Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : Nithya V | Jeeva Bunnamed

Volume 04 Issue 02 2017

ISSN no:  2348-3121

Page no: 61-63

Abstract – In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users’ social media profiles and their e-commerce behaviors. Our system predicts a user’s purchase behaviors on e-commerce websites from the user’s social media profile. We specifically aim at understanding if the user’s profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). Our system provides an extensive analysis on how users’ Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction.

KeywordsE-commerce, social media, recommendation, Data mining, Cold start


  1. Bonhard, P. & Sasse, M. A. 2006. Knowing me, Knowing you – Using Profiles and Social Networking to Improve Recommender Systems. BT Technology Journal 24, 3 (Jul. 2006), 84-98.
  2. H. Ma, T. C. Zhou, M. R. Lyu, and I. King, “Improving recommender systems by incorporating social contextual in-formation,” ACM Trans. Inf. Syst., vol. 29, no. 2, 2011.
  3. J. Wang, W. X. Zhao, Y. He, and X. Li, “Leveraging product adopter information from online reviews for product recommendation,” in ICWSM, 2015.
  4. J. Wang and Y. Zhang, “Opportunity model for e-commerce recommendation: Right product; right time,” in SIGIR, 2013.
  5. W. X. Zhao, Y. Guo, Y. He, H. Jiang, Y. Wu, and X. Li, “We know what you want to buy: a demographic-based system for product recommendation on micro blogs,” in SIGKDD, 2014.
  6. Y. Seroussi, F. Bohnert, and I. Zukerman, “Personalised rating prediction for new users using latent factor models,” in ACMHH, 2011.