An Interpretable Preference Learning Model Admitting Dynamic and Context-Dependent Preferences

References

• Adomavicius G, Bauman K, Tuzhilin A, Unger M (2021) Context-aware recommender systems: From foundations to recent developments. Ricci F, Rokach L, Shapira B, eds. Recommender Systems Handbook (Springer, New York), 211–250.Google Scholar
• Adomavicius G, Sankaranarayanan R, Sen S, Tuzhilin A (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inform. Systems 23(1):103–145.Crossref, Google Scholar
• Aggarwal M, Fallah Tehrani A (2019) Modelling human decision behaviour with preference learning. INFORMS J. Comput. 31(2):318–334.Link, Google Scholar
• Ansari A, Essegaier S, Kohli R (2000) Internet recommendation systems. J. Marketing Res. 37(3):363–375.Crossref, Google Scholar
• Ansari A, Li Y, Zhang JZ (2018) Probabilistic topic model for hybrid recommender systems: A stochastic variational Bayesian approach. Marketing Sci. 37(6):987–1008.Link, Google Scholar
• Archak N, Ghose A, Ipeirotis PG (2011) Deriving the pricing power of product features by mining consumer reviews. Management Sci. 57(8):1485–1509.Link, Google Scholar
• Bertsimas D, O’Hair A (2013) Learning preferences under noise and loss aversion: An optimization approach. Oper. Res. 61(5):1190–1199.Link, Google Scholar
• Blei DM, Lafferty JD (2006) Dynamic topic models. Cohen W, Moore A, eds. Proc. 23rd Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 113–120.Google Scholar
• Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J. Machine Learn. Res. 3(Jan):993–1022.Google Scholar
• Büschken J, Allenby GM (2016) Sentence-based text analysis for customer reviews. Marketing Sci. 35(6):953–975.Link, Google Scholar
• Chen L, Yan D, Wang F (2019) User evaluations on sentiment-based recommendation explanations. ACM Trans. Interactive Intelligent Systems 9(4):1–38.Crossref, Google Scholar
• Chi CY, Tsai RTH, Lai JY, Hsu JYJ (2010) A reinforcement learning approach to emotion-based automatic playlist generation. 2010 Internat. Conf. Tech. Appl. Artificial Intelligence (IEEE, New York), 60–65.Google Scholar
• Ching AT, Erdem T, Keane MP (2017) Empirical models of learning dynamics: A survey of recent developments. Wierenga B, van der Lans R, eds. Handbook of Marketing Decision Models (Springer, Cham, Switzerland), 223–257.Crossref, Google Scholar
• Corrente S, Greco S, Kadziński M, Słowiński R (2013) Robust ordinal regression in preference learning and ranking. Machine Learn. 93(2):381–422.Crossref, Google Scholar
• Da’u A, Salim N, Rabiu I, Osman A (2020) Recommendation system exploiting aspect-based opinion mining with deep learning method. Inform. Sci. 512:1279–1292.Crossref, Google Scholar
• De Bock KW, Coussement K, Caigny AD, Słowiński R, Baesens B, Boute R, Choi TM, et al. (2024) Explainable AI for operational research: A defining framework, methods, applications, and a research agenda. Eur. J. Oper. Res. 317(2):249–272.Crossref, Google Scholar
• Dew R, Ansari A, Li Y (2020) Modeling dynamic heterogeneity using Gaussian processes. J. Marketing Res. 57(1):55–77.Crossref, Google Scholar
• Dhillon PS, Aral S (2021) Modeling dynamic user interests: A neural matrix factorization approach. Marketing Sci. 40(6):1059–1080.Abstract, Google Scholar
• Fallah Tehrani A, Cheng W, Dembczyński K, Hüllermeier E (2012) Learning monotone nonlinear models using the Choquet integral. Machine Learn. 89(1):183–211.Crossref, Google Scholar
• Farias VF, Li AA (2019) Learning preferences with side information. Management Sci. 65(7):3131–3149.Link, Google Scholar
• Feng Y, Caldentey R, Ryan CT (2022) Robust learning of consumer preferences. Oper. Res. 70(2):918–962.Link, Google Scholar
• Gelman A, Hwang J, Vehtari A (2014) Understanding predictive information criteria for Bayesian models. Statist. Comput. 24(6):997–1016.Crossref, Google Scholar
• Guo X, Shi C, Liu C (2020) Intention modeling from ordered and unordered facets for sequential recommendation. Huang Y, King I, Liu TY, van Steen M, eds. Proc. Web Conf. 2020 (Association for Computing Machinery, New York), 1127–1137.Google Scholar
• He J, Fang X, Liu H, Li X (2019) Mobile app recommendation: An involvement-enhanced approach. MIS Quart. 43(3):827–850.Crossref, Google Scholar
• Ho YC, Wu J, Tan Y (2017) Disconfirmation effect on online rating behavior: A structural model. Inform. Systems Res. 28(3):626–642.Link, Google Scholar
• Hou Y, Li J, He Z, Yan A, Chen X, McAuley J (2024) Bridging language and items for retrieval and recommendation. Preprint, submitted March 6, https://arxiv.org/abs/2403.03952.Google Scholar
• Huang D, Luo L (2016) Consumer preference elicitation of complex products using fuzzy support vector machine active learning. Marketing Sci. 35(3):445–464.Link, Google Scholar
• Jacobs BJD, Donkers B, Fok D (2016) Model-based purchase predictions for large assortments. Marketing Sci. 35(3):389–404.Link, Google Scholar
• Jacobs B, Fok D, Donkers B (2021) Understanding large-scale dynamic purchase behavior. Marketing Sci. 40(5):844–870.Link, Google Scholar
• Kadziński M, Szczepański A (2022) Learning the parameters of an outranking-based sorting model with characteristic class profiles from large sets of assignment examples. Appl. Soft Comput. 116:108312.Crossref, Google Scholar
• Kawaguchi K, Uetake K, Watanabe Y (2021) Designing context-based marketing: Product recommendations under time pressure. Management Sci. 67(9):5642–5659.Link, Google Scholar
• Khern-Am Nuai W, Kannan K, Ghasemkhani H (2018) Extrinsic versus intrinsic rewards for contributing reviews in an online platform. Inform. Systems Res. 29(4):871–892.Link, Google Scholar
• Kim H, Allenby GM (2022) Integrating textual information into models of choice and scaled response data. Marketing Sci. 41(4):815–830.Link, Google Scholar
• Kim M, Zhang J (2023) Discovering online shopping preference structures in large and frequently changing store assortments. J. Marketing Res. 60(4):665–686.Crossref, Google Scholar
• Larsen KR, Bong CH (2016) A tool for addressing construct identity in literature reviews and meta-analyses. MIS Quart. 40(3):529–552.Crossref, Google Scholar
• Lee TY, Bradlow ET (2011) Automated marketing research using online customer reviews. J. Marketing Res. 48(5):881–894.Crossref, Google Scholar
• Li H, Ma L (2020) Charting the path to purchase using topic models. J. Marketing Res. 57(6):1019–1036.Crossref, Google Scholar
• Li Q, Zeng DD, Xu DJ, Liu R, Yao R (2020) Understanding and predicting users’ rating behavior: A cognitive perspective. INFORMS J. Comput. 32(4):996–1011.Abstract, Google Scholar
• Liebman E, Saar-Tsechansky M, Stone P (2019) The right music at the right time: Adaptive personalized playlists based on sequence modeling. MIS Quart. 43(3):765–786.Crossref, Google Scholar
• Liu J, Kadziński M, Liao X (2023) Modeling contingent decision behavior: A Bayesian nonparametric preference-learning approach. INFORMS J. Comput. 35(4):764–785.Link, Google Scholar
• Liu J, Toubia O, Hill S (2021) Content-based model of web search behavior: An application to TV show search. Management Sci. 67(10):6378–6398.Link, Google Scholar
• Liu J, Kadziński M, Liao X, Mao X (2020) Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteria. INFORMS J. Comput. 33(2):586–606.Google Scholar
• Lops P, De Gemmis M, Semeraro G (2011) Content-based recommender systems: State of the art and trends. Ricci F, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer, New York), 73–105.Crossref, Google Scholar
• Lu Y, Castellanos M, Dayal U, Zhai C (2011) Automatic construction of a context-aware sentiment lexicon: An optimization approach. Proc. 20th Internat. Conf. World Wide Web (Association for Computing Machinery, New York), 347–356.Google Scholar
• Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Adv. Neural Inform. Processing Systems 30 NIPS 2017 (Curran Associates, Red Hook, NY), 4768–4777.Google Scholar
• Luo X (2003) The performance implications of contextual marketing for electronic commerce. J. Database Marketing Customer Strategy Management 10(3):231–239.Google Scholar
• Martyn K, Kadziński M (2023) Deep preference learning for multiple criteria decision analysis. Eur. J. Oper. Res. 305(2):781–805.Crossref, Google Scholar
• Murphy KP (2012) Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, MA).Google Scholar
• Natarajan N, Shin D, Dhillon IS (2013) Which app will you use next? Collaborative filtering with interactional context. Proc. 7th ACM Conf. Recommender Systems (Association for Computing Machinery, New York), 201–208.Google Scholar
• Nikolakopoulos AN, Ning X, Desrosiers C, Karypis G (2021) Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems. Ricci F, Rokach L, Shapira B, eds. Recommender Systems Handbook (Springer, New York), 39–89.Google Scholar
• Palmisano C, Tuzhilin A, Gorgoglione M (2008) Using context to improve predictive modeling of customers in personalization applications. IEEE Trans. Knowledge Data Engrg. 20(11):1535–1549.Crossref, Google Scholar
• Pelissari R, Abackerli AJ, Duarte LT (2022) Choquet capacity identification for multiple criteria sorting problems: A novel proposal based on stochastic acceptability multicriteria analysis. Appl. Soft Comput. 120:108727.Crossref, Google Scholar
• Peterson JC, Bourgin DD, Agrawal M, Reichman D, Griffiths TL (2021) Using large-scale experiments and machine learning to discover theories of human decision-making. Science 372(6547):1209–1214.Crossref, Google Scholar
• Puranam D, Narayan V, Kadiyali V (2017) The effect of calorie posting regulation on consumer opinion: A flexible latent Dirichlet allocation model with informative priors. Marketing Sci. 36(5):726–746.Link, Google Scholar
• Ran Y, Liu J, Zhang Y (2023) Integrating users’ contextual engagements with their general preferences: An interpretable followee recommendation method. INFORMS J. Comput. 35(3):614–632.Link, Google Scholar
• Rayport JF, Sviokla JJ (1994) Managing in the marketspace. Harvard Bus. Rev. 72(6):141–150.Google Scholar
• Ribeiro MT, Singh S, Guestrin C (2016) “Why should I trust you?” Explaining the predictions of any classifier. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 1135–1144.Google Scholar
• Roberts K, Roberts JH, Danaher PJ, Raghavan R (2015) Practice prize paper—Incorporating emotions into evaluation and choice models: Application to Kmart Australia. Marketing Sci. 34(6):815–824.Link, Google Scholar
• Ru Z, Liu J, Kadziński M, Liao X (2023) Probabilistic ordinal regression methods for multiple criteria sorting admitting certain and uncertain preferences. Eur. J. Oper. Res. 311(2):596–616.Crossref, Google Scholar
• Ru Z, Liu J, Kadziński M, Liao X, Li X (2026) An interpretable preference learning model admitting dynamic and context-dependent preferences. https://doi.org/10.1287/ijoc.2023.0372.cd, https://github.com/INFORMSJoC/2023.0372.Google Scholar
• Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1(5):206–215.Crossref, Google Scholar
• Tirunillai S, Tellis GJ (2014) Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent Dirichlet allocation. J. Marketing Res. 51(4):463–479.Crossref, Google Scholar
• Trueblood JS, Brown SD, Heathcote A, Busemeyer JR (2013) Not just for consumers: Context effects are fundamental to decision making. Psych. Sci. 24(6):901–908.Crossref, Google Scholar
• Tversky A, Simonson I (1993) Context-dependent preferences. Management Sci. 39(10):1179–1189.Link, Google Scholar
• Wang Y, Chaudhry A (2018) When and how managers’ responses to online reviews affect subsequent reviews. J. Marketing Res. 55(2):163–177.Crossref, Google Scholar
• Wang XJ, Liu T, Fan W (2023) TGVx: Dynamic personalized POI deep recommendation model. INFORMS J. Comput. 35(4):786–796.Link, Google Scholar
• Wu S, Tan Y, Chen Y, Liang Y (2022) How is mobile user behavior different? A hidden Markov model of cross-mobile application usage dynamics. Inform. Systems Res. 33(3):1002–1022.Link, Google Scholar
• Xie M, Yin H, Wang H, Xu F, Chen W, Wang S (2016) Learning graph-based POI embedding for location-based recommendation. Proc. 25th ACM Internat. Conf. Inform. Knowledge Management (Association for Computing Machinery, New York), 15–24.Google Scholar
• Yin K, Fang X, Chen B, Liu Sheng OR (2023) Diversity preference-aware link recommendation for online social networks. Inform. Systems Res. 34(4):1398–1414.Link, Google Scholar
• Zhang Y, Chen X (2020) Explainable recommendation: A survey and new perspectives. Foundation Trends Inform. Retrieval 14(1):1–101.Crossref, Google Scholar
• Zhang Y, Lai G, Zhang M, Zhang Y, Liu Y, Ma S (2014) Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. Proc. 37th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 83–92.Google Scholar