Skip to main content
SpringerLink
Log in
Book cover

The Adaptive Web pp 325–341Cite as

Content-Based Recommendation Systems

  • Chapter

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4321))

Abstract

This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user’s interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to re commend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ali, K., van Stam, W.: TiVo: Making Show Recommendations Using a Distributed Collaborative Filtering Architecture. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, pp. 394–401. ACM Press, New York (2004)

    Chapter  Google Scholar 

  2. Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yang, Y.: Topic Detection and Tracking Pilot Study Final Report. In: Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, pp. 194–218 (1998)

    Google Scholar 

  3. Balabanovic, M., Shoham, Y.: FAB: Content-based, Collaborative Recommendation. Communications of the Association for Computing Machinery 40(3), 66–72 (1997)

    Google Scholar 

  4. Basu, C., Hirsh, H., Cohen, W.: Recommendation as Classification: Using Social and Content-Based Information in Recommendation. In: Proceedings of the 15th National Conference on Artificial Intelligence, Madison, Wisconsin, pp. 714–720 (1998)

    Google Scholar 

  5. Belkin, N., Croft, B.: Information Filtering and Information Retrieval: Two Sides of the Same Coin? Communications of the ACM 35(12), 29–38 (1992)

    Article  Google Scholar 

  6. Billsus, D., Pazzani, M.: Learning Collaborative Information Filters. In: Proceedings of the International Conference on Machine Learning, Madison, WI, pp. 46–54. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  7. Billsus, D., Pazzani, M., Chen, J.: A Learning Agent for Wireless News Access. In: Proceedings of the International Conference on Intelligent User Interfaces, pp. 33–36 (2002)

    Google Scholar 

  8. Burke, R.: Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)

    Google Scholar 

  9. Cohen, W.: Fast Effective Rule Induction. In: Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, CA, pp. 115–123 (1995)

    Google Scholar 

  10. Cohen, W.: Learning Rules that Classify E-mail. In: Papers from the AAAI Spring Symposium on Machine Learning in Information Access, pp. 18–25 (1996)

    Google Scholar 

  11. Cohen, W., Hirsh, H.: Joins that Generalize: Text Classification Using WHIRL. In: Proceedings of the Fourth International Conference on Knowledge Discovery & Data Mining, New York, NY, pp. 169–173 (1998)

    Google Scholar 

  12. Domingos, P., Pazzani, M.: Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. Machine Learning 29, 103–130 (1997)

    Article  MATH  Google Scholar 

  13. Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley and Sons, New York (1973)

    MATH  Google Scholar 

  14. Foltz, P., Dumais, S.: Personalized Information Delivery: An Analysis of Information Filtering Methods. Communications of the ACM 35(12), 51–60 (1992)

    Article  Google Scholar 

  15. Ittner, D., Lewis, D., Ahn, D.: Text Categorization of Low Quality Images. In: Symposium on Document Analysis and Information Retrieval, Las Vegas, NV, pp. 301–315 (1995)

    Google Scholar 

  16. Joachims, T., Freitag, D., Mitchell, T.: WebWatcher: A Tour Guide for the World Wide Web. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence, Nagoya, Japan, pp. 770–775 (1997)

    Google Scholar 

  17. Joachims, T.: Text Categorization With Support Vector Machines: Learning with Many Relevant Features. In: European Conference on Machine Learning, Chemnitz, Germany, pp. 137–142 (1998)

    Google Scholar 

  18. Kim, J., Lee, B., Shaw, M., Chang, H., Nelson, W.: Application of Decision-Tree Induction Techniques to Personalized Advertisements on Internet Storefronts. International Journal of Electronic Commerce 5(3), 45–62 (2001)

    Google Scholar 

  19. Kivinen, J., Warmuth, M.: Exponentiated Gradient versus Gradient Descent for Linear Predictors. Information and Computation 132(1), 1–63 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  20. Lewis, D., Schapire, R., Callan, J., Papka, R.: Training Algorithms for Linear Text Classifiers. In: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Konstanz, Germany, pp. 298–306. ACM Press, New York (1996)

    Chapter  Google Scholar 

  21. Lewis, D.: Naïve (Bayes) at Forty: The Independence Assumption in Information Retrieval. In: European Conference on Machine Learning, Chemnitz, Germany, pp. 4–15 (1998)

    Google Scholar 

  22. Loeb, S.: Architecting Personal Delivery of Multimedia Information. Communications of the ACM 35(12), 39–48 (1992)

    Article  Google Scholar 

  23. Mandel, M., Poliner, G., Ellis, D.: Support Vector Machine Active Learning for Music Retrieval. ACM Multimedia Systems Journal 12(1), 3–13 (2006)

    Article  Google Scholar 

  24. Maron, M.: Automatic Indexing: An Experimental Inquiry. Journal of the Association for Computing Machinery 8(3), 404–417 (1961)

    MATH  Google Scholar 

  25. McCallum, A., Rosenfeld, R., Mitchell, T., Ng, A.: Improving Text Classification by Shrinkage in a Hierarchy of Classes. In: Proceedings of the International Conference on Machine Learning, Madison, WI, pp. 359–367. Morgan Kaufmann Publishers, San Francisco (1998)

    Google Scholar 

  26. McCallum, A., Nigam, K.: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI/ICML-98 Workshop on Learning for Text Categorization, Technical Report WS-98-05, pp. 41-48. AAAI Press (1998)

    Google Scholar 

  27. Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  28. Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Learning to Classify Text from Labeled and Unlabeled Documents. In: Proceedings of the 15th International Conference on Artificial Intelligence, Madison, WI, pp. 792–799 (1998)

    Google Scholar 

  29. Pazzani, M., Billsus, D.: Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27(3), 313–331 (1997)

    Article  Google Scholar 

  30. Porter, M.: An Algorithm for Suffix Stripping. Program 14(3), 130–137 (1980)

    Google Scholar 

  31. Quinlan, J.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)

    Google Scholar 

  32. Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufman, San Francisco (1993)

    Google Scholar 

  33. Rocchio, J.: Relevance Feedback in Information Retrieval. In: Salton, G. (ed.) The SMART System: Experiments in Automatic Document Processing, pp. 313–323. Prentice Hall, Upper Saddle River (1971)

    Google Scholar 

  34. Salton, G.: Automatic Text Processing. Addison-Wesley, Reading (1989)

    Google Scholar 

  35. Schafer, B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web: Methods and Strategies of Web Personalization. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)

    Google Scholar 

  36. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  37. Widrow, A., Hoff, M.: Adaptive Switching Circuits. WESCON Convention Record 4, 96–104 (1960)

    Google Scholar 

  38. Yang, Y., Pedersen, J.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning, Nashville, TN, pp. 412–420 (1997)

    Google Scholar 

  39. Yang, Y.: An Evaluation of Statistical Approaches to Text Categorization. Information Retrieval 1(1), 67–88 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Rutgers University, ASBIII, 3 Rutgers Plaza, New Brunswick, NJ 08901,  

    Michael J. Pazzani

  2. FX Palo Alto Laboratory, Inc., 3400 Hillview Ave, Bldg. 4, Palo Alto, CA 94304,  

    Daniel Billsus

Authors
  1. Michael J. Pazzani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Billsus
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Peter Brusilovsky Alfred Kobsa Wolfgang Nejdl

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this chapter

Cite this chapter

Pazzani, M.J., Billsus, D. (2007). Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds) The Adaptive Web. Lecture Notes in Computer Science, vol 4321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72079-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72079-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72078-2

  • Online ISBN: 978-3-540-72079-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation