Analysis and characterization of comparison shopping behavior in the mobile handset domain
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In this work we characterize the session-level behavior of users on an Indian mobile phone comparison shopping website. We also correlate the popularity of handset on various news sources to its popularity on the shopping website. There are three aspects to our study: data analysis, correlation between news sources of product information and popularity of a handset, and behavior prediction. We have used KL divergence to show that a time-homogeneous Markov chain is observed when the number of clicks varies from 5 to 30. Our results depict that Markov chain model does not hold in entirety for comparison shopping setting but tells us how far the Markov chain model holds for this setting. Our analysis corroborates intuition that increasing price leads to decrease in popularity. After the strong correlation between various variables and user behavior was found, we predict the users macro (the overall sales of handset) and micro behavior (whether a user will convert or exit the site) using Markov logic networks. Our predictive model validates the intuition that past browsing behavior is an important predictor for future behavior. Methodology of combining data analysis with machine learning is, in our opinion, a new approach to the empirical study of such data sets.
KeywordsOnline comparison shopping User Behavior Markov Logic Networks Machine Learning
- 1.eBizMBA. (2013). Top 15 most popular comparison shopping websites: May 2013. http://www.ebizmba.com/articles/shopping-websites.
- 4.Domingos, P., & Lowd, D. (2009). Markov logic: An interface layer for artificial intelligence. San Rafael, CA: Morgan & Claypool Publishers.Google Scholar
- 5.Gupta, M., Mittal, H., Singla, P., & Bagchi, A. (2014). Characterizing comparison shopping behavior: A case study. In Data engineering workshops (ICDEW).Google Scholar
- 6.Sarukkai, R. R. (2000). Link prediction and path analysis using Markov chains. Computer Networks, 33(1–6), 337–386.Google Scholar
- 7.Yates, R. B., Hurtando, C., Mendoza, M., & Dupret, G. (2005). Modeling user search behavior. In LA-WEB, Web congress.Google Scholar
- 9.Zukerman, I., Albrecht, D. W., & Nicholson, A. E. (1999). Predicting users’ requests on the WWW. In Proceedings of user modeling (pp. 275–284).Google Scholar
- 12.Zhu, J., Hong, J., & Hughes, J. G. (2002). Using Markov chains for link prediction in adaptive web sites. In Proceedings of Software 2002: Computing in an imperfect world (pp. 60–73).Google Scholar
- 13.Cadez, I., Heckerman, D., Christopher, M., Padhraic, S., & Steven, W. (2000). Visualization of navigation patterns on a web site using model-based clustering. In Proceedings of conference on Knowledge discovery and data mining (pp. 280–284).Google Scholar
- 14.Li, J., & Sadagopan, N. (2008). Characterizing typical and atypical user sessions in clickstreams. In Proceedings of WWW’08.Google Scholar
- 17.Wolfinbarger, M., & Gilly, M. (2000). Consumer motivations for online shopping. In Proceedings of the AMCIS 2000 (pp. 1362–1366), California.Google Scholar
- 21.Brown, D., & Hayes, N. (2008). Influencer marketing: Who really influences your customers?. Amsterdam: Elsevier.Google Scholar
- 22.Parikh, N., & Sundaresan, N. (2008). Scalable and near real-time burst detection from eCommerce queries. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 972–980).Google Scholar
- 23.Zhang, H., Parikh, N., Singh, G., & Sundaresan, N. (2013). Chelsea won, and you bought a T-shirt: Characterizing the interplay between Twitter and e-Commerce. In Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining (pp. 829–836).Google Scholar
- 24.Zhang, Y., & Pennacchiotti, M. (2013). Predicting purchase behaviors from Social Media. In Proceedings of the 22nd international conference on World Wide Web (WWW’13).Google Scholar
- 25.Nguyen, T. (2013, February). Q4 (2012) CSE rankings. http://www.cpcstrategy.com/blog/2013/02/q4-2012-cse-rankings/. Published by CPC Strategy.
- 29.Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (Eds.). (1996). Markov chain Monte Carlo in practice. London: Chapman and Hall.Google Scholar
- 31.Pentland, A. P., & Wren, C. R. (1998). Dynamic models of human motion. In International conference on automatic face and gesture recognition (pp. 22–27).Google Scholar
- 33.Borges, J., & Levene, M. (2000). Data mining of user navigation patterns. In: Web usage analysis and user profiling (pp. 92–112). Heidelberg: Springer.Google Scholar
- 35.Chierichetti, F., Kumar, R., Raghavan, P., & Sarlos, T. (2012). Are web users really Markovian? In Proceedings of WWW’12 (pp. 609–618). New York: ACM.Google Scholar
- 36.Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.Google Scholar
- 37.Singla, P., & Domingos, P. (2006). Entity resolution with Markov logic. In Proceedings of the sixth IEEE international conference on data mining (pp. 572–582). Hong Kong: IEEE Computer Society Press.Google Scholar
- 38.Kok, S., Sumner, M., Richardson, M., Singla, P., Poon, H., Lowd, D., et al. (2008). The Alchemy system for statistical relational AI. Technical Report. University of Washington. http://alchemy.cs.washington.edu.
- 39.Poon, H., & Domingos, P. (2006). Sound and efficient inference with probabilistic and deterministic dependencies. In Proceedings of AAAI-06. Boston: AAAI Press.Google Scholar
- 40.Schölkopf, B., Burges, C., & Smola, A. (Eds.). (1998). Advances in kernel methods: Support vector machines. Cambridge, MA: MIT Press.Google Scholar
- 41.Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Belmont, CA: Wadsworth.Google Scholar
- 42.Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.Google Scholar