Most online shops apply recommender systems, i.e. software agents that elicit the users’ preferences and interests with the purpose to make product recommendations. Many of these systems suffer from the new user cold start problem which occurs when no transaction history is available for the particular new prospective buyer. External data from social networking sites, like Facebook, seem promising to overcome this problem. In this paper, we evaluate the value of Facebook profile data to create meaningful product recommendations. We find based on the outcomes of a user experiment that already simple approaches and plain profile data matching yield significant better recommendations than a pure random draw from the product data base. However, the most successful approaches use semantic categories like music/video, brands and product category information to match profile and product data. A second experiment indicates that recommendation quality seems to be stable for different profile sizes.
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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. Retrieved from http://www.citeulike.org/group/22/article/171426.
Ahearne, M., Bhattacharya, C. B., & Gruen, T. (2005). Antecedents and consequences of customer-company identification: expanding the role of relationship marketing. The Journal of Applied Psychology, 90(3), 574–585. doi:10.1037/0021-9010.90.3.574.
Aral, S., & Walker, D. (2012). Identifying influential and susceptible members of social networks. Science (New York, N.Y.), 337(6092), 337–341. doi:10.1126/science.1215842.
Beane, T. P., & Ennis, D. M. (1987). Market segmentation: a review. European Journal of Marketing, 21(5), 20–42. doi:10.1108/EUM0000000004695.
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 106-132. doi:10.1016/j.knosys.2013.03.012.
Dewally, M., & Ederington, L. (2006). Reputation, certification, warranties, and information as remedies for seller-buyer information asymmetries: lessons from the online comic book market. The Journal of Business, 79(2), 693–729. doi:10.1086/499169.
Facebook.com. (2014). Accessing Your Facebook Info | Facebook Help Center. Retrieved February 01, 2014, from https://www.facebook.com/help/405183566203254/.
Fennell, G., Allenby, G. M., Yang, S., & Edwards, Y. (2003). The effectiveness of demographic and psychographic variables for explaining brand and product category use. Quantitative Marketing and Economics, 1(2), 223–244. doi:10.1023/A:1024686630821.
Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251. doi:10.2307/1913827.
He, J., & Chu, W. W. (2010). A social network-based recommender system (SNRS). Data Mining for Social Network Data (12), 47–74. Springer. doi:10.1007/978-1-4419-6287-4.
Hinz, O., & Eckert, J. (2010). The impact of search and recommendation systems on sales in electronic commerce. Business & Information Systems Engineering, 2(2), 67–77. doi:10.1007/s12599-010-0092-x.
Hinz, O., Hann, I., & Spann, M. (2011). Price discrimination in E-commerce? An examination of dynamic pricing in name-your-own price markets. MIS Quarterly, 35(1), 81–98. Retrieved from http://dl.acm.org/citation.cfm?id=2017489.
Huang, Z., Chung, W., & Chen, H. (2004). A graph model for E-commerce recommender systems. Journal of the American Society for Information Science and Technology, 55(3), 259–274. Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/asi.10372/full.
Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: an introduction. Cambridge University Press.
Kass, R., & Finin, T. (1988). Modeling the user in natural language systems. Computational Linguistics, 14(3), 5–22.
Kim, H.-N., Ji, A.-T., Ha, I., & Jo, G.-S. (2010). Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electronic Commerce Research and Applications, 9(1), 73–83.
Li, Y.-M., Wu, C.-T., & Lai, C.-Y. (2013). A social recommender mechanism for e-commerce: combining similarity, trust, and relationship. Decision Support Systems, 55(3), 740–752. doi:10.1016/j.dss.2013.02.009.
McAlexander, J., Schouten, J., & Koenig, H. (2002). Building brand community. The Journal of Marketing, 66(1), 38–54. Retrieved from http://www.jstor.org/stable/10.2307/3203368.
Montaner, M., López, B., & La Rosa, J. D. (2003). A taxonomy of recommender agents on the internet. Artificial Intelligence Review, 19, 285–330. Retrieved from http://www.springerlink.com/index/KK844421T5466K35.pdf.
Nelson, P. (1970). Information and consumer behavior. The Journal of Political Economy, 78(2), 311–329. Retrieved from http://www.jstor.org/stable/1830691.
Pereira, R. (2000). Optimizing human-computer interaction for the electronic commerce environment. Journal of Electronic Commerce Research, 1(1), 23–44. Retrieved from http://www.csulb.edu/web/journals/jecr/issues/20001/paper3.pdf.
Rashid, A. M., Karypis, G., & Riedl, J. (2008). Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explorations Newsletter, 10(2), 90–100.
Rodríguez, R. M., Espinilla, M., Sánchez, P. J., & Martínez-López, L. (2010). Using linguistic incomplete preference relations to cold start recommendations. Internet Research, 20(3), 296–315.
Schafer, J., Konstan, J., & Riedl, J. (2001). E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5, 115–153. Retrieved from http://www.springerlink.com/index/r24285574675qu7v.pdf.
Spiekermann, S., Grossklags, J., & Berendt, B. (2001). E-privacy in 2nd generation E-commerce. In Proceedings of the 3rd ACM conference on Electronic Commerce - EC ’01 (pp. 38–47). New York, New York, USA: ACM Press. doi:10.1145/501158.501163.
Weng, L.-T., Xu, Y., Li, Y., & Nayak, R. (2008). Exploiting item taxonomy for solving cold-start problem in recommendation making. In Tools with Artificial Intelligence, 2008. ICTAI’08. 20th IEEE International Conference on (Vol. 2, pp. 113–120). IEEE.
Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: use, characteristics, and impact. MIS Quarterly, 31(1), 137–209. Retrieved from http://dl.acm.org/citation.cfm?id=2017335.
Zeithaml, V. A. (1985). The New demographics and market fragmentation. Journal of Marketing, 49(3), 64–75. doi:10.2307/1251616.
The authors would like to thank the seminar participants and students involved in this project. Further, the authors are grateful for all comments and suggestions they received, in particular by the reviewers and audience at ECIS 2013 where we presented parts of this work.
Responsible Editors: Rainer Böhme
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Heimbach, I., Gottschlich, J. & Hinz, O. The value of user’s Facebook profile data for product recommendation generation. Electron Markets 25, 125–138 (2015). https://doi.org/10.1007/s12525-015-0187-9
- Product recommendation
- Cold start problem
- Social shopping sites
- Jell Classification