Electronic Commerce Research

, Volume 19, Issue 2, pp 285–310 | Cite as

Incorporating facial attractiveness in photos for online dating recommendation

  • Zhihong Li
  • Yining Song
  • Xiaoying XuEmail author


Data sparsity has been a great challenge of data-driven applications. It is essential to explore the availability of other side information that can be utilized for alleviating this problem. This study proposes incorporating facial attractiveness embedded in user photos to boost recommendations in the context of online dating site, aiming at demonstrating the possibility of utilizing image features for increasing data richness. Specifically, subjective and objective grading methods are proposed to extract the facial attractiveness from user photos. A user network is then constructed, and a link prediction method is proposed to incorporate the extracted facial attractiveness in the recommendation process. Evaluation conducted on a real-world dataset shows that the proposed CNAF method is effective in increasing the prediction accuracy for the cold-start users. In particular, the prediction errors of the proposed CNAF method are on average 8.68%, 8.79%, and 8.71% lower than the systems using the Adamic–Adar index, resource allocation index, and preference attachment index respectively. The proposed CNAF method also maintains a high recommendation diversity.


Facial attractiveness Link prediction Recommendation Information system Online dating Image information 



We gratefully acknowledge the funding support from the National Natural Science Foundation of China (Grant 71571073 and Grant 71601081), the Guangdong Natural Science Foundation (Grant 2016A030310426) and the Fundamental Research Funds for the Central Universities (Grant 2017BQ048).

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


  1. 1.
    Brozovsky, L., & Petricek, V. (2007). Recommender system for online dating service. In Proceedings of znalosti 2007 conference, Ostrava. Google Scholar
  2. 2.
    Pizzato, L., Rej, T., Chung, T., Koprinska, I., & Kay, J. (2010). RECON: A reciprocal recommender for online dating. In ACM conference on recommender systems (pp. 207–214).Google Scholar
  3. 3.
    Niu, J., Wang, L., Liu, X., & Yu, S. (2016). FUIR: Fusing user and item information to deal with data sparsity by using side information in recommendation systems. Journal of Network & Computer Applications, 70, 41–50.Google Scholar
  4. 4.
    Guo, G., Qiu, H., Tan, Z., Liu, Y., Ma, J., & Wang, X. (2017). Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems. Knowledge-Based Systems, 138, 202–207.Google Scholar
  5. 5.
    Diaz, F., Metzler, D., & Amer-Yahia, S. (2010.)Relevance and ranking in online dating systems. In International ACM SIGIR conference on research and development in information retrieval (pp. 66–73).Google Scholar
  6. 6.
    Pourgholamali, F., Kahani, M., Bagheri, E., & Noorian, Z. (2017). Embedding unstructured side information in product recommendation. Electronic Commerce Research and Applications, 25, 70–85.Google Scholar
  7. 7.
    Nilashi, M., Ibrahim, O., & Bagherifard, K. (2018). A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications, 92, 507–520.Google Scholar
  8. 8.
    Xia, P., Zhai, S., Liu, B., Sun, Y., & Chen, C. (2016). Design of reciprocal recommendation systems for online dating. Social Network Analysis & Mining, 6(1), 32.Google Scholar
  9. 9.
    Ong, D., & Wang, J. (2016). Income attraction: An online dating field experiment. Applied Economics, 111(19), 13–22.Google Scholar
  10. 10.
    Whyte, S., & Torgler, B. (2017). Things change with age: Educational assortment in online dating. Personality and Individual Differences, 109, 5–11.Google Scholar
  11. 11.
    Clemens, C., Atkin, D., & Krishnan, A. (2015). The influence of biological and personality traits on gratifications obtained through online dating websites. Computers in Human Behavior, 49(C), 120–129.Google Scholar
  12. 12.
    Kang, T., & Hoffman, L. H. (2011). Why would you decide to use an online dating site? Factors that lead to online dating. Communication Research Reports, 28(3), 205–213.Google Scholar
  13. 13.
    Zhang, S., Lee, D., Singh, P. V., & Srinivasan, K. (2016). How much is an image Worth? An empirical analysis of property’s image aesthetic quality on demand at AirBNB. In proceedings of international conference on information systems, Dublin, Ireland. Google Scholar
  14. 14.
    Chiang, C. I., & Saw, Y. L. (2018). Do good looks matter when applying for jobs in the hospitality industry? International Journal of Hospitality Management, 71, 33–40.Google Scholar
  15. 15.
    Talamas, S. N., Mavor, K. I., & Perrett, D. I. (2016). Blinded by beauty: Attractiveness bias and accurate perceptions of academic performance. PLoS ONE, 11(2), e0148284.Google Scholar
  16. 16.
    Kenealy, P., Frude, N., & Shaw, W. (2010). Influence of children’s physical attractiveness on teacher expectations. Journal of Social Psychology, 128(3), 373–383.Google Scholar
  17. 17.
    Geiler, P., Renneboog, L., & Zhao, Y. (2018). Beauty and appearance in corporate director elections. Journal of International Financial Markets, Institutions and Money.
  18. 18.
    Islam, S., Taylor, C. J., & Hayter, J. P. (2017). Analysis of facial morphology of UK and US general election candidates: Does the ‘power face’ exist? Journal of Plastic, Reconstructive and Aesthetic Surgery, 70(7), 15.Google Scholar
  19. 19.
    Bekk, M., Spörrle, M., Völckner, F., Spieß, E., & Woschée, R. (2017). What is not beautiful should match: How attractiveness similarity affects consumer responses to advertising. Marketing Letters, 28(9), 1–14.Google Scholar
  20. 20.
    Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33.Google Scholar
  21. 21.
    Tu, K., Ribeiro, B., Jensen, D., Towsley, D., Liu, B., Jiang, H., et al. (2014). Online dating recommendations: matching markets and learning preferences. In International conference on World Wide Web. (pp. 787–792).Google Scholar
  22. 22.
    Darwin, C. (2010). The descent of man and selection in relation to sex (new ed.). Journal of the Anthropological Society of Nippon, 22(2357), 13–34.Google Scholar
  23. 23.
    Rodrigues, D., Lopes, D., Alexopoulos, T., & Goldenberg, L. (2017). A new look at online attraction: Unilateral initial attraction and the pivotal role of perceived similarity. Computers in Human Behavior, 74, 16–25.Google Scholar
  24. 24.
    Fiore, A. T., Taylor, L. S., Mendelsohn, G. A., & Hearst, M. (2008). Assessing attractiveness in online dating profiles. In Sigchi conference on human factors in computing systems (pp. 797–806).Google Scholar
  25. 25.
    Eisenthal, Y., Dror, G., & Ruppin, E. (2006). Facial attractiveness: Beauty and the machine. Neural Computation, 18(1), 119–142.Google Scholar
  26. 26.
    Fan, J., Chau, K. P., Wan, X., Zhai, L., & Lau, E. (2012). Prediction of facial attractiveness from facial proportions. Pattern Recognition, 45(6), 2326–2334.Google Scholar
  27. 27.
    Zhang, L., Zhang, D., Sun, M. M., & Chen, F. M. (2017). Facial beauty analysis based on geometric feature: Toward attractiveness assessment application. Expert Systems with Applications, 82, 252–265.Google Scholar
  28. 28.
    Huang, Z., & Zeng, D. D. (2007). a link prediction approach to anomalous email detection. In IEEE international conference on systems, man and cybernetics (pp. 1131–1136).Google Scholar
  29. 29.
    Raeder, T., Lizardo, O., Hachen, D., & Chawla, N. V. (2011). Predictors of short-term decay of cell phone contacts in a large scale communication network. Social Networks, 33(4), 245–257.Google Scholar
  30. 30.
    Folino, F., & Pizzuti, C. (2012). Link prediction approaches for disease networks. In C. Böhm, K. L. Lhotská, & M. E. Renda (Eds.), Information technology in Bio- and medical informatics (pp. 99–108). Berlin, Heidelberg: Springer.Google Scholar
  31. 31.
    Pujari, M., & Kanawati, R. (2015). Link prediction in multiplex networks. Networks & Heterogeneous Media, 10(1), 17–35.Google Scholar
  32. 32.
    Zhang, J. (2016). Uncovering mechanisms of co-authorship evolution by multirelations-based link prediction. Information Processing and Management, 53(1), 19.Google Scholar
  33. 33.
    Hristova, D., Noulas, A., Brown, C., Musolesi, M., & Mascolo, C. (2016). A multilayer approach to multiplexity and link prediction in online geo-social networks. Epj Data Science, 5(1), 24.Google Scholar
  34. 34.
    Moradabadi, B., & Meybodi, M. R. (2018). Link prediction in weighted social networks using learning automata. Engineering Applications of Artificial Intelligence, 70, 16–24.Google Scholar
  35. 35.
    Broer, P. N., Juran, S., Liu, Y. J., Weichman, K., Tanna, N., Walker, M. E., et al. (2014). The impact of geographic, ethnic, and demographic dynamics on the perception of beauty. Journal of Craniofacial Surgery, 25(2), e157.Google Scholar
  36. 36.
    Vashi, N. A., & Quay, E. R. (2015). Subjective Aspects of Beauty. In N. A. Vashi (Ed.), Beauty and body dysmorphic disorder: A clinician’s guide (pp. 63–81). Cham: Springer International Publishing.Google Scholar
  37. 37.
    Jabr, W., Mookerjee, R., Tan, Y., & Mookerjee, V. S. (2014). Leveraging philanthropic behavior for customer support: the case of user support forums. MIS Quarterly, 38(1), 187–208.Google Scholar
  38. 38.
    Asthana, A., Zafeiriou, S., Cheng, S., & Pantic, M. (2013). Robust discriminative response map fitting with constrained local models. In Computer vision and pattern recognition (pp. 3444–3451).Google Scholar
  39. 39.
    Gunawardana, A., & Shani, G. (2015). Evaluating Recommender Systems. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender systems handbook (pp. 265–308). Boston, MA: Springer.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Business AdministrationSouth China University of TechnologyGuangzhouPeople’s Republic of China

Personalised recommendations