Advertisement

Modeling Contextual Changes in User Behaviour in Fashion e-Commerce

  • Ashay TamhaneEmail author
  • Sagar Arora
  • Deepak Warrier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)

Abstract

Impulse purchases are quite frequent in fashion e-commerce; browse patterns indicate fluid context changes across diverse product types probably due to the lack of a well-defined need at the consumer’s end. Data from our fashion e-commerce portal indicate that the final product a person ends-up purchasing is often very different from the initial product he/she started the session with. We refer to this characteristic as a ‘context change’. This feature of fashion e-commerce makes understanding and predicting user behaviour quite challenging. Our work attempts to model this characteristic so as to both detect and preempt context changes. Our approach employs a deep Gated Recurrent Unit (GRU) over clickstream data. We show that this model captures context changes better than other non-sequential baseline models.

Keywords

Product Group Majority Vote User Behaviour Context Change Mean Average Precision 
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.

References

  1. 1.
    Agichtein, E., Brill, E., Dumais, S., Ragno, R.: Learning user interaction models for predicting web search result preferences. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3–10. ACM (2006)Google Scholar
  2. 2.
    Cadez, I., Heckerman, D., Meek, C., Smyth, P., White, S.: Model-based clustering and visualization of navigation patterns on a web site. Data Min. Knowl. Disc. 7(4), 399–424 (2003)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
  4. 4.
    Chung, J., Gülçehre, Ç., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR abs/1412.3555 (2014). http://arxiv.org/abs/1412.3555
  5. 5.
    Dauphin, Y.N., de Vries, H., Chung, J., Bengio, Y.: Rmsprop and equilibrated adaptive learning rates for non-convex optimization. CoRR abs/1502.04390 (2015). http://arxiv.org/abs/1502.04390
  6. 6.
    Ding, A.W., Li, S., Chatterjee, P.: Learning user real-time intent for optimal dynamic web page transformation. Inf. Syst. Res. 26(2), 339–359 (2015). http://dx.doi.org/10.1287/isre.2015.0568 CrossRefGoogle Scholar
  7. 7.
    Gündüz, Ş., Özsu, M.T.: A web page prediction model based on click-stream tree representation of user behavior. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 535–540. ACM (2003)Google Scholar
  8. 8.
    Guo, Q., Agichtein, E.: Ready to buy or just browsing?: detecting web searcher goals from interaction data. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 130–137. ACM (2010)Google Scholar
  9. 9.
    Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. CoRR abs/1511.06939 (2015). http://arxiv.org/abs/1511.06939
  10. 10.
    Kim, D.H., Atluri, V., Bieber, M., Adam, N., Yesha, Y.: A clickstream-based collaborative filtering personalization model: towards a better performance. In: Proceedings of the WIDM 2004 6th Annual ACM International Workshop on Web Information and Data Management, pp. 88–95. ACM, NY, USA (2004). http://doi.acm.org/10.1145/1031453.1031470
  11. 11.
    Kim, E., Kim, W., Lee, Y.: Combination of multiple classifiers for the customer’s purchase behavior prediction. Decis. Support Syst. 34(2), 167–175 (2003)CrossRefGoogle Scholar
  12. 12.
    Lakshminarayan, C., Kosuru, R., Hsu, M.: Modeling complex clickstream data by stochastic models: theory and methods. In: Proceedings of the 25th International Conference Companion on World Wide Web. WWW 2016 Companion, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 879–884 (2016). http://dx.doi.org/10.1145/2872518.2891070
  13. 13.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). http://dx.doi.org/10.1038/nature14539 CrossRefGoogle Scholar
  14. 14.
    Lee, J., Podlaseck, M., Schonberg, E., Hoch, R.: Visualization and analysis of clickstream data of online stores for understanding web merchandising. In: Kohavi, R., Provost, F. (eds.) Applications of Data Mining to Electronic Commerce, pp. 59–84. Springer, New York (2001)CrossRefGoogle Scholar
  15. 15.
    Li, Z., Shang, W.: Personalized news recommendation based on links of web. In: 2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS), pp. 581–584. IEEE (2015)Google Scholar
  16. 16.
    Lo, C., Frankowski, D., Leskovec, J.: Understanding behaviors that lead to purchasing: a case study of pinterest. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, NY, USA, pp. 531–540 (2016). http://doi.acm.org/10.1145/2939672.2939729
  17. 17.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013). http://arxiv.org/abs/1301.3781
  18. 18.
    Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., Khudanpur, S.: Recurrent neural network based language model. In: 11th Annual Conference of the International Speech Communication Association. INTERSPEECH 2010, Makuhari, Chiba, Japan, 26–30 September 2010, pp. 1045–1048 (2010)Google Scholar
  19. 19.
    Moe, W.W.: Buying, searching, or browsing: differentiating between online shoppers using in-store navigational clickstream. J. Consum. Psychol. 13(1), 29–39 (2003)CrossRefGoogle Scholar
  20. 20.
    Montgomery, A.L., Li, S., Srinivasan, K., Liechty, J.C.: Modeling online browsing and path analysis using clickstream data. Mark. Sci. 23, 579–595 (2004)CrossRefGoogle Scholar
  21. 21.
    Sagar Arora, D.W.: Decoding fashion contexts using word embeddings. Machine Learning Meets Fashion, KDD 2016 Workshop (2016)Google Scholar
  22. 22.
    Senecal, S., Kalczynski, P.J., Nantel, J.: Consumers’ decision-making process and their online shopping behavior: a clickstream analysis. J. Bus. Res. 58(11), 1599–1608 (2005)CrossRefGoogle Scholar
  23. 23.
    Su, Q., Chen, L.: A method for discovering clusters of e-commerce interest patterns using click-stream data. Electron. Commer. Res. Appl. 14(1), 1–13 (2015)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Vieira, A.: Predicting online user behaviour using deep learning algorithms. arXiv preprint arXiv:1511.06247 (2015)
  25. 25.
    Voorhees, E.M.: The TREC question answering track. Nat. Lang. Eng. 7(4), 361–378 (2001). http://dx.doi.org/10.1017/S1351324901002789 MathSciNetCrossRefGoogle Scholar
  26. 26.
    Wang, G., Zhang, X., Tang, S., Zheng, H., Zhao, B.Y.: Unsupervised clickstream clustering for user behavior analysis. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI 2016, NY, USA, pp. 225–236 (2016). http://doi.acm.org/10.1145/2858036.2858107
  27. 27.
    Waterson, S.J., Hong, J.I., Sohn, T., Landay, J.A., Heer, J., Matthews, T.: What did they do? understanding clickstreams with the webquilt visualization system. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 94–102. ACM (2002)Google Scholar
  28. 28.
    Wei, J., Shen, Z., Sundaresan, N., Ma, K.L.: Visual cluster exploration of web clickstream data. In: 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 3–12. IEEE (2012)Google Scholar
  29. 29.
    Zhang, M., Chen, G., Wei, Q.: Discovering consumers’ purchase intentions based on mobile search behaviors. Flexible Query Answering Systems 2015. AISC, vol. 400, pp. 15–28. Springer, Cham (2016). doi: 10.1007/978-3-319-26154-6_2 CrossRefGoogle Scholar
  30. 30.
    Zhao, J., Liu, Z., Dontcheva, M., Hertzmann, A., Wilson, A.: MatrixWave: Visual comparison of event sequence data. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 259–268. ACM (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.MyntraBengaluruIndia

Personalised recommendations