Recommender System for Product Avoidance

  • Manmeet DhaliwalEmail author
  • Jon Rokne
  • Reda Alhajj
Part of the Lecture Notes in Social Networks book series (LNSN)


Recommender systems are firmly established as a standard technology for assisting users with their choices. There are a variety of recommendation systems that exist which recommend products to users; however, they all focus on finding what the user likes to recommend them products. There has been very little, if at all, research done that focuses on what products the users should be avoiding. In this article, we propose a framework that focuses on computing a list of products that a user should be avoiding. We will be using explicit feedback provided by the user, in the form of reviews. From the reviews posted by a user, we hope to understand attributes about products that they do not like to build a user profile. To achieve that, Stanford CoreNLP Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., & McClosky, D. (2014, June). The stanford corenlp natural language processing toolkit. In ACL (System Demonstrations) (pp. 55–60). tool will be used to find out the keywords and the sentiment values for those keywords. We will also find keywords users used to describe a product to build the profile for the product. After creating our model, we test our framework by computing a list of products that a user should be avoiding. Based on our result, a confusion matrix is created to test the accuracy of our framework. Finally, we formulate challenges and improvement for future research on our approach to recommender system.


Recommender system Explicit feedback Implicit feedback Confusion matrix 


  1. 1.
    Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., & McClosky, D. (2014, June). The stanford corenlp natural language processing toolkit. In ACL (System Demonstrations) (pp. 55–60).Google Scholar
  2. 2.
    Melville, P., & Sindhwani, V. (2011). Recommender systems. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 829–838). New York: Springer.Google Scholar
  3. 3.
    Adomavicius, G., & Tuzhilin, A. (2005). 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.CrossRefGoogle Scholar
  4. 4.
    Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. In R. Kohavi & F. Provost (Eds.), Applications of data mining to electronic commerce (pp. 115–153). Hingham, MA: Springer.CrossRefGoogle Scholar
  5. 5.
    Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web (pp. 291–324). Berlin Heidelberg: Springer.CrossRefGoogle Scholar
  6. 6.
    Van Meteren, R., & Van Someren, M. (2000, May). Using content-based filtering for recommendation. In Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop (pp. 47–56).Google Scholar
  7. 7.
    Melville, P., Mooney, R. J., & Nagarajan, R. (2002, July). Content-boosted collaborative filtering for improved recommendations. In AAAI/IAAI (pp. 187–192).Google Scholar
  8. 8.
    Jawaheer, G., Szomszor, M., & Kostkova, P. (2010, September). Comparison of implicit and explicit feedback from an online music recommendation service. In Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems (pp. 47–51). ACM.Google Scholar
  9. 9.
    Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.CrossRefGoogle Scholar
  10. 10.
    Gauch, S., Speretta, M., Chandramouli, A., & Micarelli, A. (2007). User profiles for personalized information access. The Adaptive Web (pp.54–89).Google Scholar
  11. 11.
    Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 1631–1642).Google Scholar
  12. 12.
    Vu, P. M., Nguyen, T. T., Pham, H. V., & Nguyen, T. T. (2015, November). Mining user opinions in mobile app reviews: A keyword-based approach (t). In Automated Software Engineering (ASE), 2015 30th IEEE/ACM International Conference on (pp. 749–759). Lincoln, NE: IEEEGoogle Scholar
  13. 13.
    Aamir, M., & Bhusry, M. (2015). Recommendation system: State of the art approach. International Journal of Computer Applications, 120(12).CrossRefGoogle Scholar
  14. 14.
    Hu, Y., Koren, Y., & Volinsky, C. (2008, December). Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM’08. Eighth IEEE International Conference on (pp. 263–272). Pisa, Italy: IEEEGoogle Scholar
  15. 15.
    McAuley, J., Targett, C., Shi, Q., & Van Den Hengel, A. 2015, August. Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 43–52). ACM.Google Scholar
  16. 16.
    Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Waltham, MA: Elsevier.Google Scholar
  17. 17.
    Labatut, V., & Cherifi, H. (2012). Accuracy measures for the comparison of classifiers. arXiv preprint arXiv:1207.3790.Google Scholar
  18. 18.
    Zhang, H., & Zhang, X. (2007, September). Comments on ‘Data Mining Static Code Attributes to Learn Defect Predictors’. IEEE Transactions on Software Engineering, 33, 635–637.CrossRefGoogle Scholar
  19. 19.
    Matsuo, Y., & Ishizuka, M. (2004). Keyword extraction from a single document using word co-occurrence statistical information. International Journal on Artificial Intelligence Tools, 13(01), 157–169.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada

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