User Modeling and User-Adapted Interaction

, Volume 23, Issue 5, pp 447–488 | Cite as

Recommending people to people: the nature of reciprocal recommenders with a case study in online dating

  • Luiz Pizzato
  • Tomasz Rej
  • Joshua Akehurst
  • Irena Koprinska
  • Kalina Yacef
  • Judy Kay
Original Paper

Abstract

People-to-people recommenders constitute an important class of recommender systems. Examples include online dating, where people have the common goal of finding a partner, and employment websites where one group of users needs to find a job (employer) and another group needs to find an employee. People-to-people recommenders differ from the traditional items-to-people recommenders as they must satisfy both parties; we call this type of recommender reciprocal. This article is the first to present a comprehensive view of this important recommender class. We first identify the characteristics of reciprocal recommenders and compare them with traditional recommenders, which are widely used in e-commerce websites. We then present a series of studies and evaluations of a content-based reciprocal recommender in the domain of online dating. It uses a large dataset from a major online dating website. We use this case study to illustrate the distinctive requirements of reciprocal recommenders and highlight important challenges, such as the need to avoid bad recommendations since they may make users to feel rejected. Our experiments indicate that, by considering reciprocity, the rate of successful connections can be significantly improved. They also show that, despite the existence of rich explicit profiles, the use of implicit profiles provides more effective recommendations. We conclude with a discussion, linking our work in online dating to the many other domains that require reciprocal recommenders. Our key contributions are the recognition of the reciprocal recommender as an important class of recommender, the identification of its distinctive characteristics and the exploration of how these impact the recommendation process in an extensive case study in the domain of online dating.

Keywords

Recommender systems Online dating Reciprocity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, M., Karimzadehgan, M., Zhai, C.: An online news recommender system for social networks. In: Proceedings of the Workshop on Search in Social Media (SSM 2009), co-located with ACM SIGIR 2009 Conference on Information Retrieval, Boston, July 23 2009Google Scholar
  2. Akehurst, J.: A hybrid content-collaborative reciprocal recommender. Honours thesis, School of Information Technologies, The University of Sydney (2010)Google Scholar
  3. Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T.: CCR—a content-collaborative reciprocal recommender for online dating. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI 2011), Barcelona, Spain, July 2011Google Scholar
  4. 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., Bailey, J., Koh, Y., Luo, J. (eds.), New Frontiers in Applied Data Mining, vol. 7104 of Lecture Notes in Computer Science, pp. 15–27. Springer, Berlin (2012). ISBN 978-3-642-28319-2Google Scholar
  5. Amatriain, X., Pujol, J.M., Oliver, N.: I like it. . .i like it not: evaluating user ratings noise in recommender systems. In: UMAP ’09, pp. 247–258. Springer, Berlin (2009a). ISBN 978-3-642-02246-3Google Scholar
  6. Amatriain, X., Pujol, J.M., Tintarev, N., Oliver, N.: Rate it again: increasing recommendation accuracy by user re-rating. In: RecSys ’09: Proceedings of the third ACM Conference on Recommender Systems, pp. 173–180. ACM, New York (2009b). ISBN 978-1-60558-435-5Google Scholar
  7. Bansal, N., Gupta, A., Li, J., Mestre, J., Nagarajan, V., Rudra, A.: When lp is the cure for your matching woes: improved bounds for stochastic matchings. In: de Berg, M., Meyer, U. (eds.), Algorithms ESA 2010, vol. 6347 of Lecture Notes in Computer Science, pp. 218–229. Springer, Berlin (2010)Google Scholar
  8. Blecker, T., Abdelkafi, N., Kreutler, G., Friedrich, G. An advisory system for customers’ objective needs elicitation in mass customization. In: 4th Workshop on Information Systems for Mass Customization (ISMC 2004) at the fourth International ICSC Symposium on Engineering of Intelligent Systems (EIS 2004), University of Madeira, Funchal/Portugal, 28 February–3 March 2004, S. 1–10Google Scholar
  9. Brožovský, L., Petříček, V.: Recommender system for online dating service. CoRR, abs/cs/0703042 (2007)Google Scholar
  10. Bull, S., Greer, J.E., McCalla, G.I., Kettel, L., Bowes, J.: User modelling in i-help: what, why, when and how. In: User Modeling, vol. 2109 of Lecture Notes in Computer Science, pp. 117–126. Springer, Heidelberg (2001). ISBN 3-540-42325-7Google Scholar
  11. Burke, R.: Hybrid recommender systems: Survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002). ISSN 0924-1868. doi:10.1023/A:1021240730564
  12. Cai, X., Bain, M., Krzywicki, A., Wobcke, W., Kim, Y.S., Compton, P., Mahidadia, A.: Learning collaborative filtering and its application to people to people recommendation in social networks. In: Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM ’10, pp. 743–748. IEEE Computer Society, Washington (2010). ISBN 978-0-7695-4256-0Google Scholar
  13. 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: AI 2010: Advances in Artificial Intelligence, vol. 6464/2011 of Lecture Notes in Computer Science, pp. 476–485. Springer, Berlin (2011)Google Scholar
  14. Chen, J., Geyer, W., Dugan, C., Muller, M., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: CHI ’09, pp. 201–210. ACM, New York (2009a). ISBN 978-1-60558-246-7Google Scholar
  15. Chen, N., Immorlica, N., Karlin, A.R., Mahdian, M., Rudra, A. Approximating matches made in heaven. In: Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I, ICALP ’09, pp. 266–278. Springer, Berlin (2009b). ISBN 978-3-642-02926-4Google Scholar
  16. Diaz, F., Metzler, D., Amer-Yahia, S.: Relevance and ranking in online dating systems. In SIGIR ’10: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 66–73. ACM, New York (2010). ISBN 978-1-4503-0153-4Google Scholar
  17. Fazel-Zarandi, M., Devlin, H.J., Huang, Y., Contractor, N. Expert recommendation based on social drivers, social network analysis, and semantic data representation. In: Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec ’11, pp. 41–48. ACM, New York (2011). ISBN 978-1-4503-1027-7Google Scholar
  18. Freyne, J., Jacovi, M., Guy, I., Geyer, W.: Increasing engagement through early recommender intervention. In: RecSys ’09: Proceedings of the Third ACM Conference on Recommender Systems, pp. 85–92. ACM, New York, (2009). ISBN 978-1-60558-435-5Google Scholar
  19. Gale D., Shapley L.S.: College admissions and the stability of marriage. Am. Math. Monthly 69(1), 9–15 (1962) ISSN 0002-9890MathSciNetCrossRefMATHGoogle Scholar
  20. Garcia, R., Amatriain, X.: Weighted content based methods for recommending connections in online social networks. In: Geyer, W., Freyne, J., Mobasher, B., Anand, S. S. (eds.), Proceedings of the 2nd ACM RecSys’10 Workshop on Recommender Systems and the Social Web, pp. 68–71. Barcelona, Spain (2010)Google Scholar
  21. Gelain, M., Pini, M.S., Rossi, F., Venable, K.B., Walsh, T.: Local search for stable marriage problems with ties and incomplete lists. CoRR, abs/1007.0637 (2010)Google Scholar
  22. Greer J., McCalla G., Collins J., Kumar V., Meagher P., Vassileva J.: Supporting peer help and collaboration in distributed workplace environments. Int. J. Artif. Intell. Educ. 9(1998), 159–177 (1998)Google Scholar
  23. Gunawardana A., Shani G.: A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 10, 2935–2962 (2009) ISSN 1532-4435MathSciNetMATHGoogle Scholar
  24. Joachims, T., Granka, L., Pan, B., Hembrooke, H.,Gay, G.: Accurately interpreting clickthrough data as implicit feedback. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’05, pp. 154–161. ACM, New York (2005). ISBN 1-59593-034-5Google Scholar
  25. Kim, Y.S., Mahidadia, A., Compton, P., Cai, X., Bain, M., Krzywicki, A., Wobcke, W.: People recommendation based on aggregated bidirectional intentions in social network site. In: Knowledge Management and Acquisition for Smart Systems and Services, vol. 6232/2010 of Lecture Notes in Computer Science, pp. 247–260. Springer, Berlin (2010)Google Scholar
  26. Kutty, S., Chen, L., Nayak, R.: A people-to-people recommendation system using tensor space models. In: Shin, D. (ed.), ACM Service-Oriented Architectures and Programming: 27th Annual ACM Symposium on Applied Computing (SAC). ACM Press, Riva del Garda (2012)Google Scholar
  27. Malinowski, J., Keim, T., Wendt, O., Weitzel, T.: Matching people and jobs: a bilateral recommendation approach. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences, vol. 6, p. 137c. IEEE Computer Society, Washington (2006)Google Scholar
  28. McFee, B., Lanckriet, G.: Metric learning to rank. In: Proceedings of the 27th International Conference on Machine Learning (ICML’10), June 2010Google Scholar
  29. Park, S.T., Chu, W.: Pairwise preference regression for cold-start recommendation. In: RecSys ’09: Proceedings of the Third ACM Conference on Recommender Systems, pp. 21–28. ACM, New York (2009). ISBN 978-1-60558-435-5Google Scholar
  30. Park, Y.-J., Tuzhilin, A.: The long tail of recommender systems and how to leverage it. In: RecSys ’08, pp. 11–18. ACM, New York (2008). ISBN 978-1-60558-093-7Google Scholar
  31. Patil, A.N.: Homophily based link prediction in social networks. Technical report, Department of Computer Science, Stony Brook University (2009). http://www.cs.stonybrook.edu/~akshay/rpe.pdf
  32. Pazzani M.J.: A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13(5–6), 393–408 (1999) ISSN 0269-2821CrossRefGoogle Scholar
  33. Pizzato, L., Chung, T., Rej, T., Koprinska, I., Yacef, K., Kay, J.: Learning user preference in online dating. In: Hüllermeier, E., Fürnkranz, J. (eds.), Proceedings of the Preference Learning (PL-10) Tutorial and Workshop, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), September 2010a. http://www.ke.tu-darmstadt.de/events/PL-10/papers/8-Pizzato.pdf
  34. Pizzato, L., Rej, T., Chung, T., Koprinska, I., Kay, J.: RECON: a reciprocal recommender for online dating. In: RecSys ’10: Proceedings of the fourth ACM Conference on Recommender Systems, pp. 207–214. ACM, New York (2010b). ISBN 978-1-60558-906-0Google Scholar
  35. Pizzato, L., Rej, T., Chung, T., Yacef, K., Koprinska, I., Kay, J.: Reciprocal recommenders. In: 8th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, UMAP’2010, Hawaii, USA, 20–24 June (2010c)Google Scholar
  36. Pizzato, L., Rej, T., Yacef, K., Koprinska, I., Kay, J.: Finding someone you will like and who won’t reject you. In: Proceedings of the 19th International Conference on User Modeling, Adaptation and Personalization (UMAP 2011), Girona, Spain, July 2011Google Scholar
  37. Pizzato, L.A., Silvestrini, C.: Stochastic matching and collaborative filtering to recommend people to people. In: Proceedings of the fifth ACM conference on Recommender systems, RecSys ’11, pp. 341–344. ACM, New York (2011). ISBN 978-1-4503-0683-6Google Scholar
  38. Reuters: Time constraints boost popularity of online dating. Reuters Life!, 25 January 2010. http://www.reuters.com/article/idUSTRE60O4PC2010012. Accessed 20 Oct 2010
  39. Richards, D., Taylor, M., Busch, P.: Expertise recommendation: A two-way knowledge communication channel. In: International Conference on Autonomic and Autonomous Systems, pp. 35–40, 2008Google Scholar
  40. Ronn, E.: Np-complete stable matching problems. J. Algorithms 11(2), 285–304 (1990). ISSN 0196-6774Google Scholar
  41. Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Applications of dimensionality reduction in recommender systems—a case study. In: WebKDD Workshop at the ACM SIGKKD. ACM, New York (2000)Google Scholar
  42. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: SIGIR ’02, pp. 253–260 (2002). ISBN 1-58113-561-0Google Scholar
  43. Sinha, R., Swearingen, K.: The role of transparency in recommender systems. In: CHI ’02 Extended Abstracts on Human Factors in Computing Systems, CHI ’02, pp. 830–831. ACM, New York (2002). ISBN 1-58113-454-1Google Scholar
  44. Sinha, R.R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries (2001)Google Scholar
  45. Terveen L., McDonald D.W.: Social matching: a framework and research agenda. ACM Trans. Comput.-Hum. Interact. 12(3), 401–434 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Luiz Pizzato
    • 1
  • Tomasz Rej
    • 1
  • Joshua Akehurst
    • 1
  • Irena Koprinska
    • 1
  • Kalina Yacef
    • 1
  • Judy Kay
    • 1
  1. 1.Computer Human Adapted Interaction (CHAI), School of Information TechnologiesUniversity of SydneyNSWAustralia

Personalised recommendations