and information retrieval.

    Shun-Zhi Zhu received his Ph.D. degree in control theory and engineering in 2007 from Xiamen University. He is currently an associate professor and vice chair of the Department of Computer Science & Technology at Xiamen University of Technology. His research interests are information systems, GIS, and data mining.

    Tao Li received his Ph.D. degree in computer science in 2004 from the University of Rochester. He is currently an associate professor in the School of Computer Science at Florida International University. His research interests are in data mining, machine learning and information retrieval. He is a recipient of USA NSF CAREER Award and multiple IBM Faculty Research Awards.

  • Received Date: February 20, 2011
  • Revised Date: June 13, 2011
  • Published Date: September 04, 2011
    Abstract
  • Online news articles, as a new format of press releases, have sprung up on the Internet. With its convenience and recency, more and more people prefer to read news online instead of reading the paper-format press releases. However, a gigantic amount of news events might be released at a rate of hundreds, even thousands per hour. A challenging problem is how to efficiently select specific news articles from a large corpus of newly-published press releases to recommend to individual readers, where the selected news items should match the reader's reading preference as much as possible. This issue refers to personalized news recommendation. Recently, personalized news recommendation has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Existing personalized news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information. A variety of techniques have been proposed to tackle personalized news recommendation, including content-based, collaborative filtering systems and hybrid versions of these two. In this paper, we provide a comprehensive investigation of existing personalized news recommenders. We discuss several essential issues underlying the problem of personalized news recommendation, and explore possible solutions for performance improvement. Further, we provide an empirical study on a collection of news articles obtained from various news websites, and evaluate the effect of different factors for personalized news recommendation. We hope our discussion and exploration would provide insights for researchers who are interested in personalized news recommendation.
    • FullText(HTML)
    • References (38)
  • [1]
    Liu J, Dolan P, Pedersen E R. Personalized news recommendation based on click behavior. In Proc. the 14th International Conference on Intelligent User Interfaces, Hong Kong, China, Feb. 7-10, 2010, pp.31-40.
    [2]
    Burke R. Hybrid systems for personalized recommendations. In Proc. Workshop on Intelligent Techniques for Web Personalization, Acapulco, Mexico, Aug. 11, 2005, pp.133-152.
    [3]
    Billsus D, Pazzani M J. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 2000, 10(2): 147-180.
    [4]
    Carreira R, Crato J M, Gon?calves D, Jorge J A. Evaluating adaptive user profiles for news classification. In Proc. the 9th International Conference on Intelligent User Interfaces, Funchal, Brtngal, Jan. 13-16, 2004, pp.206-212.
    [5]
    Kim H R, Chan P K. Learning implicit user interest hierarchy for context in personalization. Applied Intelligence, 2008, 28(2): 153-166.
    [6]
    Liang T P, Lai H J. Discovering user interests from web browsing behavior: An application to internet news services. In Proc. HICSS, Hawaii, USA, Jan. 7-10, 2002, pp.2718-2727.
    [7]
    Tan A H, Teo C. Learning user profiles for personalized information dissemination. In Proc. IEEE International Joint Conference on Computational Intelligence, Horolulu, USA, May 12-17, 2002, pp.183-188.
    [8]
    Jurafsky D, Martin J H, Kehler A, Vander Linden K, Ward N. Speech and Language Processing. Prentice Hall, 2000.
    [9]
    Hofmann T. Probabilistic latent semantic indexing. In Proc. the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, USA, Aug. 15-19, 1999, pp.50-57.
    [10]
    Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. The Journal of Machine Learning Research, 2003, 3: 993- 1022.
    [11]
    Billsus D, Pazzani M J. A personal news agent that talks, learns and explains. In Proc. the 3rd Annual Conference on Autonomous Agents, Seattle, USA, May 1-5, 1999, pp.268-275.
    [12]
    Ahn J, Brusilovsky P, Grady J, He D, Syn S Y. Open user profiles for adaptive news systems: Help or harm? In Proc. the 16th International Conference on World Wide Web, Banff, Canada, May 8-12, 2007, pp.11-20.
    [13]
    Das A S, Datar M, Garg A, Rajaram S. Google news personalization: Scalable online collaborative filtering. In Proc. the 16th International Conference on World Wide Web, Banff, Canada, May 8-12, 2007, pp.271-280.
    [14]
    Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. GroupLens: An open architecture for collaborative filtering of netnews. In Proc. the 1994 ACM Conference on Computer Supported Cooperative Work, Chapel Hill, USA, Oct. 22-26, 1994 pp.175-186.
    [15]
    Sarwar B, Karypis G, Konstan J, Reidl J. Item-based collaborative filtering recommendation algorithms. In Proc. the 10th International Conference on World Wide Web, Hong Kong, China, May 1-5, 2001, pp.285-295.
    [16]
    Yu K, Xu X, Tao J, Ester M, Kriegel H P. Instance selection techniques for memory-based collaborative filtering. In Proc. the 2nd SIAM International Conference on Data Mining, Arlington, USA, Apr. 11-13, 2002, pp.59-74.
    [17]
    Breese J S, Heckerman D, Kadie C et al. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. the 14th Conference on Uncertainty in Artificial Intelligence, Madison, USA, Jul. 24-26, 1998, pp.43-52.
    [18]
    Hofmann T. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 2004, 22(1): 89-115.
    [19]
    Shani G, Heckerman D, Brafman R I. An MDP-based recommender system. Journal of Machine Learning Research, 2006, 6(2): 1265.
    [20]
    Schafer J B, Konstan J, Riedi J. Recommender systems in e-commerce. In Proc. the 1st ACM Conference on Electronic Commerce, Denver, USA, Nov. 3-5, 1999, pp.158-166.
    [21]
    Li L, Chu W, Langford J, Schapire R E. A contextual-bandit approach to personalized news article recommendation. In Proc. the 19th International Conference on World Wide Web, Raleigh, USA, Apr. 26-30, 2010, pp.661-670.
    [22]
    Schein A I, Popescul A, Ungar L H, Pennock D M. Methods and metrics for cold-start recommendations. In Proc. the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tenpere, Finland, Aug. 11-15, 2002, pp.253-260.
    [23]
    Chu W, Park S T. Personalized recommendation on dynamic content using predictive bilinear models. In Proc. the 18th International Conference on World Wide Web, Madrid, Spain, Apr. 20-24, 2009, pp.691-700.
    [24]
    Li L, Wang D, Li T, Knox D, Padmanabhan B. SCENE: A scalable two-stage personalized news recommendation system. In Proc. the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, China, Jul. 25-29, 2011, pp.124-134.
    [25]
    Gionis A, Indyk P, Motwani R. Similarity search in high dimensions via hashing. In Proc. the 25th International Conference on Very Large Data Bases, Edinberg, UK, Sept. 7-10, 1999, pp.518-529.
    [26]
    Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 2008, 51(1): 107-113.
    [27]
    Chu C T, Kim S K, Lin Y A, Yu Y Y, Bradski G, Ng A Y, Olukotun K. Map-reduce for machine learning on multicore. In Proc. the 2006 Conference on Neural Information Processing Systems, Vancouver, Canada, Dec. 4-7, 2006, pp.281-288.
    [28]
    Kang U, Tsourakakis C E, Faloutsos C. PEGASUS: A petascale graph mining system implementation and observations. In Proc. the 9th IEEE International Conference on Data Mining, Miami, USA, Dec. 6-9, 2009, pp.229-238.
    [29]
    Papadimitriou S, Sun J. Disco: Distributed co-clustering with map-reduce: A case study towards petabyte-scale end-to-end mining. In Proc. the 8th IEEE International Conference on Data Mining, Pisa, Italy, Dec. 15-19, 2008, pp.512-521.
    [30]
    Wang D, Zhu S, Li T, Gong Y. Comparative document summarization via discriminative sentence selection. In Proc. the 18th ACM Conference on Information and Knowledge Management, Hong Kong, China, Nov. 2-6, 2009, pp.1963-1966.
    [31]
    Gauch S, Speretta M, Chandramouli A, Micarelli A. User Profiles for Personalized Information Access. The Adaptive Web, 2007, pp.54-89.
    [32]
    Tan P N, Steinbach M, Kumar V et al. Introduction to Data Mining. Boston: Pearson Addison Wesley, 2006.
    [33]
    IJntema W, Goossen F, Frasincar F, Hogenboom F. Ontologybased news recommendation. In Proc. the 2010 EDBT Workshops, Laussane, Switzerland, Mar. 22-26, 2010, pp.1-6.
    [34]
    Cunningham D H, Maynard D D, Bontcheva D K, Tablan M V. GATE: A framework and graphical development environment for robust NLP tools and applications. In Proc. the 40th Anniversary Meeting of the Association for Computational Linguistics, Philadelphia, USA, Jul. 6-12, 2002, pp.168-175.
    [35]
    Nemhauser G L, Wolsey L A, Fisher M L. An analysis of approximations for maximizing submodular set functions. Mathematical Programming, 1978, 14(1): 265-294.
    [36]
    Khuller S, Moss A, Naor J S. The budgeted maximum coverage problem. Information Processing Letters, 1999, 70(1): 39-45.
    [37]
    Girolami M, Kabán A. On an equivalence between PLSI and LDA. In Proc. the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, Jul. 28-Aug. 1, 2003, pp.433-434.
    [38]
    Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM Trans. Intelligent Systems and Technology, 2001, 2(3): Article No.27.
    • Relative Articles

  • Related Articles

    [1]Bo Chen, Liang Liu, Hua-Dong Ma. CDM: Content Diffusion Model for Information-Centric Networks[J]. Journal of Computer Science and Technology, 2021, 36(6): 1431-1451. DOI: 10.1007/s11390-021-0205-7
    [2]Su-Ke Li, Zhi Guan, Li-Yong Tang, Zhong Chen. Exploiting Consumer Reviews for Product Feature Ranking[J]. Journal of Computer Science and Technology, 2012, 27(3): 635-649. DOI: 10.1007/s11390-012-1250-z
    [3]Dian-Xiang Xu, Omar El-Ariss, Wei-Feng Xu, Lin-Zhang Wang. Aspect-Oriented Modeling and Verification with Finite State Machines[J]. Journal of Computer Science and Technology, 2009, 24(5): 949-961.
    [4]Xian-He Sun, Surendra Byna, Yong Chen. Server-Based Data Push Architecture for Multi-Processor Environments[J]. Journal of Computer Science and Technology, 2007, 22(5): 641-652.
    [5]Ai-Bo Song, Mao-Xian Zhao, Zuo-Peng Liang, Yi-Sheng Dong, Jun-Zhou Luo. Discovering User Profiles for Web Personalized Recommendation[J]. Journal of Computer Science and Technology, 2004, 19(3).
    [6]SAI Ying, Y. Y. Yao. Analyzing and Mining Ordered Information Tables[J]. Journal of Computer Science and Technology, 2003, 18(6).
    [7]GAO Shuming, WAN Huagen, PENG Qunsheng. Constraint-Based Virtual Solid Modeling[J]. Journal of Computer Science and Technology, 2000, 15(1): 56-63.
    [8]Song Jun, Gu Guanqun. Modeling Distributed Multimedia Synchronization with DSPN[J]. Journal of Computer Science and Technology, 1998, 13(5): 448-454.
    [9]Hu Mou. A Multiple-Valued Algebra for Modeling MOS VLSI Circuits at Switch-Level[J]. Journal of Computer Science and Technology, 1992, 7(2): 175-184.
    [10]Jiao Guofang, Liu Shenquan. Information Model for Product Modeling[J]. Journal of Computer Science and Technology, 1992, 7(1): 47-55.
    • Supplements (0)

Catalog

    Get Citation
    PDF
    XML
    Article views (104) PDF downloads (3052) Cited by()
    Related

    Copyright ©2025 JCST, All Rights Reserved     京ICP备05002829号-1

    Institute of Computing Technology, Chinese Academy of Sciences
    P.O. Box 2704, Beijing 100190, P.R. China

    Tel.: 86-10-62610746 E-mail: jcst@ict.ac.cn

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return