Contrast Pattern Based Collaborative Behavior Recommendation for Life Improvement

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)


Positive attitudes and happiness have major impacts on human health and in particular recovery from illness. While contributing factors leading human beings to positive emotional states are studied in psychology, the effects of these factors vary and change from one person to another. We propose a behaviour recommendation system that recommends the most effective behaviours leading users with a negative mental state (i.e. unhappiness) to a positive emotional state (i.e., happiness). By leveraging the contrast pattern mining framework, we extract the common contrasting behaviours between happy and unhappy users. These contrast patterns are aligned with user behaviours and habits. We find the personalized behaviour recommendation for those with negative emotional states by placing the problem into the nearest neighborhood collaborative filtering framework. A real dataset of people with heart disease or diabetes is used in our recommendation system. The experiments conducted show that the proposed method can be effective in the health-care domain.


Recommendation System Latent Dirichlet Allocation Contrast Ratio Contrast Pattern Effective Behaviour 
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 work is funded by the Big Data Research, Analytics, and Information Network (BRAIN) Alliance (established by the Ontario Research Fund - Research Excellence Program), Manifold Data Mining Inc., and Natural Sciences and Engineering Research Council of Canada (NSERC). We would like to thank Manifold for providing the dataset used in this research. In particular, we thank Ted Hains of Manifold and Jianhong Wu of York University for their insights and collaboration in our joint project.


  1. 1.
    Abel, F.: We know where you should work next summer: job recommendations. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 230–230. ACM (2015)Google Scholar
  2. 2.
    An, A., Wan, Q., Zhao, J., Huang, X.: Diverging patterns: discovering significant frequency change dissimilarities in large databases. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1473–1476. ACM (2009)Google Scholar
  3. 3.
    Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 635–644. ACM (2011)Google Scholar
  4. 4.
    Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Fan, H., Ramamohanarao, K.: Efficiently mining interesting emerging patterns. In: Dong, G., Tang, C., Wang, W. (eds.) WAIM 2003. LNCS, vol. 2762, pp. 189–201. Springer, Heidelberg (2003). doi: 10.1007/978-3-540-45160-0_19 CrossRefGoogle Scholar
  6. 6.
    Garren, S.T.: Maximum likelihood estimation of the correlation coefficient in a bivariate normal model with missing data. Stat. Probab. Lett. 38(3), 281–288 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  8. 8.
    Karacapilidis, N., Hatzieleftheriou, L.: A hybrid framework for similarity-based recommendations. Int. J. Bus. Intell. Data Min. 1(1), 107–121 (2005)CrossRefGoogle Scholar
  9. 9.
    Kim, W., Kerschberg, L., Scime, A.: Learning for automatic personalization in a semantic taxonomy-based meta-search agent. Electron. Commer. Res. Appl. 1(2), 150–173 (2002)CrossRefGoogle Scholar
  10. 10.
    Li, J., Yang, Q.: Strong compound-risk factors: efficient discovery through emerging patterns and contrast sets. IEEE Trans. Inf. Technol. Biomed. 11(5), 544–552 (2007)CrossRefGoogle Scholar
  11. 11.
    Linden, G., Smith, B., York, J.: recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  12. 12.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72079-9_10 CrossRefGoogle Scholar
  13. 13.
    Ramamohanarao, K., Bailey, J.: Emerging patterns: mining and applications. In: Proceedings of International Conference on Intelligent Sensing and Information Processing, 2004, pp. 409–414. IEEE (2004)Google Scholar
  14. 14.
    Ramamohanarao, K., Bailey, J., Fan, H.: Efficient mining of contrast patterns and their applications to classification. In: 3rd International Conference on Intelligent Sensing and Information Processing, pp. 39–47. IEEE (2005)Google Scholar
  15. 15.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  16. 16.
    Sun, X., Huang, Q., Zhu, Y., Guo, N.: Mining distinguishing patterns based on malware traces. In: 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 2, pp. 677–681. IEEE (2010)Google Scholar
  17. 17.
    Trewin, S.: Knowledge-based recommender systems. Encycl. Libr. Inf. Sci. 69(Suppl. 32), 180 (2000)Google Scholar
  18. 18.
    Webb, G.I., Butler, S., Newlands, D.: On detecting differences between groups. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 256–265. ACM (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceYork UniversityTorontoCanada
  2. 2.Manifold Data Mining Inc.TorontoCanada

Personalised recommendations