Skip to main content

Content-Based Recommender Systems

Abstract

The collaborative systems discussed in the previous chapters use the correlations in the ratings patterns across users to make recommendations. On the other hand, these methods do not use item attributes for computing predictions. This would seem rather wasteful; after all, if John likes the futuristic science fiction movie Terminator, then there is a very good chance that he might like a movie from a similar genre, such as Aliens. In such cases, the ratings of other users may not be required to make meaningful recommendations.

Keywords

  • Recommender System
  • User Profile
  • Gini Index
  • Recommendation Process
  • Implicit Feedback

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-29659-3_4
  • Chapter length: 28 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-29659-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   69.99
Price excludes VAT (USA)
Hardcover Book
USD   69.99
Price excludes VAT (USA)

Notes

  1. 1.

    The exact recommendation method used by IMDb is proprietary and not known to the author. The description here is intended only for illustrative purposes.

  2. 2.

    For structured data, the centroid of the group may be used.

  3. 3.

    A different approach in collaborative filtering is to leverage user-user rules. For user-user rules, the antecedents and consequents may both contain the ratings of specific users. Refer to section 3.3 of Chapter 3.

Bibliography

  1. G. Adomavicius, and A. Tuzhilin. 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, 17(6), pp. 734–749, 2005.

    CrossRef  Google Scholar 

  2. C. Aggarwal. Data classification: algorithms and applications. CRC Press, 2014.

    Google Scholar 

  3. C. Aggarwal and C. Zhai. A survey of text classification algorithms. Mining Text Data, Springer, 2012.

    Google Scholar 

  4. C. Aggarwal. Data mining: the textbook. Springer, New York, 2015.

    Google Scholar 

  5. J. Ahn, P. Brusilovsky, J. Grady, D. He, and S. Syn. Open user profiles for adaptive news systems: help or harm? World Wide Web Conference, pp. 11–20, 2007.

    Google Scholar 

  6. A. Ansari, S. Essegaier, and R. Kohli. Internet recommendation systems. Journal of Marketing Research, 37(3), pp. 363–375, 2000.

    CrossRef  Google Scholar 

  7. F. Asnicar and C. Tasso. IfWeb: a prototype of user model-based intelligent agent for document filtering and navigation in the world wide web. International Conference on User Modeling, pp. 3–12, 1997.

    Google Scholar 

  8. M. Balabanovic, and Y. Shoham. Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3), pp. 66–72, 1997.

    CrossRef  Google Scholar 

  9. D. Billsus and M. Pazzani. Learning collaborative information filters. ICML Conference, pp. 46–54, 1998.

    Google Scholar 

  10. D. Billsus and M. Pazzani. Learning probabilistic user models. International Conference on User Modeling, Workshop on Machine Learning for User Modeling, 1997.

    Google Scholar 

  11. D. Billsus and M. Pazzani. A hybrid user model for news story classification. International Conference on User Modeling, 1999.

    Google Scholar 

  12. D. Billsus and M. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10(2–3), pp. 147–180, 2000.

    CrossRef  Google Scholar 

  13. C. M. Bishop. Pattern recognition and machine learning. Springer, 2007.

    Google Scholar 

  14. C. M. Bishop. Neural networks for pattern recognition. Oxford University Press, 1995.

    Google Scholar 

  15. K. Bollacker, S. Lawrence, and C. L. Giles. CiteSeer: An autonomous web agent for automatic retrieval and identification of interesting publications. International Conference on Autonomous Agents, pp. 116–123, 1998.

    Google Scholar 

  16. C. Burges. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2), pp. 121–167, 1998.

    CrossRef  Google Scholar 

  17. R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-adapted Interaction, 12(4), pp. 331–370, 2002.

    CrossRef  MATH  Google Scholar 

  18. D. Cai, S. Yu, J. Wen, and W. Y. Ma. Extracting content structure for web pages based on visual representation. Web Technologies and Applications, pp. 406–417, 2003.

    Google Scholar 

  19. O. Celma, M. Ramirez, and P. Herrera. Foafing the music: A music recommendation system based on RSS feeds and user preferences. International Conference on Music Information Retrieval, pp. 464–467, 2005.

    Google Scholar 

  20. O. Celma, and X. Serra. FOAFing the music: Bridging the semantic gap in music recommendation. Web Semantics: Science, Services and Agents on the World Wide Web, 6(4), pp. 250–256, 2008.

    CrossRef  Google Scholar 

  21. S. Chakrabarti. Mining the Web: Discovering knowledge from hypertext data. Morgan Kaufmann, 2003.

    Google Scholar 

  22. L. Chen, and K. Sycara. WebMate: a personal agent for browsing and searching. International conference on Autonomous agents, pp. 9–13, 1998.

    Google Scholar 

  23. W. Cohen, R. Schapire and Y. Singer. Learning to order things. Advances in Neural Information Processing Systems, pp. 451–457, 2007.

    Google Scholar 

  24. W. Cohen. Learning rules that classify e-mail. AAAI symposium on machine learning in information access. pp. 18–25, 1996.

    Google Scholar 

  25. W. Cohen. Fast effective rule induction. ICML Conference, pp. 115–123, 1995.

    Google Scholar 

  26. A. Csomai and R. Mihalcea. Linking documents to encyclopedic knowledge. IEEE Intelligent Systems, 23(5), pp. 34–41, 2008.

    CrossRef  Google Scholar 

  27. M. De Gemmis, P. Lops, and G. Semeraro. A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation. User Modeling and User-Adapted Interaction, 17(3), pp. 217–255, 2007.

    CrossRef  Google Scholar 

  28. M. De Gemmis, P. Lops, G. Semeraro and P. Basile. Integrating tags in a semantic content-based recommender. Proceedings of the ACM Conference on Recommender Systems, pp. 163–170, 2008.

    Google Scholar 

  29. E. Gabrilovich and S. Markovitch. Computing semantic relatedness using wikipedia-based explicit semantic analysis. IJCAI Conference, pp. 1606–1611, 2007.

    Google Scholar 

  30. E. Gabrilovich, and S. Markovitch. Overcoming the brittleness bottleneck using Wikipedia: Enhancing text categorization with encyclopedic knowledge. AAAI Conference, pp. 1301–1306, 2006.

    Google Scholar 

  31. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning. Springer, 2009.

    Google Scholar 

  32. T. Joachims. Training linear SVMs in linear time. ACM KDD Conference, pp. 217–226, 2006.

    Google Scholar 

  33. J. Lees-Miller, F. Anderson, B. Hoehn, and R. Greiner. Does Wikipedia information help Netflix predictions?. Machine Learning and Applications, pp. 337–343, 2008.

    Google Scholar 

  34. H. Lieberman. Letizia: An agent that assists Web browsing, IJCAI, pp. 924–929, 1995.

    Google Scholar 

  35. B. Liu. Web data mining: exploring hyperlinks, contents, and usage data. Springer, New York, 2007.

    Google Scholar 

  36. P. Lops, M. de Gemmis, and G. Semeraro. Content-based recommender systems: state of the art and trends. Recommender Systems Handbook, Springer, pp. 73–105, 2011.

    Google Scholar 

  37. H. Mak, I. Koprinska, and J. Poon. Intimate: A web-based movie recommender using text categorization. International Conference on Web Intelligence, pp. 602–605, 2003.

    Google Scholar 

  38. B. Magnini, and C. Strapparava. Improving user modelling with content-based techniques. International Conference on User Modeling, pp. 74–83, 2001.

    Google Scholar 

  39. C. Manning, P. Raghavan, and H. Schutze. Introduction to information retrieval. Cambridge University Press, Cambridge, 2008.

    Google Scholar 

  40. S. McNee, J. Riedl, and J. Konstan. Being accurate is not enough: how accuracy metrics have hurt recommender systems. SIGCHI Conference, pp. 1097–1101, 2006.

    Google Scholar 

  41. T. M. Mitchell. Machine learning. McGraw Hill International Edition, 1997.

    Google Scholar 

  42. D. Mladenic. Machine learning used by Personal WebWatcher. Proceedings of the ACAI-99 Workshop on Machine Learning and Intelligent Agents, 1999.

    Google Scholar 

  43. D. Mladenic. Text learning and related intelligent agents: A survey. IEEE Intelligent Systems, 14(4), pp. 44–54, 1999.

    CrossRef  Google Scholar 

  44. R. J. Mooney and L. Roy. Content-based book recommending using learning for text categorization. ACM Conference on Digital libraries, pp. 195–204, 2000.

    Google Scholar 

  45. M. Pazzani and D. Billsus. Learning and revising user profiles: The identification of interesting Web sites. Machine learning, 27(3), pp. 313–331, 1997.

    CrossRef  Google Scholar 

  46. M. Pazzani and D. Billsus. Content-based recommendation systems. Lecture Notes in Computer Science, Springer, 4321, pp. 325–341, 2007.

    Google Scholar 

  47. M. Pazzani, J. Muramatsu, and D. Billsus. Syskill and Webert: Identifying interesting Web sites. AAAI Conference, pp. 54–61, 1996.

    Google Scholar 

  48. J. Rocchio. Relevance feedback information retrieval. The SMART retrieval system – experiments in automated document processing, pp. 313–323, Prentice-Hall, Englewood Cliffs, NJ, 1971.

    Google Scholar 

  49. J. Salter, and N. Antonopoulos. CinemaScreen recommender agent: combining collaborative and content-based filtering. Intelligent Systems, 21(1), pp. 35–41, 2006.

    CrossRef  Google Scholar 

  50. B. Sheth and P. Maes. Evolving agents for personalized information filtering. Ninth Conference on Artificial Intelligence for Applications, pp. 345–352, 1993.

    Google Scholar 

  51. H. Sorensen and M. McElligott. PSUN: a profiling system for Usenet news. CIKM Intelligent Information Agents Workshop, 1995.

    Google Scholar 

  52. E. G. Toms. Serendipitous information retrieval. DELOS Workshop: Information Seeking, Searching and Querying in Digital Libraries, 2000.

    Google Scholar 

  53. K. L. Wu, C. C. Aggarwal, and P. S. Yu. Personalization with dynamic profiler. International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems, pp. 12–20, 2001. Also available online as IBM Research Report, RC22004, 2001. Search interface at http://domino.research.ibm.com/library/cyberdig.nsf/index.html

  54. Y. Zhai, and B. Liu. Web data extraction based on partial tree alignment. World Wide Web Conference, pp. 76–85, 2005.

    Google Scholar 

  55. http://www.pandora.com

  56. http://www.imdb.com

  57. http://www.pandora.com/about/mgp

  58. http://opennlp.apache.org/index.html

  59. https://code.google.com/p/ir-themis/

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Aggarwal, C.C. (2016). Content-Based Recommender Systems. In: Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29659-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-29659-3_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29657-9

  • Online ISBN: 978-3-319-29659-3

  • eBook Packages: Computer ScienceComputer Science (R0)