Effective Product Recommendation using the Real-Time Web
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.
KeywordsRecommender System Sentiment Analysis Target User Similar User Recommendation Algorithm
Unable to display preview. Download preview PDF.
Based on work supported by Science Foundation Ireland, Grant No. 07/CE/I1147.
- 3.. S. Ahn and C.-K. Shi. Exploring movie recommendation system using cultural metadata. Transactions on Edutainment II, pages 119–134, 2009.Google Scholar
- 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.. 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.Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30–37, 2009.Google Scholar
- 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.. 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.. V. Pandey and C. Iyer. Sentiment analysis of microblogs. http://www.stanford.edu/ class/cs229/proj2009/PandeyIyer.pdf, 2009. Accessed on: April 2010.
- 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.. 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.. 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.. J. Read. Using emoticons to reduce dependency in machine learning techniques for sentiment classification, 2005.Google Scholar
- 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.G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA, 1986.Google Scholar
- 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
- 27.C. J. van Rijsbergen. Information Retrieval. Butterworth-Heinemann, Newton, MA, USA, 1979.Google Scholar
- 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.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