Skip to main content
SpringerLink
Log in

Personalized News Recommendation: A Review and an Experimental Investigation

  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

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 effciently 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  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.

    Article  Google Scholar 

  4. Carreira R, Crato J M, Gonçalves 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.

    Article  Google Scholar 

  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

    MATH  Google Scholar 

  11. Billsus D, Pazzani M J. A personal news agent that talks, learns and explains. In Proc. the 3 rd 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 net-news. 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.

    Article  Google Scholar 

  19. Shani G, Heckerman D, Brafman R I. An MDP-based recommender system. Journal of Machine Learning Research, 2006, 6(2): 1265.

    MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  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 peta-scale 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.

    Google Scholar 

  33. IJntema W, Goossen F, Frasincar F, Hogenboom F. Ontology-based 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.

    Article  MathSciNet  MATH  Google Scholar 

  36. Khuller S, Moss A, Naor J S. The budgeted maximum coverage problem. Information Processing Letters, 1999, 70(1): 39–45.

    Article  MathSciNet  MATH  Google Scholar 

  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.

Download references

Author information

Authors and Affiliations

  1. School of Computing and Information Sciences, Florida International University, Miami, Florida, 33199, U.S.A

    Lei Li, Ding-Ding Wang & Tao Li

  2. Department of Computer Science and Technology, Xiamen University of Technology, Xiamen, 361024, China

    Shun-Zhi Zhu

Authors
  1. Lei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ding-Ding Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shun-Zhi Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tao Li.

Additional information

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.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

(PDF 74.7 KB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, L., Wang, DD., Zhu, SZ. et al. Personalized News Recommendation: A Review and an Experimental Investigation. J. Comput. Sci. Technol. 26, 754–766 (2011). https://doi.org/10.1007/s11390-011-0175-2

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-011-0175-2

Keywords

Search

Navigation