×

On the equivalence of optimal recommendation sets and myopically optimal query sets. (English) Zbl 1493.68353

Summary: Preference elicitation is an important component in many AI applications, including decision support and recommender systems. Such systems must assess user preferences, based on interactions with their users, and make recommendations using (possibly incomplete and imprecise) beliefs about those preferences. Mechanisms for explicit preference elicitation – asking users to answer direct queries about their preferences – can be of great value; but due to the cognitive and time cost imposed on users, it is important to minimize the number of queries by asking those that have high (expected) value of information. An alternative approach is to simply make recommendations and have users provide feedback (e.g., accept a recommendation or critique it in some way) and use this more indirect feedback to gradually improve the quality of the recommendations. Due to inherent uncertainty about a user’s true preferences, often a set of recommendations is presented to the user at each stage. Conceptually, a set of recommendations can also be viewed as choice query, in which the user indicates which option is most preferred from that set. Because of the potential tension between making a good set recommendation and asking an informative choice query, we explore the connection between the two. We consider two different models of preference uncertainty and optimization: (a) a Bayesian framework in which a posterior over user utility functions is maintained, optimal recommendations are assessed using expected utility, and queries are assessed using expected value of information; and (b) a minimax-regret framework in which user utility uncertainty is strict (represented by a polytope), recommendations are made using the minimax-regret robustness criterion, and queries are assessed using worst-case regret reduction. We show that, somewhat surprisingly, in both cases, there is no tradeoff to be made between good recommendations and good queries: we prove that the optimal recommendation set of size \(k\) is also an optimal choice query of size \(k\). We also examine the case where user responses to choice queries are error prone (using both constant and mixed multinomial logit noise models) showing the results are robust to this form of noise. In both frameworks, our theoretical results have practical consequences for the design of interactive recommenders. Our results also allow us to design efficient algorithms to compute optimal query/recommendation sets. We develop several such algorithms (both exact and approximate) for both settings and provide empirical validation of their performance.

MSC:

68T35 Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence
68T37 Reasoning under uncertainty in the context of artificial intelligence
68U35 Computing methodologies for information systems (hypertext navigation, interfaces, decision support, etc.)
91B06 Decision theory
91B16 Utility theory
PDFBibTeX XMLCite
Full Text: DOI HAL

References:

[1] Abbas, A., Entropy methods for adaptive utility elicitation, IEEE Trans. Syst. Sci. Cybern., 34, 2, 169-178 (2004)
[2] Adomavicius, G.; Kwon, Y., Maximizing aggregate recommendation diversity: a graph-theoretic approach, (Proceedings of the Workshop on Novelty and Diversity in Recommender Systems (DiveRS-11) at the 5th ACM International Conference on Recommender Systems. Proceedings of the Workshop on Novelty and Diversity in Recommender Systems (DiveRS-11) at the 5th ACM International Conference on Recommender Systems, RecSys-11, Chicago, IL, USA (2011)), 3-10
[3] Ai, Q.; Bi, K.; Guo, J.; Croft, W. B., Learning a deep listwise context model for ranking refinement, (Proceedings of the 41st Annual International ACM Conference on Research and Development in Information Retrieval. Proceedings of the 41st Annual International ACM Conference on Research and Development in Information Retrieval, SIGIR-18, Ann Arbor, MI, USA (2018)), 135-144
[4] Ailon, N.; Karnin, Z. S.; Joachims, T., Reducing dueling bandits to cardinal bandits, (Proceedings of the Thirty-First International Conference on Machine Learning. Proceedings of the Thirty-First International Conference on Machine Learning, ICML-14, Beijing, China (2014)), 856-864
[5] Akrour, R.; Schoenauer, M.; Sebag, M., APRIL: active preference learning-based reinforcement learning, (Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2012, Bristol, UK (2012)), 116-131
[6] Baier, J. A.; McIlraith, S. A., Planning with preferences, AI Mag., 29, 4, 25-36 (2008)
[7] Bana, E.; Costa, C. A.; Vansnick, J.-C., MACBETH - an interactive path towards the construction of cardinal value functions, Int. Trans. Oper. Res., 1, 489-500 (1994) · Zbl 0857.90004
[8] Bell, D. E., Regret in decision making under uncertainty, Oper. Res., 30, 961-981 (1982) · Zbl 0491.90004
[9] Bello, I.; Kulkarni, S.; Jain, S.; Boutilier, C.; Chi, E.; Eban, E.; Luo, X.; Mackey, A.; Meshi, O., Seq2slate: re-ranking and slate optimization with RNNs (2018)
[10] Benabbou, N.; Diodoro, S. D.S. D.; Perny, P.; Viappiani, P., Incremental preference elicitation in multi-attribute domains for choice and ranking with the Borda count, (Proceedings of the 10th International Conference on Scalable Uncertainty Management. Proceedings of the 10th International Conference on Scalable Uncertainty Management, SUM-16, Nice, France (2016)), 81-95
[11] Benabbou, N.; Perny, P., Combining preference elicitation and search in multiobjective state-space graphs, (Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15, Buenos Aires, Argentina (2015)), 297-303
[12] Benabbou, N.; Perny, P.; Viappiani, P., Incremental elicitation of Choquet capacities for multicriteria decision making, (Proceedings of the European Conference on Artificial Intelligence. Proceedings of the European Conference on Artificial Intelligence, ECAI-14 (2014)), 87-92 · Zbl 1366.91044
[13] Benabbou, N.; Perny, P.; Viappiani, P., Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems, Artif. Intell., 246, 152-180 (2017) · Zbl 1419.68071
[14] Berry, D. A.; Fristedt, B., Bandit Problems: Sequential Allocation of Experiments (1985), Chapman and Hall: Chapman and Hall London · Zbl 0659.62086
[15] Bigot, D.; Zanuttini, B.; Fargier, H.; Mengin, J., Probabilistic conditional preference networks, (Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence. Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, UAI-13, Bellevue, WA, USA (2013))
[16] Bonilla, E. V.; Guo, S.; Sanner, S., Gaussian process preference elicitation, (Advances in Neural Information Processing Systems 23. Advances in Neural Information Processing Systems 23, IPS-10, Vancouver, BC, Canada (2010)), 262-270
[17] Bous, G.; Pirlot, M., Learning multicriteria utility functions with random utility models, (Proceedings of the Third International Conference on Algorithmic Decision Theory. Proceedings of the Third International Conference on Algorithmic Decision Theory, ADT-13, Bruxelles, Belgium (2013)), 101-115 · Zbl 1406.91128
[18] Boutilier, C., A POMDP formulation of preference elicitation problems, (Proceedings of the Eighteenth National Conference on Artificial Intelligence. Proceedings of the Eighteenth National Conference on Artificial Intelligence, AAAI-02, Edmonton, AB, Canada (2002)), 239-246
[19] Boutilier, C., Computational decision support: regret-based models for optimization and preference elicitation, (Zentall, T. R.; Crowley, P. H., Comparative Decision Making: Analysis and Support Across Disciplines and Applications (2011), Oxford University Press), 423-453
[20] Boutilier, C.; Brafman, R.; Domshlak, C.; Hoos, H.; Poole, D., Preference-based constrained optimization with CP-nets, Comput. Intell., 20, 2, 137-157 (2004)
[21] Boutilier, C.; Patrascu, R.; Poupart, P.; Schuurmans, D., Constraint-based optimization and utility elicitation using the minimax decision criterion, Artif. Intell., 170, 8-9, 686-713 (2006) · Zbl 1131.91317
[22] Boutilier, C.; Sandholm, T.; Shields, R., Eliciting bid taker non-price preferences in (combinatorial) auctions, (Proceedings of the Nineteenth National Conference on Artificial Intelligence. Proceedings of the Nineteenth National Conference on Artificial Intelligence, AAAI-04, San Jose, CA, USA (2004)), 204-211
[23] Boutilier, C.; Zemel, R. S.; Marlin, B., Active collaborative filtering, (Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence. Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, UAI-03, Acapulco, Mexico (2003)), 98-106
[24] Braziunas, D., Decision-theoretic Elicitation of Generalized Additive Utilities (2011), University of Toronto, PhD thesis
[25] Braziunas, D.; Boutilier, C., Minimax regret based elicitation of generalized additive utilities, (Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence. Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, UAI-07, Vancouver, BC, Canada (2007)), 25-32
[26] Braziunas, D.; Boutilier, C., Elicitation of factored utilities, AI Mag., 29, 4, 79-92 (2008)
[27] Braziunas, D.; Boutilier, C., Assessing regret-based preference elicitation with the UTPREF recommendation system, (Proceedings of the Eleventh ACM Conference on Electronic Commerce. Proceedings of the Eleventh ACM Conference on Electronic Commerce, EC’10 (2010)), 219-228
[28] Buchbinder, N.; Feldman, M.; Naor, J.; Schwartz, R., Submodular maximization with cardinality constraints, (Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms. Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA-14, Portland, Oregon, USA (2014)), 1433-1452 · Zbl 1423.90212
[29] Burke, R., Interactive critiquing for catalog navigation in e-commerce, Artif. Intell. Rev., 18, 3-4, 245-267 (2002)
[30] (Camerer, C. F.; Loewenstein, G.; Rabin, M., Advances in Behavioral Economics (2003), Princeton University Press: Princeton University Press Princeton, New Jersey)
[31] 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, 1-2, 67-119 (2014)
[32] Chajewska, U.; Koller, D.; Parr, R., Making rational decisions using adaptive utility elicitation, (Proceedings of the Seventeenth National Conference on Artificial Intelligence. Proceedings of the Seventeenth National Conference on Artificial Intelligence, AAAI-00, Austin, TX, USA (2000)), 363-369
[33] Chen, L.; Pu, P., Evaluating critiquing-based recommender agents, (Proceedings of the Twenty-First National Conference on Artificial Intelligence. Proceedings of the Twenty-First National Conference on Artificial Intelligence, AAAI-06, Boston, MA, USA (2006))
[34] Chen, L.; Pu, P., Hybrid critiquing-based recommender systems, (Proceedings of the 12th International Conference on Intelligent User Interfaces. Proceedings of the 12th International Conference on Intelligent User Interfaces, IUI-07, Honolulu, Hawaii, USA (2007)), 22-31
[35] Chen, L.; Pu, P., Critiquing-based recommenders: survey and emerging trends, User Model. User-Adapt. Interact., 22, 1-2, 125-150 (2012)
[36] Chen, M.; Beutel, A.; Covington, P.; Jain, S.; Belletti, F.; Chi, E., Top-k off-policy correction for a REINFORCE recommender system, (Proceedings of the 12th ACM International Conference on Web Search and Data Mining. Proceedings of the 12th ACM International Conference on Web Search and Data Mining, WSDM-19, Melbourne, Australia (2019)), 456-464
[37] Cheng, H.-T.; Koc, L.; Harmsen, J.; Shaked, T.; Chandra, T.; Aradhye, H.; Anderson, G.; Corrado, G.; Chai, W.; Ispir, M.; Anil, R.; Haque, Z.; Hong, L.; Jain, V.; Liu, X.; Shah, H., Wide & deep learning for recommender systems, (Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, MA, USA (2016)), 7-10
[38] Choi, S.; Ha, H.; Hwang, U.; Kim, C.; Ha, J.-W.; Yoon, S., Reinforcement learning-based recommender system using biclustering technique (2018)
[39] Chu, W.; Ghahramani, Z., Gaussian processes for ordinal regression, J. Mach. Learn. Res., 6, 1019-1041 (2005) · Zbl 1222.68170
[40] Corless, R. M.; Gonnet, G. H.; Hare, D. E.; Jeffrey, D. J.; Knuth, D. E., On the Lambert W function, Adv. Comput. Math., 5, 1, 329-359 (1996) · Zbl 0863.65008
[41] Cornelio, C.; Goldsmith, J.; Mattei, N.; Rossi, F.; Venable, K. B., Updates and uncertainty in CP-nets, (Proceedings of the 26th Australasian Joint Conference. Proceedings of the 26th Australasian Joint Conference, AI 2013, Dunedin, New Zealand (2013)), 301-312
[42] Covington, P.; Adams, J.; Sargin, E., Deep neural networks for youtube recommendations, (Proceedings of the 10th ACM Conference on Recommender Systems. Proceedings of the 10th ACM Conference on Recommender Systems, RecSys-16, Boston, MA, USA (2016)), 191-198
[43] Craswell, N.; Zoeter, O.; Taylor, M.; Ramsey, B., An experimental comparison of click position-bias models, (Proceedings of the 2008 International Conference on Web Search and Data Mining. Proceedings of the 2008 International Conference on Web Search and Data Mining, WSDM-08 (2008)), 87-94
[44] desJardins, M.; Eaton, E.; Wagstaff, K., Learning user preferences for sets of objects, (Proceedings of the Twenty-Third International Conference on Machine Learning. Proceedings of the Twenty-Third International Conference on Machine Learning, ICML-06, Pittsburgh, USA (2006)), 273-280
[45] Dragone, P.; Pellegrini, G.; Vescovi, M.; Tentori, K.; Passerini, A., No more ready-made deals: constructive recommendation for telco service bundling, (Proceedings of the 12th ACM Conference on Recommender Systems. Proceedings of the 12th ACM Conference on Recommender Systems, RecSys-18, Vancouver, British Columbia, Canada (2018)), 163-171
[46] Dragone, P.; Teso, S.; Kumar, M.; Passerini, A., Decomposition strategies for constructive preference elicitation, (Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, AAAI-18, New Orleans, LA, USA (2018)), 2934-2942
[47] Ekstrand, M. D.; Riedl, J.; Konstan, J. A., Collaborative filtering recommender systems, Found. Trends Hum. Comput. Interac., 4, 2, 175-243 (2011)
[48] Elahi, M.; Ricci, F.; Rubens, N., Active learning strategies for rating elicitation in collaborative filtering: a system-wide perspective, ACM Trans. Intell. Syst. Technol., 5, 1, 13 (2013)
[49] Fishburn, P. C., Interdependence and additivity in multivariate, unidimensional expected utility theory, Int. Econ. Rev., 8, 335-342 (1967) · Zbl 0153.49302
[50] Gajos, K.; Weld, D. S., Preference elicitation for interface optimization, (Proceedings of the 18th Annual ACM Symposium on User Interface Software and Technology. Proceedings of the 18th Annual ACM Symposium on User Interface Software and Technology, UIST-05, Seattle, WA, USA (2005)), 173-182
[51] Gauci, J.; Conti, E.; Liang, Y.; Virochsiri, K.; He, Y.; Kaden, Z.; Narayanan, V.; Ye, X., Horizon: Facebook’s open source applied reinforcement learning platform (2018)
[52] Gilbert, H.; Spanjaard, O.; Viappiani, P.; Weng, P., Reducing the number of queries in interactive value iteration, (Proceedings of the Fourth International Conference on Algorithmic Decision Theory. Proceedings of the Fourth International Conference on Algorithmic Decision Theory, ADT-15, Lexington, KY, USA (2015)), 139-152 · Zbl 1405.91114
[53] Gomez-Uribe, C. A.; Hunt, N., The Netflix recommender system: algorithms, business value, and innovation, ACM Trans. Manag. Inf. Syst., 6, 4, 13:1-13:19 (2016)
[54] González, J.; Dai, Z.; Damianou, A. C.; Lawrence, N. D., Preferential Bayesian optimization, (Proceedings of the Thirty-Fourth International Conference on Machine Learning. Proceedings of the Thirty-Fourth International Conference on Machine Learning, ICML-17, Sydney, Australia (2017)), 1282-1291
[55] Grabisch, M., Set Functions, Games and Capacities in Decision Making, Theory and Decision Library C, vol. 46 (2016), Springer International Publishing · Zbl 1339.91003
[56] Grabisch, M.; Labreuche, C., A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid, Ann. Oper. Res., 175, 1, 247-286 (2010) · Zbl 1185.90118
[57] Grabisch, M.; Marichal, J.-L.; Mesiar, R.; Pap, E., Aggregation Functions, Encyclopedia of Mathematics and Its Applications (2009), Cambridge University Press: Cambridge University Press New York, NY, USA · Zbl 1196.00002
[58] Greco, S.; Mousseau, V.; Slowinski, R., Ordinal regression revisited: multiple criteria ranking using a set of additive value functions, Eur. J. Oper. Res., 191, 416-436 (2008) · Zbl 1147.90013
[59] Guo, S.; Sanner, S., Real-time multiattribute Bayesian preference elicitation with pairwise comparison queries, (Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS-10, Chia Laguna Resort, Sardinia, Italy (2010)), 289-296
[60] Guo, Y.; Gomes, C. P., Learning optimal subsets with implicit user preferences, (Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09, Pasadena, CA, USA (2009)), 1052-1057
[61] Halpern, J. Y., Reasoning About Uncertainty (2005), MIT Press
[62] He, R.; McAuley, J., Fusing similarity models with Markov chains for sparse sequential recommendation, (Proceedings of the IEEE International Conference on Data Mining. Proceedings of the IEEE International Conference on Data Mining, ICDM-16, Barcelona, Spain (2016))
[63] Herbrich, R.; Minka, T.; Graepel, T., Trueskill^tm: a Bayesian skill rating system, (Advances in Neural Information Processing Systems 19. Advances in Neural Information Processing Systems 19, NIPS-06, Vancouver, BC, Canada (2006)), 569-576
[64] Herlocker, J. L.; Konstan, J. A.; Borchers, A.; Riedl, J., An algorithmic framework for performing collaborative filtering, (Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR-99, Berkeley, CA, USA (1999)), 230-237
[65] Hines, G.; Larson, K., Preference elicitation for risky prospects, (Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems. Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, AAMAS.10, Toronto, ON, Canada (2010)), 889-896
[66] Holloway, H. A.; White, C. C., Question selection for multiattribute decision-aiding, Eur. J. Oper. Res., 148, 525-543 (2003) · Zbl 1035.90034
[67] (Howard, R. A.; Matheson, J. E., Readings on the Principles and Applications of Decision Analysis (1984), Strategic Decision Group: Strategic Decision Group Menlo Park, CA)
[68] Ie, E.; Jain, V.; Wang, J.; Navrekar, S.; Agarwal, R.; Wu, R.; Cheng, H.-T.; Chandra, T.; Boutilier, C., SlateQ: a tractable decomposition for reinforcement learning with recommendation sets, (Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, Macau, China (2019)), 2592-2599
[69] Iyengar, V. S.; Lee, J.; Campbell, M., Q-Eval: evaluating multiple attribute items using queries, (Proceedings of the Third ACM Conference on Electronic Commerce. Proceedings of the Third ACM Conference on Electronic Commerce, EC’01, Tampa, FL, USA (2001)), 144-153
[70] Jacobson, K.; Murali, V.; Newett, E.; Whitman, B.; Yon, R., Music personalization at Spotify, (Proceedings of the 10th ACM Conference on Recommender Systems. Proceedings of the 10th ACM Conference on Recommender Systems, RecSys-16, Boston, Massachusetts, USA (2016)), 373
[71] Jacquet-Lagrèze, E.; Siskos, Y., Assessing a set of additive utility functions for multicriteria decision making: the UTA method, Eur. J. Oper. Res., 10, 151-164 (1982) · Zbl 0481.90078
[72] Jiang, R.; Gowal, S.; Mann, T. A.; Rezende, D. J., Beyond greedy ranking: slate optimization via list-CVAE, (Proceedings of the Seventh International Conference on Learning Representations. Proceedings of the Seventh International Conference on Learning Representations, ICLR-19, New Orleans, LA, USA (2019))
[73] Joachims, T., Optimizing search engines using clickthrough data, (Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-02, Edmonton, AB, Canada (2002)), 133-142
[74] Keeney, R. L.; Raiffa, H., Decisions with Multiple Objectives: Preferences and Value Tradeoffs (1976), John Wiley and Sons: John Wiley and Sons New York · Zbl 0488.90001
[75] Kohli, P.; Salek, M.; Stoddard, G., A fast bandit algorithm for recommendation to users with heterogeneous tastes, (Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, AAAI-13, Bellevue, WA, USA (2013)), 1135-1141
[76] 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, 77-87 (1997)
[77] Koren, Y.; Bell, R. M., Advances in collaborative filtering, (Ricci, F.; Rokach, L.; Shapira, B., Recommender Systems Handbook (2015), Springer), 77-118
[78] Kouvelis, P.; Yu, G., Robust Discrete Optimization and Its Applications (1997), Kluwer: Kluwer Dordrecht · Zbl 0873.90071
[79] Kunaver, M.; Pozrl, T., Diversity in recommender systems - a survey, Knowl.-Based Syst., 123, 154-162 (2017)
[80] Kveton, B.; Szepesvari, C.; Wen, Z.; Ashkan, A., Cascading bandits: learning to rank in the cascade model, (Proceedings of the Thirty-Second International Conference on Machine Learning. Proceedings of the Thirty-Second International Conference on Machine Learning, ICML-15, Lille, France (2015)), 767-776
[81] Labreuche, C.; Huédé, F. L., Miriad: a tool suite for mcda, (Proceedings of EUSFLAT’05 (2005)), 204-209
[82] Leskovec, J.; Krause, A.; Guestrin, C.; Faloutsos, C.; VanBriesen, J. M.; Glance, N. S., Cost-effective outbreak detection in networks, (Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-07, San Jose, California, USA (2007)), 420-429
[83] Loepp, B.; Hussein, T.; Ziegler, J., Choice-based preference elicitation for collaborative filtering recommender systems, (Proceedings of the 2014 SIGCHI Conference on Human Factors in Computing Systems. Proceedings of the 2014 SIGCHI Conference on Human Factors in Computing Systems, CHI-14, Toronto, ON, Canada (2014)), 3085-3094
[84] Loomes, G.; Sugden, R., Regret theory: an alternative theory of rational choice under uncertainty, Econ. J., 92, 805-824 (1982)
[85] Louviere, J. J.; Hensher, D. A.; Swait, J. D., Stated Choice Methods: Analysis and Application (2000), Cambridge University Press: Cambridge University Press Cambridge · Zbl 0992.91002
[86] Lu, T.; Boutilier, C., Robust approximation and incremental elicitation in voting protocols, (Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11, Barcelona, Catalonia, Spain (2011)), 287-293
[87] Lucas, C. G.; Griffiths, T. L.; Xu, F.; Fawcett, C., A rational model of preference learning and choice prediction by children, (Advances in Neural Information Processing Systems 21. Advances in Neural Information Processing Systems 21, IPS-08 (2008)), 985-992
[88] Luce, R. D., Individual Choice Behavior: A Theoretical Analysis (1959), Wiley · Zbl 0093.31708
[89] Marichal, J.-L.; Meyer, P.; Roubens, M., Sorting multi-attribute alternatives: the TOMASO method, Comput. Oper. Res., 32, 861-877 (2005) · Zbl 1071.90550
[90] McFadden, D., Conditional logit analysis of qualitative choice behavior, (Zarembka, P., Frontiers in Econometrics (1974), Academic Press), 105-142
[91] McGinty, L.; Reilly, J., On the evolution of critiquing recommenders, (Ricci, F.; Rokach, L.; Shapira, B.; Kantor, P. B., Recommender Systems Handbook (2011), Springer), 419-453
[92] Minka, T. P., Expectation propagation for approximate Bayesian inference, (Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence. Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, UAI-01, Seattle, WA, USA (2001)), 362-369
[93] Minoux, M., Accelerated greedy algorithms for maximizing submodular set functions, (Proceedings of the 8th IFIP Conference on Optimization Techniques. Proceedings of the 8th IFIP Conference on Optimization Techniques, Würzburg, Germany (1978)), 234-243 · Zbl 0372.90128
[94] Naamani-Dery, L.; Golan, I.; Kalech, M.; Rokach, L., Preference elicitation for group decisions using the Borda voting rule, Group Decis. Negot., 24, 6, 1015-1033 (2015)
[95] Neal, R. M., Slice sampling, Ann. Stat., 31, 3, 705-770 (2003) · Zbl 1051.65007
[96] Nemhauser, G. L.; Wolsey, L. A.; Fisher, M. L., An analysis of approximations for maximizing submodular set functions—I, Math. Program., 14, 1, 265-294 (1978) · Zbl 0374.90045
[97] Ng, A.; Russell, S., Algorithms for inverse reinforcement learning, (Proceedings of the Seventeenth International Conference on Machine Learning. Proceedings of the Seventeenth International Conference on Machine Learning, ICML-00, Stanford, CA, USA (2000)), 663-670
[98] Perny, P.; Viappiani, P.; Boukhatem, A., Incremental preference elicitation for decision making under risk with the rank-dependent utility model, (Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence. Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, UAI-16, New York City, NY, USA (2016))
[99] Perrault, A.; Boutilier, C., Experiential preference elicitation for autonomous heating and cooling systems, (Proceedings of the Eighteenth Conference on Autonomous Agents and Multiagent Systems. Proceedings of the Eighteenth Conference on Autonomous Agents and Multiagent Systems, AAMAS-19, Montreal, QC, Canada (2019)), 431-439
[100] Price, R.; Messinger, P. R., Optimal recommendation sets: covering uncertainty over user preferences, (Proceedings of the Twentieth National Conference on Artificial Intelligence. Proceedings of the Twentieth National Conference on Artificial Intelligence, AAAI-05, Pittsburgh, PA, USA (2005)), 541-548
[101] Regan, K.; Boutilier, C., Regret-based reward elicitation for Markov decision processes, (Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI-09, Montreal, QC, Canada (2009)), 444-451
[102] Reilly, J.; McCarthy, K.; McGinty, L.; Smyth, B., Dynamic critiquing, (Proceedings of the 7th European Conference on Advances in Case-Based Reasoning. Proceedings of the 7th European Conference on Advances in Case-Based Reasoning, ECCBR-04, Madrid, Spain (2004)), 763-777
[103] Reilly, J.; McCarthy, K.; McGinty, L.; Smyth, B., Incremental critiquing, Knowl.-Based Syst., 18, 4-5, 143-151 (2005)
[104] Reilly, J.; Zhang, J.; McGinty, L.; Pu, P.; Smyth, B., Evaluating compound critiquing recommenders: a real-user study, (Proceedings of the Eighth ACM Conference on Electronic Commerce. Proceedings of the Eighth ACM Conference on Electronic Commerce, EC’07, San Diego, California, USA (2007)), 114-123
[105] Rendle, S.; Freudenthaler, C.; Schmidt-Thieme, L., Factorizing personalized Markov chains for next-basket recommendation, (Proceedings of the 19th International World Wide Web Conference. Proceedings of the 19th International World Wide Web Conference, WWW-10, Raleigh, NC, USA (2010)), 811-820
[106] Rennie, J.; Srebro, N., Fast maximum margin matrix factorization for collaborative prediction, (Proceedings of the Twenty-Second International Conference on Machine Learning. Proceedings of the Twenty-Second International Conference on Machine Learning, ICML-05, Bonn, Germany (2005))
[107] Riquelme, C.; Tucker, G.; Snoek, J., Deep Bayesian bandits showdown: an empirical comparison of Bayesian deep networks for Thompson sampling, (Proceedings of the Sixth International Conference on Learning Representations. Proceedings of the Sixth International Conference on Learning Representations, ICLR-18, Vancouver, BC, USA (2018))
[108] Saaty, T., The Analytic Hierarchy Process, Planning, Priority Setting, Resource Allocation (1980), McGraw-Hill: McGraw-Hill New York · Zbl 0587.90002
[109] Sahoo, N.; Singh, P. V.; Mukhopadhyay, T., A hidden Markov model for collaborative filtering, Manag. Inf. Syst. Q., 36, 4 (2012)
[110] Salakhutdinov, R.; Mnih, A., Probabilistic matrix factorization, (Advances in Neural Information Processing Systems 20. Advances in Neural Information Processing Systems 20, NIPS-07, Vancouver, BC, Canada (2007)), 1257-1264
[111] Salo, A.; Hämäläinen, R. P., Preference ratios in multiattribute evaluation (PRIME)-elicitation and decision procedures under incomplete information, IEEE Trans. Syst. Man Cybern., 31, 6, 533-545 (2001)
[112] Salo, A.; Hämäläinen, R. P., Preference programming – multicriteria weighting models under incomplete information, (Handbook of Multicriteria Analysis. Handbook of Multicriteria Analysis, Applied Optimization, vol. 103 (2010), Springer), 167-187 · Zbl 1207.90001
[113] Savage, L. J., The theory of statistical decision, J. Am. Stat. Assoc., 46, 253, 55-67 (1951) · Zbl 0042.14302
[114] Savage, L. J., The Foundations of Statistics (1954), Wiley: Wiley New York · Zbl 0055.12604
[115] Selvin, S.; Bloxham, M.; Khuri, A. I.; Moore, M.; Coleman, R.; Bryce, G. R.; Hagans, J. A.; Chalmers, T. C.; Maxwell, E. A.; Smith, G. N., Letters to the editor, Am. Stat., 29, 1, 67-71 (1975)
[116] Shani, G.; Heckerman, D.; Brafman, R. I., An MDP-based recommender system, J. Mach. Learn. Res., 6, 1265-1295 (2005) · Zbl 1222.68406
[117] Sui, Y.; Zhuang, V.; Burdick, J. W.; Yue, Y., Multi-dueling bandits with dependent arms, (Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence. Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI-17, Sydney, Australia (2017))
[118] Swaminathan, A.; Krishnamurthy, A.; Agarwal, A.; Dudik, M.; Langford, J.; Jose, D.; Zitouni, I., Off-policy evaluation for slate recommendation, (Advances in Neural Information Processing Systems 30. Advances in Neural Information Processing Systems 30, IPS-17, Long Beach, CA, USA (2017)), 3632-3642
[119] Taghipour, N.; Kardan, A.; Ghidary, S. S., Usage-based web recommendations: a reinforcement learning approach, (Proceedings of the First ACM Conference on Recommender Systems. Proceedings of the First ACM Conference on Recommender Systems, RecSys07 (2007), ACM: ACM Minneapolis, MN, USA), 113-120
[120] Tan, Y. K.; Xu, X.; Liu, Y., Improved recurrent neural networks for session-based recommendations, (Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, MA, USA (2016)), 17-22
[121] Tehrani, A. F.; Cheng, W.; Dembczynski, K.; Hüllermeier, E., Learning monotone nonlinear models using the Choquet integral, (Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD 2011, Athens, Greece (2011)), 414-429
[122] Teso, S.; Dragone, P.; Passerini, A., Coactive critiquing: elicitation of preferences and features, (Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI-17, San Francisco, California, USA (2017)), 2639-2645
[123] Teso, S.; Passerini, A.; Viappiani, P., Constructive preference elicitation by setwise max-margin learning, (Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16, New York, NY, USA (2016)), 2067-2073
[124] Teso, S.; Passerini, A.; Viappiani, P., Constructive preference elicitation for multiple users with setwise max-margin, (Proceedings of the Fifth International Conference on Algorithmic Decision Theory. Proceedings of the Fifth International Conference on Algorithmic Decision Theory, ADT-17, Luxembourg, Luxembourg (2017)), 3-17 · Zbl 1398.91225
[125] Torra, V.; Narukawa, Y., Modeling Decisions - Information Fusion and Aggregation Operators (2007), Springer
[126] Toubia, O.; Hauser, J. R.; Simester, D. I., Polyhedral methods for adaptive choice-based conjoint analysis, J. Mark. Res., 41, 1, 116-131 (2004)
[127] Train, K. E., Discrete Choice Methods with Simulation (2009), Cambridge University Press: Cambridge University Press Cambridge, United Kingdom · Zbl 1269.62073
[128] Tversky, A.; Kahneman, D., Judgment under uncertainty: heuristics and biases, Science, 185, 4157, 1124-1131 (1974)
[129] van den Oord, A.; Dieleman, S.; Schrauwen, B., Deep content-based music recommendation, (Advances in Neural Information Processing Systems 26. Advances in Neural Information Processing Systems 26, IPS-13, Lake Tahoe, NV, USA (2013)), 2643-2651
[130] Vargas, S.; Baltrunas, L.; Karatzoglou, A.; Castells, P., Coverage, redundancy and size-awareness in genre diversity for recommender systems, (Proceedings of the Eighth ACM Conference on Recommender Systems. Proceedings of the Eighth ACM Conference on Recommender Systems, RecSys-14, Foster City, Silicon Valley, CA, USA (2014)), 209-216
[131] Viappiani, P.; Boutilier, C., Regret-based optimal recommendation sets in conversational recommender systems, (Proceedings of the 3rd ACM Conference on Recommender Systems. Proceedings of the 3rd ACM Conference on Recommender Systems, RecSys-09, New York, NY, USA (2009)), 101-108
[132] Viappiani, P.; Boutilier, C., Optimal Bayesian recommendation sets and myopically optimal choice query sets, (Advances in Neural Information Processing Systems 23. Advances in Neural Information Processing Systems 23, IPS-10, Vancouver, BC, Canada (2010)), 2352-2360
[133] Viappiani, P.; Boutilier, C., Recommendation sets and choice queries: there is no exploration/exploitation tradeoff!, (Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI-11, San Francisco, CA, USA (2011)), 1571-1574
[134] Viappiani, P.; Faltings, B.; Pu, P., Preference-based search using example-critiquing with suggestions, J. Artif. Intell. Res., 27, 465-503 (2006) · Zbl 1182.68066
[135] Viappiani, P.; Kroer, C., Robust optimization of recommendation sets with the maximin utility criterion, (Proceedings of the Third International Conference on Algorithmic Decision Theory. Proceedings of the Third International Conference on Algorithmic Decision Theory, ADT-13 (2013)), 411-424 · Zbl 1406.91091
[136] Wald, A., Statistical Decision Functions (1950), Wiley: Wiley New York · Zbl 0040.36402
[137] Wang, H.; Wang, N.; Yeung, D.-Y., Collaborative deep learning for recommender systems, (Proceedings of the Twenty-First ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the Twenty-First ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-15, Sydney, Australia (2015)), 1235-1244
[138] Wang, Y.; Ouyang, H.; Wang, C.; Chen, J.; Asamov, T.; Chang, Y., Efficient ordered combinatorial semi-bandits for whole-page recommendation, (Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI-17, San Francisco, CA, USA (2017)), 2746-2753
[139] Weng, P.; Zanuttini, B., Interactive value iteration for Markov decision processes with unknown rewards, (Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13, Beijing, China (2013)), 2415-2421
[140] Wilhelm, M.; Ramanathan, A.; Bonomo, A.; Jain, S.; Chi, E. H.; Gillenwater, J., Practical diversified recommendations on YouTube with determinantal point processes, (Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM-18, Torino, Italy (2018)), 2165-2173
[141] Wu, C.-Y.; Ahmed, A.; Beutel, A.; Smola, A. J.; Jing, H., Recurrent recommender networks, (Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM-17, Cambridge, UK (2017)), 495-503
[142] Yager, R. R., On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Trans. Syst. Man Cybern., 18, 183-190 (1998) · Zbl 0637.90057
[143] Zhang, J.; Pu, P., A comparative study of compound critique generation in conversational recommender systems, (Proceedings of the 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems. Proceedings of the 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH-06, Dublin, Ireland (2006)), 234-243
[144] Zhao, X.; Xia, L.; Zhang, L.; Ding, Z.; Yin, D.; Tang, J., Deep reinforcement learning for page-wise recommendations, (Proceedings of the 12th ACM Conference on Recommender Systems. Proceedings of the 12th ACM Conference on Recommender Systems, RecSys-18, Vancouver, BC, Canada (2018)), 95-103
[145] Ziegler, C.; McNee, S. M.; Konstan, J. A.; Lausen, G., Improving recommendation lists through topic diversification, (Proceedings of the 14th International Conference on World Wide Web. Proceedings of the 14th International Conference on World Wide Web, WWW-05, Chiba, Japan (2005)), 22-32
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.