Abstract
Business agility requires support from recommendation systems, but explaining recommendations may yield information disclosure. We analyze how to provide explanations in the scenario of Multi-Stakeholder Recommendation where the sensible information of one stakeholder should not be disclosed in the explanation to another stakeholder. Among the several types of explanations analyzed, counterfactual explanations come off best as they allow the system to preserve each stakeholder’s privacy and sensitive information in terms of preferences.
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References
Abdollahpouri, H., et al.: Beyond personalization: research directions in multistakeholder recommendation (2019)
Abdollahpouri, H., et al.: Multistakeholder recommendation: survey and research directions. User Modeling and User-Adap. Inter. 30(1), 127–158 (2020). https://doi.org/10.1007/s11257-019-09256-1
Abdollahpouri, H., Burke, R.: Multi-stakeholder recommendation and its connection to multi-sided fairness. In: RMSE@RecSys. CEUR Workshop Proceedings, vol. 2440. CEUR-WS.org (2019)
Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B.: The unfairness of popularity bias in recommendation. In: RMSE@RecSys. CEUR Workshop Proceedings, vol. 2440. CEUR-WS.org (2019)
Abdou, W., Bloch, C., Charlet, D., Spies, F.: Multi-pareto-ranking evolutionary algorithm. In: Hao, J.-K., Middendorf, M. (eds.) EvoCOP 2012. LNCS, vol. 7245, pp. 194–205. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29124-1_17
Burke, R.D., Abdollahpouri, H., Mobasher, B., Gupta, T.: Towards multi-stakeholder utility evaluation of recommender systems. In: UMAP (Extended Proceedings). CEUR Workshop Proceedings, vol. 1618. CEUR-WS.org (2016)
Castells, P., Hurley, N.J., Vargas, S.: Novelty and diversity in recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 881–918. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_26
Celli, A., Marchesi, A., Farina, G., Gatti, N.: No-regret learning dynamics for extensive-form correlated equilibrium. In: NeurIPS (2020)
Eiter, T., Lukasiewicz, T.: Complexity results for structure-based causality. Artif. Intell. 142(1), 53–89 (2002). https://doi.org/10.1016/S0004-3702(02)00271-0
Gedikli, F., Jannach, D., Ge, M.: How should I explain? A comparison of different explanation types for recommender systems. Int. J. Hum. Comput. Stud. 72(4), 367–382 (2014)
Halpern, J.Y., Pearl, J.: Causes and explanations: a structural-model approach - part II: explanations. In: IJCAI, pp. 27–34. Morgan Kaufmann (2001)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: CSCW, pp. 241–250. ACM (2000)
Hilton, D., McClure, J., Slugoski, B.: The course of events: counterfactuals, causal sequences, and explanation (2005)
Hofmann, T., Schölkopf, B., Smola, A.J.: Kernel methods in machine learning. Ann. Stat. 36(3), 1171–1220 (2008)
Hurley, N., Zhang, M.: Novelty and diversity in top-n recommendation - analysis and evaluation. ACM Trans. Internet Technol. 10(4), 14:1–14:30 (2011)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 145–186. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_5
Kunaver, M., Porl, T.: Diversity in recommender systems a survey. Knowl.-Based Syst. 123(C), 154–162 (2017)
Masthoff, J.: Group recommender systems: combining individual models. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 677–702. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_21
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI Extended Abstracts, pp. 1097–1101. ACM (2006)
Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1–38 (2019)
Pu, P., Chen, L.: Trust-inspiring explanation interfaces for recommender systems. Knowl.-Based Syst. 20(6), 542–556 (2007)
Tintarev, N., Masthoff, J.: A survey of explanations in recommender systems. In: ICDE Workshops, pp. 801–810. IEEE Computer Society (2007)
Tintarev, N., Masthoff, J.: Explaining recommendations: design and evaluation. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 353–382. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_10
Vargas, S.: Novelty and diversity enhancement and evaluation in recommender systems and information retrieval. In: SIGIR, p. 1281. ACM (2014)
Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: RecSys, pp. 109–116. ACM (2011)
Verdeaux, W., Moreau, C., Labroche, N., Marcel, P.: Causality based explanations in multi-stakeholder recommendations. In: EDBT/ICDT Workshops. CEUR Workshop Proceedings, vol. 2578. CEUR-WS.org (2020)
Xia, P., Liu, B., Sun, Y., Chen, C.X.: Reciprocal recommendation system for online dating. In: ASONAM, pp. 234–241. ACM (2015)
Zhang, Y., Chen, X.: Explainable recommendation: a survey and new perspectives. Found. Trends Inf. Ret. 14(1), 1–101 (2020)
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Cornacchia, G., Donini, F.M., Narducci, F., Pomo, C., Ragone, A. (2021). Explanation in Multi-Stakeholder Recommendation for Enterprise Decision Support Systems. In: Polyvyanyy, A., Rinderle-Ma, S. (eds) Advanced Information Systems Engineering Workshops. CAiSE 2021. Lecture Notes in Business Information Processing, vol 423. Springer, Cham. https://doi.org/10.1007/978-3-030-79022-6_4
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