A Survey on Recommender Systems for News Data

  • Hugo L. Borges
  • Ana C. Lorena
Part of the Studies in Computational Intelligence book series (SCI, volume 260)


The advent of online newspapers broadened the diversity of available news’ sources. As the volume of news grows, so does the need for tools which act as filters, delivering only information that can be considered relevant to the reader. Recommender systems can be used in the organization of news, easing reading and navigation through newspapers. Employing the users’ history on items consumption, user profiles or other source of knowledge, these systems can personalize the user experience, reducing the information overload we currently face. This chapter presents these recommender filters, explaining their particularities and applications in the news’ domain.


News data recommender systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tscher, M.J.A.: The BigChaos Solution to the Netflix Prize (2008)Google Scholar
  2. 2.
    Tscher, R.L.A., Jahrer, M.: Improved neighborhood-based algorithms for large-scale recommender systems. In: SIGKDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, ACM Press, New York (2008)Google Scholar
  3. 3.
    Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 734–749 (2005)Google Scholar
  4. 4.
    Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  5. 5.
    Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: two sides of the same coin?. Communications of the ACM 35(12), 29–38 (1992)CrossRefGoogle Scholar
  6. 6.
    Bennet, J.: The cinematch system: Operation, scale, coverage, accuracy impact (2006)Google Scholar
  7. 7.
    Billsus, D., Pazzani, M.: A personal news agent that talks, learns and explains. In: Proceedings of the third annual conference on Autonomous Agents, pp. 268–275. ACM, New York (1999)CrossRefGoogle Scholar
  8. 8.
    Billsus, D., Pazzani, M.J.: User Modeling for Adaptive News Access. User Modeling and User-Adapted Interaction 10(2), 147–180 (2000)CrossRefGoogle Scholar
  9. 9.
    Bogers, T., van den Bosch, A.: Comparing and evaluating information retrieval algorithms for news recommendation. In: Proceedings of the 2007 ACM conference on Recommender systems, pp. 141–144. ACM Press, New York (2007)CrossRefGoogle Scholar
  10. 10.
    Burke, R.: Knowledge-based recommender systems. Encyclopedia of Library and Information Systems 69(suppl. 32), 175–186 (2000)Google Scholar
  11. 11.
    Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)zbMATHCrossRefGoogle Scholar
  12. 12.
    Candillier, L., Meyer, F., Fessant, F.: Designing specific weighted similarity measures to improve collaborative filtering systems. In: Perner, P. (ed.) ICDM 2008. LNCS (LNAI), vol. 5077, pp. 242–255. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Candillier, L., Meyer, F., Fessant, F.: Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling. In: Candillier, L., Meyer, F., Fessant, F. (eds.) State-of-the-Art Recomender Systems, ch. 1, pp. 1–22. Idea Group Inc., IGI (February 2009)Google Scholar
  14. 14.
    Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: ACM SIGIR Workshop on Recommender Systems (1999)Google Scholar
  15. 15.
    Cosley, D., Lam, S., Albert, I., Konstan, J., Riedl, J.: Is seeing believing?: how recommender system interfaces affect users’ opinions. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 585–592. ACM, New York (2003)Google Scholar
  16. 16.
    Cotter, P., Smyth, B.: Ptv: Intelligent personalised tv guides. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, pp. 957–964. AAAI Press/The MIT Press (2000)Google Scholar
  17. 17.
    Das, A., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th international conference on World Wide Web, pp. 271–280. ACM Press, New York (2007)CrossRefGoogle Scholar
  18. 18.
    Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22(1), 143–177 (2004)CrossRefGoogle Scholar
  19. 19.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval 4(2), 133–151 (2001)zbMATHCrossRefGoogle Scholar
  20. 20.
    Good, N., Schafer, J., Konstan, J., Borchers, A., Sarwar, B., Herlocker, J., Riedl, J.: Combining Collaborative Filtering with Personal Agents for Better Recommendations. In: Procedings of The National Conference on Aritificial Inteligence, pp. 439–446. John Wiley & Sons Ltd, Chichester (1999)Google Scholar
  21. 21.
    Herlocker, J., Konstan, J.A., Riedl, J.: An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms. Information Retrieval 5(4), 287–310 (2002)CrossRefGoogle Scholar
  22. 22.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  23. 23.
    Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 194–201. ACM Press/Addison-Wesley Publishing Co., New York (1995)Google Scholar
  24. 24.
    Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR 1999: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 50–57. ACM Press, New York (1999)CrossRefGoogle Scholar
  25. 25.
    Hofmann, T.: Latent Semantic Models for Collaborative Filtering. ACM Transactions on Information Systems 22(1), 89–115 (2004)CrossRefGoogle Scholar
  26. 26.
    Ken Goldberg, E.B.: Tavi Nathanson. Jester 4.0Google Scholar
  27. 27.
    Kim, T.H., Yang, S.B.: Using Attributes to Improve Prediction Quality in Collaborative Filtering. LNCS, 1–10 (2004)Google Scholar
  28. 28.
    Lang, K.: Newsweeder: Learning to filter netnews. In: Proceedings of the 12th International Machine Learning Conference (ML 1995), pp. 331–339 (1995)Google Scholar
  29. 29.
    Malone, T.W., Grant, K.R., Turbak, F.A., Brobst, S.A., Cohen, M.D.: Intelligent information-sharing systems. Communications of the ACM 30(5), 390–402 (1987)CrossRefGoogle Scholar
  30. 30.
    Mccallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: AAAI 1998 Workshop on Learning for Text Categorization, pp. 41–48. AAAI Press, Menlo Park (1998)Google Scholar
  31. 31.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering. In: Proceedings of the SIGIR-2001 Workshop on Recommender Systems, vol. 9, pp. 187–192 (2001)Google Scholar
  32. 32.
    Mitchel, T.M.: Machine Learning. McGraw-Hill, New York (1997)Google Scholar
  33. 33.
    Montaner, M., Lopez, B., de la Rosa, J.: Opinion-Based Filtering through Trust. LNCS, pp. 164–178. Springer, Heidelberg (2002)Google Scholar
  34. 34.
    Montaner, M., López, B., de la Rosa, J.: A Taxonomy of Recommender Agents on the Internet. Artificial Intelligence Review 19(4), 285–330 (2003)CrossRefGoogle Scholar
  35. 35.
    Movielens data setsGoogle Scholar
  36. 36.
    Nikos Manouselis, C.C.: Personalized Information Retrieval and Access. In: Overview of Design Options for Neighbourhood-Based Collaborative Filtering Systems, ch. 2, pp. 30–54. Idea Group Inc., IGI (2008)Google Scholar
  37. 37.
    Ponte, J., Croft, W.: A language modeling approach to information retrieval. In: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 275–281. ACM, New York (1998)CrossRefGoogle Scholar
  38. 38.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: CSCW 1994: Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186. ACM Press, New York (1994)CrossRefGoogle Scholar
  39. 39.
    Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978)zbMATHCrossRefGoogle Scholar
  40. 40.
    Robertson, S., Walker, S.: Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In: Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 232–241. Springer, New York (1994)Google Scholar
  41. 41.
    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  42. 42.
    Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth”. In: CHI 1995: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 210–217. ACM Press/Addison-Wesley Publishing Co, New York (1995)Google Scholar
  43. 43.
    Wasfi, A.: Collecting user access patterns for building user profiles and collaborative filtering. In: Proceedings of the 4th international conference on Intelligent user interfaces, pp. 57–64. ACM Press, New York (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hugo L. Borges
    • 1
  • Ana C. Lorena
    • 1
  1. 1.Centro de Matemática, Computação e CogniçãoUniversidade Federal do ABCSanto AndréBrazil

Personalised recommendations