Skip to main content
Log in

A fairness-aware multi-stakeholder recommender system

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Traditional recommender systems mainly focus on the accuracy of recommendation, which lead to recommender systems reinforcing popular items and ignoring lesser-known items. There is increasing evidence that providing good recommendations of surprising items can lead to better user satisfaction. Users may be delightfully surprised if long-tail items are brought to them. Marketplaces need to keep providers satisfied by making sure that their items get enough exposure. In this work, we propose a fairness-aware multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail inclusion, personalized diversity, and recommendation accuracy. Experimental results against real-world datasets show that the proposed method significantly improves the diversity of recommended items in a personalized matter and the coverage of providers with no or minor loss of accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. https://www.booking.com

  2. https://grouplens.org/datasets/movielens/1m/

  3. https://grouplens.org/datasets/movielens/100k/

  4. https://grouplens.org/datasets/movielens/1m/

References

  1. Abbassi, Z., Amer-Yahia, S., Lakshmanan, L.V., Vassilvitskii, S., Yu, C.: Getting recommender systems to think outside the box. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 285–288 (2009)

  2. Abdollahpouri, H.: Incorporating system-level objectives into recommender systems. In: Companion Proceedings of the 2019 World Wide Web Conference, pp. 2–6. ACM (2019)

  3. Abdollahpouri, H., Adomavicius, G., Burke, R., Guy, I., Jannach, D., Kamishima, T., Krasnodebski, J., Pizzato, L.: Beyond personalization: research directions in multistakeholder recommendation. arXiv:1905.01986(2019)

  4. 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)

    Article  Google Scholar 

  5. Anderson, C.: The Long Tail: why the Future of Business is Selling Less of More. Hachette Books, New York (2006). https://en.wikipedia.org/wiki/Hachette_Books

    Google Scholar 

  6. Burke, R., Sonboli, N., Mansoury, M., Ordoñez-gauger, A.: Balanced neighborhoods for fairness-aware collaborative recommendation (2017)

  7. Burke, R.D., Abdollahpouri, H., Mobasher, B., Gupta, T.: Towards multi-stakeholder utility evaluation of recommender systems. In: UMAP (Extended Proceedings) (2016)

  8. Chen, L., Wu, W., He, L.: Personality and recommendation diversity. In: Emotions and Personality in Personalized Services, pp. 201–225. Springer (2016)

  9. Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 39–46. ACM (2010)

  10. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  11. Di Noia, T., Ostuni, V.C., Rosati, J., Tomeo, P., Di Sciascio, E.: An analysis of users’ propensity toward diversity in recommendations. In: Proceedings of the 8th ACM Conference on Recommender systems, pp. 285–288. ACM (2014)

  12. Domingues, M.A., Gouyon, F., Jorge, A.M., Leal, J.P., Vinagre, J., Lemos, L., Sordo, M.: Combining usage and content in an online music recommendation system for music in the long-tail. In: Proceedings of the 21st International Conference on World Wide Web, pp. 925–930. ACM (2012)

  13. Edizel, B., Bonchi, F., Hajian, S., Panisson, A., Tassa, T.: Fairecsys: mitigating algorithmic bias in recommender systems. Int. J. Data Sci. Anal. 9(2), 197–213 (2020)

    Article  Google Scholar 

  14. Garcia-Soriano, D., Bonchi, F.: Maxmin-fair ranking: individual fairness under group-fairness constraints. arXiv:2106.08652 (2021)

  15. Hamedani, E.M., Kaedi, M.: Recommending the long tail items through personalized diversification. Knowl. Based Syst. 164, 348–357 (2019)

    Article  Google Scholar 

  16. Jambor, T., Wang, J.: Optimizing multiple objectives in collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 55–62. ACM (2010)

  17. Kaminskas, M., Bridge, D.: Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems (TiiS) 7(1), 2 (2017)

    Google Scholar 

  18. Kermany, N.R., Alizadeh, S.H.: A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques. Electron. Commer. Res. Appl. 21, 50–64 (2017)

    Article  Google Scholar 

  19. Kermany, N.R., Zhao, W., Yang, J., Wu, J.: Reincre: enhancing collaborative filtering recommendations by incorporating user rating credibility. In: International Conference on Web Information Systems Engineering, pp. 64–72. Springer (2020)

  20. Kermany, N.R., Zhao, W., Yang, J., Wu, J., Pizzato, L.: An ethical multi-stakeholder recommender system based on evolutionary multi-objective optimization. In: 2020 IEEE International Conference on Services Computing (SCC), pp. 478–480. IEEE (2020)

  21. Koren, Y.: The bellkor solution to the netflix grand prize. Netflix Prize Documentation 81(2009), 1–10 (2009)

    Google Scholar 

  22. Kunaver, M., Požrl, T.: Diversity in recommender systems–a survey. Knowl. Based Syst. 123, 154–162 (2017)

    Article  Google Scholar 

  23. Leonhardt, J., Anand, A., Khosla, M.: User fairness in recommender systems. In: Companion Proceedings of the The Web Conference 2018, pp. 101–102 (2018)

  24. Li, Y., Chen, H., Fu, Z., Ge, Y., Zhang, Y.: User-oriented fairness in recommendation. In: Proceedings of the Web Conference 2021, pp. 624–632 (2021)

  25. Liu, W., Burke, R.: Personalizing fairness-aware re-ranking. arXiv:1809.02921 (2018)

  26. Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support. Syst. 74, 12–32 (2015)

    Article  Google Scholar 

  27. Ma, X., Wu, J., Xue, S., Yang, J., Sheng, Q.Z., Xiong, H.: A comprehensive survey on graph anomaly detection with deep learning. arXiv:2106.07178 (2021)

  28. Magalhaes-Mendes, J.: A comparative study of crossover operators for genetic algorithms to solve the job shop scheduling problem. WSEAS Trans. Comput. 12(4), 164–173 (2013)

    Google Scholar 

  29. Mansoury, M.: Fairness-aware recommendation in multi-sided platforms. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 1117–1118 (2021)

  30. Mehrotra, R., McInerney, J., Bouchard, H., Lalmas, M., Diaz, F.: Towards a fair marketplace: counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 2243–2251 (2018)

  31. Miettinen, K.: Nonlinear Multiobjective Optimization, vol. 12. Springer Science & Business Media, Berlin (2012)

    Google Scholar 

  32. Modani, N., Jain, D., Soni, U., Gupta, G.K., Agarwal, P.: Fairness aware recommendations on behance. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 144–155. Springer (2017)

  33. Pang, J., Guo, J., Zhang, W.: Using multi-objective optimization to solve the long tail problem in recommender system. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 302–313. Springer (2019)

  34. Patro, G.K., Biswas, A., Ganguly, N., Gummadi, K.P., Chakraborty, A.: Fairrec: two-sided fairness for personalized recommendations in two-sided platforms. In: Proceedings of The Web Conference 2020, pp. 1194–1204 (2020)

  35. Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5(1), 3–55 (2001)

    Article  MathSciNet  Google Scholar 

  36. Shi, Y., Zhao, X., Wang, J., Larson, M., Hanjalic, A.: Adaptive diversification of recommendation results via latent factor portfolio. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 175–184. ACM (2012)

  37. Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Jin, D., et al.: A comprehensive survey on community detection with deep learning. arXiv:2105.12584 (2021)

  38. Wang, S., Gong, M., Li, H., Yang, J.: Multi-objective optimization for long tail recommendation. Knowl. Based Syst. 104, 145–155 (2016)

    Article  Google Scholar 

  39. Xu, G., Zhang, Y., Yi, X.: Modelling User Behaviour for Web Recommendation Using Lda Model. In: 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 3, pp. 529–532. IEEE (2008)

  40. Yao, W., He, J., Huang, G., Zhang, Y.: Modeling dual role preferences for trust-aware recommendation. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 975–978 (2014)

  41. Yin, H., Cui, B., Li, J., Yao, J., Chen, C.: Challenging the long tail recommendation. Proceedings of the VLDB Endowment 5(9), 896–907 (2012)

    Article  Google Scholar 

  42. Zhu, Z., Hu, X., Caverlee, J.: Fairness-aware tensor-based recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1153–1162. ACM (2018)

  43. Zuo, Y., Gong, M., Zeng, J., Ma, L., Jiao, L.: Personalized recommendation based on evolutionary multi-objective optimization [research frontier]. IEEE Comput. Intell. Mag. 10(1), 52–62 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Naime Ranjbar Kermany.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ranjbar Kermany, N., Zhao, W., Yang, J. et al. A fairness-aware multi-stakeholder recommender system. World Wide Web 24, 1995–2018 (2021). https://doi.org/10.1007/s11280-021-00946-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-021-00946-8

Keywords

Navigation