Transfer-Learning Based Model for Reciprocal Recommendation

  • Chia-Hsin Ting
  • Hung-Yi Lo
  • Shou-De LinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9652)


This paper tackles the reciprocal recommendation task which has various applications such as online dating, employee recruitment and mentor-mentee matching. The major difference between traditional recommender systems and reciprocal recommender systems is that a reciprocal recommender has to satisfy the preference on both directions. This paper proposes a simple yet novel regularization term, the Mutual-Attraction Indicator, to model the mutual preferences of both parties. Given such indicator, we design a transfer-learning based CF model for reciprocal recommender. The experiments are based on two real world tasks, online dating and human resource matching, showing significantly improved performance over the original factorization model and state-of-the-art reciprocal recommenders.


Reciprocal recommender system Transfer learning 


  1. 1.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: World Wide Web, pp. 285–295 (2001)Google Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  3. 3.
    Parameswaran, R., Blough, D.M.: Privacy preserving collaborative filtering using data obfuscation. In: Granular Computing, pp. 380–387 (2007)Google Scholar
  4. 4.
    Bennett, J., Lanning, S.: The netflix prize. In: Proceedings of KDD Cup and Workshop (2007)Google Scholar
  5. 5.
    Piotte, M., Chabbert, M.: The pragmatic theory solution to the netflix grand prize. In: Netflix Prize Documentation (2009)Google Scholar
  6. 6.
    Koren, Y.: The bellkor solution to the netflix grand prize. Netflix Prize Documentation 81 (2009)Google Scholar
  7. 7.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. Soc. Press 42(8), 30–37 (2009)CrossRefGoogle Scholar
  8. 8.
    Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Pan, W., Liu, N N., Xiang, E.W., Yang, Q.: Transfer learning to predict missing ratings via heterogeneous user feedbacks. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, no. 3, p. 2318 (2011)Google Scholar
  10. 10.
    Cai, X., Bain, M., Krzywicki, A., Wobcke, W., Kim, Y.S., Compton, P., Mahidadia, A.: Collaborative filtering for people to people recommendation in social networks. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 476–485. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Cai, X., et al.: Learning collaborative filtering and its application to people to people recommendation in social networks. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 743–748. IEEE (2010)Google Scholar
  12. 12.
    Kutty, S., Chen, L., Nayak, R.: A people-to-people recommendation system using tensor space models. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 187–192 (2012)Google Scholar
  13. 13.
    Pizzato, L., et al.: RECON: a reciprocal recommender for online dating. In: Proceedings of ACM Conference on Recommender Systems (RecSys). ACM, Barcelona (2010)Google Scholar
  14. 14.
    Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T.: Explicit and implicit user preferences in online dating. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds.) PAKDD Workshops 2011. LNCS, vol. 7104, pp. 15–27. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Celma, O., Cano, P.: From hits to niches? or how popular artists can bias music recommendation and discovery. In: Proceedings of 2nd Netflix-KDD Workshop (2008)Google Scholar
  16. 16.
    Li, B.: Cross-domain collaborative filtering: a brief survey. In: 23rd IEEE International Conference on Tools with Artificial Intelligence, pp. 1085–1086 (2011)Google Scholar
  17. 17.
    Li, B., Yang, Q., Xue, X.: Transfer learning for collaborative filtering via a rating-matrix generative model. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 617–624. ACM (2009)Google Scholar
  18. 18.
    Cao, B., Liu, N.N.: Transfer learning for collective link prediction in multiple heterogeneous domains. In: Proceedings of the 27th International Conference on Machine Learning, pp. 159–166 (2010)Google Scholar
  19. 19.
    Zhang, Y., Cao, B., Yeung, D.-Y.: Multi-domain collaborative filtering. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 725–732 (2010)Google Scholar
  20. 20.
    Shi, Y., Larson, M., Hanjalic, A.: Tags as bridges between domains: improving recommendation with tag-induced cross-domain collaborative filtering. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 305–316. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Pan, W., Xiang, E.W., Liu, N.N., Yang, Q.: Transfer learning in collaborative filtering for sparsity reduction. In: AAAI, pp. 230–235 (2010)Google Scholar
  22. 22.
    Li, B., Zhu, X., et al.: Cross-domain collaborative filtering over time. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, pp. 2293–2298. AAAI Press (2011)Google Scholar
  23. 23.
    Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 43–52. IEEE (2007)Google Scholar
  24. 24.
    Yu, H.-F., et al.: Feature engineering and classifier ensemble for KDD cup 2010. In: Proceedings of the KDD Cup 2010 Workshop, pp. 1–16 (2010)Google Scholar
  25. 25.
    Jing, H., Liang, A.-C., Lin, S.-D., Tsao, Y.: A transfer probabilistic collective factorization model to handle sparse data in collaborative filtering. In: 2014 IEEE International Conference on Data Mining (ICDM), pp. 250–259, 14–17 December 2014Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan

Personalised recommendations