Advertisement

Effective Product Recommendation using the Real-Time Web

  • Sandra Garcia Esparza
  • Michael P. O’Mahony
  • Barry Smyth
Conference paper

Abstract

The so-called real-time web (RTW) is a web of opinions, comments, and personal viewpoints, often expressed in the form of short, 140-character text messages providing abbreviated and highly personalized commentary in real-time. Today, Twitter is undoubtedly the king of the RTW. It boasts 190 million users and generates in the region of 65m tweets per day1. This RTW data is far from the structured data (movie ratings, product features, etc.) that is familiar to recommender systems research but it is useful to consider its applicability to recommendation scenarios. In this paper we consider harnessing the real-time opinions of users, expressed through the Twitter-like short textual reviews available on the Blippr service (www.blippr.com). In particular we describe how users and products can be represented from the terms used in their associated reviews and describe experiments to highlight the recommendation potential of this RTW data-source and approach.

Keywords

Recommender System Sentiment Analysis Target User Similar User Recommendation Algorithm 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

Based on work supported by Science Foundation Ireland, Grant No. 07/CE/I1147.

References

  1. 1.
    S. Aciar, D. Zhang, S. Simoff, and J. Debenham. Recommender system based on consumer product reviews. In Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pages 719–723, Washington, DC, USA, 2006. IEEE Computer Society.CrossRefGoogle Scholar
  2. 2.
    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):734–749, 2005.CrossRefGoogle Scholar
  3. 3.
    . S. Ahn and C.-K. Shi. Exploring movie recommendation system using cultural metadata. Transactions on Edutainment II, pages 119–134, 2009.Google Scholar
  4. 4.
    M. Balabanović and Y. Shoham. Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3):66–72, 1997.CrossRefGoogle Scholar
  5. 5.
    R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User- Adapted Interaction, 12(4):331–370, 2002.MATHCrossRefGoogle Scholar
  6. 6.
    S. Chelcea, G. Gallais, and B. Trousse. A personalized recommender system for travel information. In Proceedings of the 1st French-speaking conference on Mobility and ubiquity computing (UbiMob ’04), pages 143–150, New York, NY, USA, 2004. ACM.CrossRefGoogle Scholar
  7. 7.
    M. Hu and B. Liu. Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ’04), pages 168–177, New York, NY, USA, 2004. ACM.CrossRefGoogle Scholar
  8. 8.
    . B. J. Jansen, M. Zhang, K. Sobel, and A. Chowdury. Micro-blogging as online word of mouth branding. Proceedings of the 27th international conference extended abstracts on Human factors in computing systems (CHI EA ’09), pages 3859–3864, 2009.Google Scholar
  9. 9.
    . Y. Koren. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 447–456, Paris, France, June 28–July 1 2009.Google Scholar
  10. 10.
    Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30–37, 2009.Google Scholar
  11. 11.
    . C. W.-k. Leung, S. C.-f. Chan, and F.-l. Chung. Integrating collaborative filtering and sentiment analysis: A rating inference approach. In Proceedings of the ECAI 2006 Workshop on Recommender Systems, pages 62–66, Riva del Garda, Italy, 2006.Google Scholar
  12. 12.
    . T. Mullen and N. Collier. Sentiment analysis using support vector machines with diverse information sources. In Proceedings of the conference on Empirical Methods in Natural Language Processing, 2004.Google Scholar
  13. 13.
    . V. Pandey and C. Iyer. Sentiment analysis of microblogs. http://www.stanford.edu/ class/cs229/proj2009/PandeyIyer.pdf, 2009. Accessed on: April 2010.
  14. 14.
    B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing (EMNLP ’02), pages 79–86, Morristown, NJ, USA, 2002. Association for Computational Linguistics.CrossRefGoogle Scholar
  15. 15.
    . D. Poirier, I. Tellier, F. Franoise, and S. Julien. Toward text-based recommendations. In Proceedings of the 9th international conference on Adaptivity, Personalization and Fusion of Heterogeneous Information (RIAO ’10), Paris, France, 2010.Google Scholar
  16. 16.
    . A.-M. Popescu and O. Etzioni. Extracting product features and opinions from reviews. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT ’05), pages 339–346, 2005.Google Scholar
  17. 17.
    . J. Read. Using emoticons to reduce dependency in machine learning techniques for sentiment classification, 2005.Google Scholar
  18. 18.
    P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of ACM Conference on Computer- Supported Cooperative Work (CSCW 94), pages 175–186, Chapel Hill, North Carolina, USA, August 1994.CrossRefGoogle Scholar
  19. 19.
    G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA, 1986.Google Scholar
  20. 20.
    B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Analysis of recommendation algorithms for ecommerce. In Proceedings of the 2nd ACM Conference on Electronic Commerce (EC ’00), pages 158–167, Minneapolis, Minnesota, USA, October 17-20 2000. ACM.CrossRefGoogle Scholar
  21. 21.
    . B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference (WWW ’01), pages 285–295, Hong Kong, May 2001.Google Scholar
  22. 22.
    J. B. Schafer, J. A. Konstan, and J. Riedl. E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1-2):115–153, 2001.MATHCrossRefGoogle Scholar
  23. 23.
    S. Sen, J. Vig, and J. Riedl. Tagommenders: connecting users to items through tags. In Proceedings of the 18th international conference on World wide web (WWW ’09), pages 671–680, New York, NY, USA, 2009. ACM.CrossRefGoogle Scholar
  24. 24.
    U. Shardanand and P. Maes. Social information filtering: algorithms for automating “word of mouth”. In Proceedings of the SIGCHI conference on Human factors in computing systems (CHI ’95), pages 210–217, New York, NY, USA, 1995. ACM Press/Addison-Wesley Publishing Co.CrossRefGoogle Scholar
  25. 25.
    B. Smyth and P. Cotter. A personalised TV listings service for the digital TV age. Knowledge- Based Systems, 13(2-3):53–59, 2000.CrossRefGoogle Scholar
  26. 26.
    H. Tang, S. Tan, and X. Cheng. A survey on sentiment detection of reviews. Expert Systems with Applications, 36(7):10760–10773, 2009.CrossRefGoogle Scholar
  27. 27.
    C. J. van Rijsbergen. Information Retrieval. Butterworth-Heinemann, Newton, MA, USA, 1979.Google Scholar
  28. 28.
    . J.Wiebe and E. Riloff. Creating subjective and objective sentence classifiers from unannotated texts. In Proceedings of the 6th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing ’05), pages 486–497, Mexico City, Mexico, 2005.Google Scholar
  29. 29.
    R. T. A. Wietsma and F. Ricci. Product reviews in mobile decision aid systems. In Pervasive Mobile Interaction Devices (PERMID ’05), pages 15–18, Munich, Germany, 2005.Google Scholar
  30. 30.
    Z. Zhang and B. Varadarajan. Utility scoring of product reviews. In Proceedings of the 15th ACM international conference on Information and knowledge management (CIKM ’06), pages 51–57, New York, NY, USA, 2006. ACM.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.CLARITY: Centre for Sensor Web Technologies, School of Computer Science and InformaticsUniversity College DublinDublinIreland

Personalised recommendations