Skip to main content

The value of user’s Facebook profile data for product recommendation generation

Electronic Markets volume 25, pages 125–138 (2015)Cite this article

Abstract

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.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2

Notes

  1. http://lucene.apache.org/core/index.html

  2. Stop words are common words defined as irrelevant for a search (e.g. “a”, “and”, “the” etc.), cf. e.g. (Jannach et al. 2010, p. 56)

References

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Beane, T. P., & Ennis, D. M. (1987). Market segmentation: a review. European Journal of Marketing, 21(5), 20–42. doi:10.1108/EUM0000000004695.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251. doi:10.2307/1913827.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Nelson, P. (1970). Information and consumer behavior. The Journal of Political Economy, 78(2), 311–329. Retrieved from http://www.jstor.org/stable/1830691.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Zeithaml, V. A. (1985). The New demographics and market fragmentation. Journal of Marketing, 49(3), 64–75. doi:10.2307/1251616.

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. TU Darmstadt/Electronic Markets, Hochschulstr. 1, 64289, Darmstadt, Germany

    Irina Heimbach, Jörg Gottschlich & Oliver Hinz

Authors
  1. Irina Heimbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jörg Gottschlich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oliver Hinz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Irina Heimbach.

Additional information

Responsible Editors: Rainer Böhme

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12525-015-0187-9

Keywords

  • Product recommendation
  • Cold start problem
  • Recommender
  • Facebook
  • Social shopping sites
  • Jell Classification
  • C90
  • M31