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Text-Based User-kNN: Measuring User Similarity Based on Text Reviews

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 8538)

Abstract

This article reports on a modification of the user-kNN algorithm that measures the similarity between users based on the similarity of text reviews, instead of ratings. We investigate the performance of text semantic similarity measures and we evaluate our text-based user-kNN approach by comparing it to a range of ratings-based approaches in a ratings prediction task. We do so by using datasets from two different domains: movies from RottenTomatoes and Audio CDs from Amazon Products. Our results show that the text-based userkNN algorithm performs significantly better than the ratings-based approaches in terms of accuracy measured using RMSE.

Keywords

  • Recommender systems
  • Collaborative Filtering
  • Text reviews
  • Semantic similarity measures

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References

  1. Herlocker, J., Konstan, J., Borchers, J.A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proceedings of the 1999 Conference on Research and Development in Information Retrieval (1999)

    Google Scholar 

  2. Terzi, M., Ferrario, M., Whittle, J.: Free Text In User Reviews: Their Role In Recommender Systems. In: Proceedings of the 3rd ACM RecSys 2010 Workshop on Recommender Systems and the Social Web, pp. 45–48. ACM, Chicago (2011)

    Google Scholar 

  3. Leung, C.W.K., Chan, S.C.F., Chung, F.: Integrating collaborative filtering and sentiment analysis: A rating inference approach. In: Proceedings of the ECAI 2006 Workshop on Recommender Systems, Riva del Garda, Italy, pp. 62–66 (2006)

    Google Scholar 

  4. Zhang, W., Ding, G., Chen, L., Li, C.: Augmenting Chinese Online Video Recommendations by Using Virtual Ratings Predicted by Review Sentiment Classification. In: Proc. of the IEEE ICDM Workshops. IEEE Computer Society, Washington, DC (2010)

    Google Scholar 

  5. Chen, L., Wang, F.: Preference-based Clustering Reviews for Augmenting e-Commerce Recommendation. In: Knowledge-Based Systems (2013)

    Google Scholar 

  6. Musat, C.C., Liang, Y., Faltings, B.: Recommendation using textual opinions. In: Proceedings of the 23rd IJCAI, pp. 2684–2690. AAAI Press (2013)

    Google Scholar 

  7. Pero, Š., Horváth, T.: Opinion-Driven Matrix Factorization for Rating Prediction. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 1–13. Springer, Heidelberg (2013)

    CrossRef  Google Scholar 

  8. Singh, V.K., Mukherjee, M., Mehta, G.K.: Combining collaborative filtering and sentiment classification for improved movie recommendations. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds.) MIWAI 2011. LNCS, vol. 7080, pp. 38–50. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  9. Raghavan, S., Gunasekar, S., Ghosh, J.: Review quality aware collaborative filtering. In: Proceedings of the 6th ACM Conference on RecSys, pp. 123–130. ACM, Chicago (2011)

    Google Scholar 

  10. McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM RecSys. ACM (2013)

    Google Scholar 

  11. Levi, A., Mokryn, O., Diot, C., Taft, N.: Finding a needle in a haystack of reviews: cold start context-based hotel recommender system. In: Proc. RecSys 2012, pp. 115–122. ACM, New York (2012)

    Google Scholar 

  12. Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  13. Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet: Similarity - Measuring the Relatedness of Concepts. In: Proc. of AAAI, pp. 1024–1025. AAAI, Menlo Park (2004)

    Google Scholar 

  14. Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)

    Google Scholar 

  15. Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellbaum, C. (ed.), pp. 305–332. MIT Press (1998)

    Google Scholar 

  16. Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: 32nd Annual Meeting of the Association for Computational Linguistics, pp. 133–138 (1994)

    Google Scholar 

  17. Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of IJCAI, pp. 448–453 (1995)

    Google Scholar 

  18. Lin, D.: An information theoretic definition of similarity. In: Proceedings of the 15th IICML. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  19. Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: ROCLING X. Academia Sinica, Tapei (1997)

    Google Scholar 

  20. Miller, G.A., Leacock, C., Tengi, R., Bunker, R.T.: A semantic concordance. In: Proceedings of the Workshop on HLT, Stroudsburg, PA, USA, pp. 303–308 (1993)

    Google Scholar 

  21. Gantner, Z., Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Mymedialite: a free recommender system library. In: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 305–308. ACM, New York (2011)

    CrossRef  Google Scholar 

  22. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006)

    MATH  MathSciNet  Google Scholar 

  23. Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook (2011)

    Google Scholar 

  24. Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the Conference on Web Search and Web Data Mining (2008)

    Google Scholar 

  25. Bennet, J., Lanning, S.: The Netflix Prize. In: KDD Cup and Workshop (2007)

    Google Scholar 

  26. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD, pp. 426–434. ACM, New York (2008)

    Google Scholar 

  27. Mohler, M., Mihalcea, R.: Text-to-Text Semantic Similarity for Automatic Short Answer Grading. In: EC-ACL 2009, Athens, Greece, pp. 567–575 (2009)

    Google Scholar 

  28. Gunawardana, A., Shani, G.: A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 10, 2935–2962 (2009)

    MATH  MathSciNet  Google Scholar 

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Terzi, M., Rowe, M., Ferrario, MA., Whittle, J. (2014). Text-Based User-kNN: Measuring User Similarity Based on Text Reviews. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_17

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  • DOI: https://doi.org/10.1007/978-3-319-08786-3_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08785-6

  • Online ISBN: 978-3-319-08786-3

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