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Multistakeholder recommendation: Survey and research directions

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Abstract

Recommender systems provide personalized information access to users of Internet services from social networks to e-commerce to media and entertainment. As is appropriate for research in a field with a focus on personalization, academic studies of recommender systems have largely concentrated on optimizing for user experience when designing, implementing and evaluating their algorithms and systems. However, this concentration on the user has meant that the field has lacked a systematic exploration of other aspects of recommender system outcomes. A user-centric approach limits the ability to incorporate system objectives, such as fairness, balance, and profitability, and obscures concerns that might come from other stakeholders, such as the providers or sellers of items being recommended. Multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article outlines the multistakeholder perspective on recommendation, highlighting example research areas and discussing important issues, open questions, and prospective research directions.

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  1. https://data.donorschoose.org/explore-our-impact/.

References

  • Abdollahpouri, H., Burke, R.: Multi-stakeholder recommendation and its connection to multi-sided fairness. In: Workshop on Recommendation in Multi-stakeholder Environments (RMSE’19), in Conjunction with the 13th ACM Conference on Recommender Systems, RecSys’19 (2019)

  • Abdollahpouri, H., Burke, R., Mobasher, B.: Controlling popularity bias in learning to rank recommendation. In: Proceedings of the 11th ACM Conference on Recommender Systems (2017a)

  • Abdollahpouri, H., Burke, R., Mobasher, B.: Recommender systems as multistakeholder environments. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP’17) (2017b)

  • Abdollahpouri, H., Burke, R., Mobasher, B..: Managing popularity bias in recommender systems with personalized re-ranking. In: The Thirty-Second International Flairs Conference (2019a)

  • Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B.: The unfairness of popularity bias in recommendation. In: RecSys Workshop on Recommendation in Multi-stakeholder Environments (2019b)

  • Adamopoulos, P., Tuzhilin, A.: The business value of recommendations: a privacy-preserving econometric analysis. In: Proceedings of ICIS ’15 (2015)

  • Agarwal, D., Chen, B.-C., Elango, P., Wang, X.: Click shaping to optimize multiple objectives. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’11, pp. 132–140. ACM, New York, NY, USA (2011)

  • Agarwal, D., Chen, B.-C., Elango, P., Wang, X.: Personalized click shaping through Lagrangian duality for online recommendation. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR ’12, pp. 485–494. ACM, New York, NY, USA (2012)

  • 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 International Joint Conference on Artificial Intelligence (IJCAI) (2011)

  • Akoglu, L., Faloutsos, C.: ValuePick: towards a value-oriented dual-goal recommender system. In: ICDM ’10 Workshops, pp. 1151–1158 (2010)

  • Alanazi, A., Bain, M.: A scalable people-to-people hybrid reciprocal recommender using hidden Markov models. In: 2nd International Workshop on Machine Learning Methods for Recommender Systems (2016)

  • Arrow, K.J., Sen, A., Suzumura, K.: Handbook of Social Choice and Welfare, vol. 2. Elsevier, Amsterdam (2010)

    MATH  Google Scholar 

  • Azaria, A., Hassidim, A., Kraus, S., Eshkol, A., Weintraub, O., Netanely, I.: Movie recommender system for profit maximization. In: Proceedings of RecSys ’13, pp. 121–128 (2013)

  • Bateni, M.H., Chen, Y., Ciocan, D., Mirrokni, V.: Fair Resource Allocation in a Volatile Marketplace (2018). https://doi.org/10.2139/ssrn.2789380

  • Beutel, A., Chen, J., Doshi, T., Qian, H., Wei, L., Wu, Y., Heldt, L., Zhao, Z,, Hong, L., Chi, E.H., et al.: Fairness in recommendation ranking through pairwise comparisons. arXiv preprint arXiv:1903.00780 (2019)

  • Bodapati, A.V.: Recommendation systems with purchase data. J. Mark. Res. 45(1), 77–93 (2008)

    Article  Google Scholar 

  • Breese, J.S., Heckerman, D., Kadie, C., et al.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)

  • Burke, R.D., Hammond, K.J., Yound, B.C.: The FindMe approach to assisted browsing. IEEE Expert 12(4), 32–40 (1997)

    Article  Google Scholar 

  • Burke, R.: Multisided fairness for recommendation. In: Workshop on Fairness, Accountability and Transparency in Machine Learning (FATML), p. 5 (2017)

  • Burke, R., Abdollahpouri, H.: Educational recommendation with multiple stakeholders. In: Third International Workshop on Educational Recommender Systems (2016)

  • Burke, R., Abdollahpouri, H., Mobasher, B., Gupta, T.: Towards multi-stakeholder utility evaluation of recommender systems. In: Proceedings of the International Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP 2016). ACM (2016)

  • Burke, R., Sonboli, N., Mansoury, M., Ordoñez-Gauger, A.: Balanced neighborhoods for fairness-aware collaborative recommendation. In: Workshop on Responsible Recommendation (FATRec) (2017)

  • Burke, R., Sonboli, N., Ordonez-Gauger, A.: Balanced neighborhoods for multi-sided fairness in recommendation. In: Conference on Fairness, Accountability and Transparency, pp. 202–214 (2018a)

  • Burke, R., Kontny, J., Sonboli, N.: Synthetic attribute data for evaluating consumer-side fairness. arXiv preprint arXiv:1809.04199 (2018b)

  • Calders, T., Verwer, S.: Three naive Bayes approaches for discrimination-free classification. Data Min. Knowl. Disc. 21, 277–292 (2010)

    Article  MathSciNet  Google Scholar 

  • Campos, P.G., Díez, F., Cantador, I.: Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model. User Adapt. Interact. 24(1), 67–119 (2014)

    Article  Google Scholar 

  • Chau, P.Y.K., Ho, S.Y., Ho, K.K.W., Yao, Y.: Examining the effects of malfunctioning personalized services on online users’ distrust and behaviors. Decis. Support Syst. 56(C), 180–191 (2013)

    Article  Google Scholar 

  • Chen, J., Geyer, W., Dugan, C., Muller, M., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI ’09, pp. 201–210. ACM, New York, NY, USA (2009)

  • Chen, L.-S., Hsu, F.-H., Chen, M.-C., Hsu, Y.-C.: Developing recommender systems with the consideration of product profitability for sellers. Inf. Sci. 178(4), 1032–1048 (2008)

    Article  Google Scholar 

  • Chen, P.-Y.S., Wu, S., Yoon, J.: The impact of online recommendations and consumer feedback on sales. In: Proceedings of ICIS ’04, pp. 711–724 (2004)

  • Daly, E.M., Geyer, W., Millen, D.R.: The network effects of recommending social connections. In: Proceedings of the Fourth ACM Conference on Recommender Systems. RecSys ’10, pp. 301–304. ACM, New York, NY, USA (2010)

  • Das, A., Mathieu, C., Ricketts, D.: Maximizing profit using recommender systems. CoRR abs/0908.3633 (2009)

  • Ekstrand, M.D., Tian, M., Azpiazu, I.M., Ekstrand, J.D., Anuyah, O., McNeill, D., Pera, M.S.: All the cool kids, how do they fit in? Popularity and demographic biases in recommender evaluation and effectiveness. In: Conference on Fairness, Accountability and Transparency, pp. 172–186 (2018a)

  • Ekstrand, M.D., Tian, M., Kazi, M.R.I., Mehrpouyan, H., Kluver, D.: Exploring author gender in book rating and recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 242–250. ACM (2018b)

  • Evans, D.S., Schmalensee, R., Noel, M.D., Chang, H.H., Garcia-Swartz, D.D.: Platform economics: essays on multi-sided businesses. In: Evans, D.S. (ed.) Platform Economics: Essays on Multi-Sided Businesses. Competition Policy International (2011). https://ssrn.com/abstract=1974020

  • Evans, D.S., Schmalensee, R.: Matchmakers: The New Economics of Multisided Platforms. Harvard Business Review Press, Brighton (2016)

    Google Scholar 

  • Farnadi, G., Kouki, P., Thompson, S.K., Srinivasan, S., Getoor, L.: A fairness-aware hybrid recommender system. Presented at the 2nd FATRec Workshop on Responsible Recommendation held at RecSys 2018, Vancouver, CA (2018)

  • Fitzsimons, G.J., Lehmann, D.R.: Reactance to recommendations: when unsolicited advice yields contrary responses. Mark. Sci. 23(1), 82–94 (2004)

    Article  Google Scholar 

  • Freeman, R.E.: Strategic Management: A Stakeholder Approach. Cambridge University Press, Cambridge (2010)

    Book  Google Scholar 

  • Freyne, J., Jacovi, M., Guy, I., Geyer, W.: Increasing engagement through early recommender intervention. In: Proceedings of the Third ACM Conference on Recommender Systems. RecSys ’09, pp. 85–92. ACM, New York, NY, USA (2009)

  • Garcia, I., Sebastia, L., Onaindia, E.: On the design of individual and group recommender systems for tourism. Expert Syst. Appl. 38(6), 7683–7692 (2011)

    Article  Google Scholar 

  • Garcin, F., Faltings, B., Donatsch, O., Alazzawi, A., Bruttin, C., Huber, A.: Offline and online evaluation of news recommender systems at Swissinfo.ch. In: Proceedings of RecSys ’14, pp. 169–176 (2014)

  • Ghazanfar, M.A., Prügel-Bennett, A.: Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Syst. Appl. 41(7), 3261–3275 (2014)

    Article  Google Scholar 

  • Goodpaster, K.E.: Business ethics and stakeholder analysis. Bus. Ethics Quart. 1, 53–73 (1991)

    Article  Google Scholar 

  • Goswami, A., Hedayati, F., Mohapatra, P.: Recommendation systems for markets with two sided preferences. In: 2014 13th International Conference on Machine Learning and Applications, pp. 282–287 (2014)

  • Guy, I.: People recommendation on social media. In: Brusilovsky, P., He, D. (eds.) Social Information Access: Systems and Technologies, pp. 570–623. Springer International Publishing, Cham (2018)

    Chapter  Google Scholar 

  • Guy, I., Ronen, I., Wilcox, E.: Do you know? Recommending people to invite into your social network. In: Proceedings of the 14th International Conference on Intelligent User Interfaces. IUI ’09, pp. 77–86. ACM, New York, NY, USA (2009)

  • Hammar, M., Karlsson, R., Nilsson, B.J.: Using maximum coverage to optimize recommendation systems in e-commerce. In: Proceedings of RecSys ’13, pp. 265–272 (2013)

  • Hosanagar, K., Krishnan, R., Ma, L.: Recommended for you: the impact of profit incentives on the relevance of online recommendations. In: Proceedings of ICIS ’08 (2008)

  • Iyer, G., Soberman, D., Villas-Boas, J.M.: The targeting of advertising. Mark. Sci. 24(3), 461–476 (2005)

    Article  Google Scholar 

  • Jambor, T., Wang, J.: Optimizing multiple objectives in collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems. RecSys ’10, pp. 55–62. ACM, New York, NY, USA (2010)

  • Jannach, D., Adomavicius, G.: Recommendations with a purpose. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 7–10. ACM (2016)

  • Jannach, D., Adomavicius, G.: Price and profit awareness in recommender systems. In: Proceedings of the ACM RecSys 2017 Workshop on Value-Aware and Multi-stakeholder Recommendation (2017)

  • Jannach, D., Hegelich, K.: A case study on the effectiveness of recommendations in the mobile internet. In: Proceedings of RecSys ’09, pp. 205–208 (2009)

  • Jannach, D., Lerche, L., Jugovac, M.: Item familiarity as a possible confounding factor in user-centric recommender systems evaluation. i-com J. Interact. Media 14(1), 29–39 (2015)

    Google Scholar 

  • Jiang, Y., Liu, Y.: Optimization of online promotion: a profit-maximizing model integrating price discount and product recommendation. Int. J. Inf. Technol. Decis. Mak. 11(05), 961–982 (2012)

    Article  Google Scholar 

  • Kamishima, T., Akaho, S.: Personalized pricing recommender system—multi-stage epsilon-greedy approach. In: The 2nd Int’l Workshop on Information Heterogeneity and Fusion in Recommender Systems (2011)

  • Kamishima, T., Akaho, S., Asoh, H., Sakuma, J.: Efficiency improvement of neutrality-enhanced recommendation. In: The 3rd Workshop on Human Decision Making in Recommender Systems (2013)

  • Kamishima, T., Akaho, S., Asoh, H., Sato, I.: Model-based approaches for independence-enhanced recommendation. In: Proceedings of the IEEE 16th Int’l Conference on Data Mining Workshops, pp. 860–867 (2016)

  • Kamishima, T., Akaho, S., Asoh, H., Sakuma, J.: Recommendation independence. In: Conference on Fairness, Accountability and Transparency. PMLR, vol. 81, pp. 187–201 (2018)

  • Kamishima, T., Akaho, S.: Considerations on recommendation independence for a find-good-items task. In: Workshop on Responsible Recommendation (FATRec) (2017)

  • Kamishima, T., Akaho, S., Asoh, H., Sakuma, J.: Fairness-aware classifier with prejudice remover regularizer. In: Proceedings of the ECML PKDD 2012, Part II, pp. 35–50 (2012)

  • Kamishima, T., Akaho, S., Asoh, H., Sakuma, J.: Correcting popularity bias by enhancing recommendation neutrality. In: Poster Proceedings of the 8th ACM Conference on Recommender Systems, RecSys 2014, Foster City, Silicon Valley, CA, USA, October 6–10 (2014)

  • Karako, C., Manggala, P.: Using image fairness representations in diversity-based re-ranking for recommendations. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 23–28. ACM (2018a)

  • Karako, C., Manggala, P.: Using image fairness representations in diversity-based re-ranking for recommendations. Presented at the 2nd FATRec Workshop on Responsible Recommendation held at RecSys 2018, Vancouver, CA (2018b)

  • Kensing, F., Blomberg, J.: Participatory design: issues and concerns. Comput. Support. Coop. Work 7(3–4), 167–185 (1998)

    Article  Google Scholar 

  • Kirshenbaum, E., Forman, G., Dugan, M.: A live comparison of methods for personalized article recommendation at Forbes.com. In: Proceedings of ECML/PKDD ’12, pp. 51–66 (2012)

  • Kivran-Swaine, F., Govindan, P., Naaman, M.: The impact of network structure on breaking ties in online social networks: unfollowing on Twitter. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI ’11, pp. 1101–1104. ACM, New York, NY, USA (2011)

  • Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–7 (1997)

    Article  Google Scholar 

  • Krasnodebski, J., Dines, J.: Considering supplier relations and monetization in designing recommendation systems. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 381–382. ACM (2016)

  • Kwak, H., Chun, H., Moon, S.: Fragile online relationship: a first look at unfollow dynamics in Twitter. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI ’11, pp. 1091–1100. ACM, New York, NY, USA (2011)

  • Leavitt, N.: Recommendation technology: will it boost e-commerce? Computer 39(5), 13–16 (2006)

    Article  Google Scholar 

  • Lee, E.L., Lou, J.-K., Chen, W.-M., Chen, Y.-C., Lin, S.-D., Chiang, Y.-S., Chen, K.-T.: Fairness-aware loan recommendation for microfinance services. In: Proceedings of the 2014 International Conference on Social Computing, p. 3. ACM (2014)

  • Li, L., Li, T.: MEET: a generalized framework for reciprocal recommender systems. In: Proceedings of the 21st ACM international conference on information and knowledge management. CIKM ’12, pp. 35–44. ACM, New York, NY, USA (2012)

  • Lin, Y., Jessurun, J., De Vries, B., Timmermans, H.: Motivate: towards context-aware recommendation mobile system for healthy living. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 250–253. IEEE (2011)

  • Liu, D.-R., Shih, Y.-Y.: Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences. J. Syst. Softw. 77(2), 181–191 (2005)

    Article  MathSciNet  Google Scholar 

  • Liu, W., Burke, R.: Personalizing fairness-aware re-ranking. Presented at the 2nd FATRec Workshop held at RecSys 2018, Vancouver, CA. arXiv preprint arXiv:1809.02921 (2018)

  • Liu, W., Guo, J., Sonboli, N., Burke, R., Zhang, S.: Personalized fairness-aware re-ranking for microlending. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 467–471. ACM (2019)

  • Lopes, G.R., Moro, M.M., Wives, L.K., De Oliveira, J.P.M.: Collaboration recommendation on academic social networks. In: International Conference on Conceptual Modeling, pp. 190–199. Springer (2010)

  • Lu, W., Chen, S., Li, K., Lakshmanan, L.V.S.: Show me the money: dynamic recommendations for revenue maximization. Proc. VLDB Endow. 7(14), 1785–1796 (2014)

    Article  Google Scholar 

  • Masthoff, J.: Group recommender systems: combining individual models. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 677–702. Springer, Berlin (2011)

    Chapter  Google Scholar 

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

  • Mehta, A., Saberi, A., Vazirani, U., Vazirani, V.: Adwords and generalized online matching. J. ACM 54(5), 22 (2007)

    Article  MathSciNet  Google Scholar 

  • Moulin, H.: Fair Division and Collective Welfare. MIT Press, Cambridge (2004)

    Google Scholar 

  • Nguyen, P., Dines, J., Krasnodebski, J.: A multi-objective learning to re-rank approach to optimize online marketplaces for multiple stakeholders. arXiv preprint arXiv:1708.00651 (2017)

  • Oestreicher-Singer, G., Sundararajan, A.: The visible hand? Demand effects of recommendation networks in electronic markets. Manag. Sci. 58(11), 1963–1981 (2012)

    Article  Google Scholar 

  • Panniello, U., Hill, S., Gorgoglione, M.: The impact of profit incentives on the relevance of online recommendations. Electron. Commer. Res. Appl. 20, 87–104 (2016)

    Article  Google Scholar 

  • Pathak, B., Garfinkel, R., Gopal, R.D., Venkatesan, R., Yin, F.: Empirical analysis of the impact of recommender systems on sales. J. Manag. Inf. Syst. 27(2), 159–188 (2010)

    Article  Google Scholar 

  • Pizzato, L., Rej, T., Chung, T., Koprinska, I., Kay, J.: RECON: a reciprocal recommender for online dating. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 207–214. ACM (2010)

  • 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, NY, USA (2010b)

  • Ponce, V., Deschamps, J.-P., Giroux, L.-P., Salehi, F., Abdulrazak, B.: QueFaire: context-aware in-person social activity recommendation system for active aging. In: Inclusive Smart Cities and e-Health, pp. 64–75. Springer (2015)

  • Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 157–164. ACM (2011)

  • Rochet, J.-C., Tirole, J.: Platform competition in two-sided markets. J. Eur. Econ. Assoc. 1(4), 990–1029 (2003)

    Article  Google Scholar 

  • Rodriguez, M., Posse, C., Zhang, E.: Multiple objective optimization in recommender systems. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 11–18. ACM (2012a)

  • Rodriguez, M., Posse, C., Zhang, E.: Multiple objective optimization in recommender systems. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 11–18. ACM (2012b)

  • Roth, A.E., Sotomayor, M.: Two-sided matching. In: Aumann, R.J., Hart, S. (eds.) Handbook of Game Theory with Economic Applications, vol. 1, pp. 485–541. Elsevier, Amsterdam (1992)

    Chapter  Google Scholar 

  • Semerci, O., Gruson, A., Edwards, C., Lacker, B., Gibson, C., Radosavljevic, V.: Homepage personalization at spotify. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 527–527. ACM (2019)

  • Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 210–217. ACM Press/Addison-Wesley Publishing Co. (1995)

  • Shih, Y.-Y.: Product recommendation approaches: collaborative filtering via customer lifetime value and customer demands. Expert Syst. App. 35(1–2), 350–360 (2008)

    Article  Google Scholar 

  • Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2219–2228. ACM (2018)

  • Smyth, B., McClave, P.: Similarity vs. diversity. In: Case-Based Reasoning Research and Development, pp. 347–361. Springer (2001)

  • Steel, E., Angwin, J.: On the web’s cutting edge, anonymity in name only. The Wall Street Journal (2010). http://on.wsj.com/aimKCw. Accessed 1 July 2019

  • Sürer, Ö., Burke, R., Malthouse, E.C.: Multistakeholder recommendation with provider constraints. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 54–62. ACM (2018)

  • Svore, K.M., Volkovs, M.N., Burges, C.J.C.: Learning to rank with multiple objective functions. In: Proceedings of the 20th International Conference on World Wide Web. WWW ’11, pp. 367–376. ACM, New York, NY, USA (2011)

  • Tang, J., Wu, S., Sun, J., Su, H.: Cross-domain collaboration recommendation. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1285–1293. ACM (2012)

  • Terveen, L., McDonald, D.W.: Social matching: a framework and research agenda. ACM Trans. Comput. Hum. Interact. 12(3), 401–434 (2005)

    Article  Google Scholar 

  • Tintarev, N., Sullivan, E., Guldin, D., Qiu, S., Odjik, D.: Same, same, but different: algorithmic diversification of viewpoints in news. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 7–13. ACM (2018)

  • Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 109–116. ACM (2011)

  • Wang, H.-F., Wu, C.-T.: A mathematical model for product selection strategies in a recommender system. Expert Syst. Appl. 36(3, Part 2), 7299–7308 (2009)

    Article  Google Scholar 

  • Xia, P., Liu, B., Sun, Y., Chen, C.: Reciprocal recommendation system for online dating. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 234–241. ACM (2015)

  • Xia, P., Zhai, S., Liu, B., Sun, Y., Chen, C.: Design of reciprocal recommendation systems for online dating. Soc. Netw. Anal. Min. 6(1), 32 (2016)

    Article  Google Scholar 

  • Xu, B., Huang, Y., Kwak, H., Contractor, N.: Structures of broken ties: exploring unfollow behavior on Twitter. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work. CSCW ’13, pp. 871–876. ACM, New York, NY, USA (2013)

  • Yao, S., Huang, B.: Beyond parity: fairness objectives for collaborative filtering. In: Advances in Neural Information Processing Systems, p. 30 (2017a)

  • Yao, S., Huang, B.: New fairness metrics for recommendation that embrace differences. In: Workshop on Fairness, Accountability and Transparency in Machine Learning (FATML), p. 5 (2017b)

  • Yu, M., Zhang, X., Kreager, D.: New to online dating? Learning from experienced users for a successful match. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 467–470 (2016)

  • Yuan, S., Abidin, A.Z., Sloan, M., Wang, J.: Internet advertising: an interplay among advertisers, online publishers, ad exchanges and web users. arXiv preprint arXiv:1206.1754 (2012)

  • Zehlike, M., Bonchi, F., Castillo, C., Hajian, S., Megahed, M., Baeza-Yates, R.: FA\(\ast \)IR: a fair top-k ranking algorithm. In: Proceedings of the 25th ACM Conference on Information and Knowledge Management (2017a)

  • Zehlike, M., Bonchi, F., Castillo, C., Hajian, S., Megahed, M., Baeza-Yates, R.: Fa*ir: a fair top-k ranking algorithm. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1569–1578. ACM (2017b)

  • Zhang, Y., Chen, X.: Explainable recommendation: a survey and new perspectives. arXiv preprint arXiv:1804.11192 (2018)

  • Zheng, Y., Ghane, N., Sabouri, M.: Personalized educational learning with multi-stakeholder optimizations. In: Adjunct Proceedings of the ACM Conference on User Modelling, Adaptation and Personalization. ACM (2019)

  • 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)

  • Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22–32. ACM (2005)

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Abdollahpouri, H., Adomavicius, G., Burke, R. et al. Multistakeholder recommendation: Survey and research directions. User Model User-Adap Inter 30, 127–158 (2020). https://doi.org/10.1007/s11257-019-09256-1

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