Mitigating Exposure Bias for Recommendations in Physical Spaces: An Unbiased Pairwise Ranking Approach Using Spatial Movement
References
- (2016) Big data research in information systems: Toward an inclusive research agenda. J. Assoc. Inform. Systems 17(2):1–32.Google Scholar
- (2024) Pathways for design research on artificial intelligence. Inform. Systems Res. 35(2):441–459.Link, Google Scholar
- (2019) Reducing recommender system biases: An investigation of rating display designs. MIS Quart. 43(4):1321–1341.Crossref, Google Scholar
- (2022) Effects of personalized recommendations versus aggregate ratings on post-consumption preference responses. MIS Quart. 46(1):627–644.Crossref, Google Scholar
- (2012) Spatial models for context-aware indoor navigation systems: A survey. J. Spatial Inform. Sci. 1(4):85–123.Google Scholar
- (2006) Discrete choice models of pedestrian walking behavior. Transportation Res. Part B: Methodological 40(8):667–687.Crossref, Google Scholar
- (2020) Learning from positive and unlabeled data: A survey. Machine Learn. 109(4):719–760.Crossref, Google Scholar
- (2011) From point of purchase to path to purchase: How preshopping factors drive unplanned buying. J. Marketing 75(1):31–45.Crossref, Google Scholar
- (2013) Online display advertising: Modeling the effects of multiple creatives and individual impression histories. Marketing Sci. 32(5):753–767.Link, Google Scholar
- (1998) Empirical analysis of predictive algorithms for collaborative filtering. Cooper GF, Moral S, eds. Proc. 14th Conf. Uncertainty Artificial Intelligence (Morgan Kaufmann Publishers, San Francisco, CA), 43–52.Google Scholar
- (2024) Background music recommendation on short video sharing platforms. Inform. Systems Res. 35(4):1890–1908.Link, Google Scholar
- (2023) Bias and debias in recommender system: A survey and future directions. ACM Trans. Inform. Systems 41(3):1–39.Crossref, Google Scholar
- (2019) SamWalker: Social recommendation with informative sampling strategy. Proc. World Wide Web Conf. (ACM, New York), 228–239.Google Scholar
- (2013) Where you like to go next: Successive point-of-interest recommendation. Rossi F, ed. Proc. 23rd Internat. Joint Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 2605–2611.Google Scholar
- (2002) Beyond first impressions: The effects of repeated exposure on consumer liking of visually complex and simple product designs. J. Acad. Marketing Sci. 30(2):119–130.Crossref, Google Scholar
- (2021) Debiased explainable pairwise ranking from implicit feedback. Proc. 15th ACM Conf. Recommender Systems (ACM, New York), 321–331.Google Scholar
- (2022) Debiasing the cloze task in sequential recommendation with bidirectional transformers. Proc. 28th ACM SIGKDD Conf. Knowledge Discovery Data Mining (ACM, New York), 273–282.Google Scholar
- (2020) Shopping mall robots are boosting retail. Accessed July 1, 2025, https://www.electronicspecifier.com/industries/robotics/shopping-mall-robots-are-boosting-retail.Google Scholar
- (2020) Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. ACM Trans. Inform. Systems 39(1):1–42.Crossref, Google Scholar
- (1966) A stochastic model of supermarket traffic flow. Oper. Res. 14(4):555–567.Link, Google Scholar
- (2019) Mobile targeting using customer trajectory patterns. Management Sci. 65(11):5027–5049.Link, Google Scholar
- (1972) Retail turnover in the East Midlands: A regional application of a gravity model. Regional Stud. 6(2):183–196.Crossref, Google Scholar
- (2024) Shopping trip recommendations: A novel deep learning-enhanced global planning approach. Decision Support Systems 182:114238.Crossref, Google Scholar
- (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, 2nd ed. (Springer, New York).Crossref, Google Scholar
- (2021) Gable launches mall kiosk with twin big screens for directory and advertising. Accessed July 1, 2025, https://www.sixteen-nine.net/2021/06/04/gable-launches-mall-kiosk-with-twin-big-screens-for-directory-and-advertising/.Google Scholar
- (2019) Mobile app recommendation: An involvement-enhanced approach. MIS Quart. 43(3):827–849.Crossref, Google Scholar
- (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inform. Retrieval 5(4):287–310.Crossref, Google Scholar
- (2016) Session-based recommendations with recurrent neural networks. Proc. 4th Internat. Conf. Learn. Representations (ICLR, Appleton, WI), 1–10.Google Scholar
- (2004) Pedestrian route-choice and activity scheduling theory and models. Transportation Res. Part B: Methodological 38(2):169–190.Crossref, Google Scholar
- (2008) Collaborative filtering for implicit feedback datasets. Proc. 8th IEEE Internat. Conf. Data Mining (IEEE, New York), 263–272.Google Scholar
- (2009) Path data in marketing: An integrative framework and prospectus for model building. Marketing Sci. 28(2):320–335.Link, Google Scholar
- (2013) The effect of in-store travel distance on unplanned spending: Applications to mobile promotion strategies. J. Marketing 77(2):1–16.Crossref, Google Scholar
- (2022) Debiasing neighbor aggregation for graph neural network in recommender systems. Proc. 31st ACM Internat. Conf. Inform. Knowledge Management (ACM, New York), 4128–4132.Google Scholar
- (2014) Simulation of pedestrians behavior in a shopping mall. Wąs J, Sirakoulis GC, Bandini S, eds. Cellular Automata, vol. 8751 (Springer International Publishing, Cham, Switzerland), 650–659.Crossref, Google Scholar
- (2024) Mitigating exposure bias in recommender systems—A comparative analysis of discrete choice models. ACM Trans. Recommender Systems 3(2):1–37.Crossref, Google Scholar
- (2024) An update on Amazon’s plans for Just Walk Out and checkout-free technology. Accessed July 1, 2025, https://www.aboutamazon.com/news/retail/amazon-just-walk-out-dash-cart-grocery-shopping-checkout-stores.Google Scholar
- (2019) Predicting dynamic embedding trajectory in temporal interaction networks. Proc. 25th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1269–1278.Google Scholar
- (2021) How do product attributes and reviews moderate the impact of recommender systems through purchase stages? Management Sci. 67(1):524–546.Link, Google Scholar
- (2017) Alternating pointwise-pairwise learning for personalized item ranking. Proc. ACM Conf. Information Knowledge Management (ACM, New York), 2155–2158.Google Scholar
- (2004) The effect of multi-purpose shopping on pricing and location strategy for grocery stores. J. Retailing 80(2):85–99.Crossref, Google Scholar
- (2023) When variety seeking meets unexpectedness: Incorporating variety-seeking behaviors into design of unexpected recommender systems. Inform. Systems Res. 35(3):1257–1273.Link, Google Scholar
- (2022) How do recommender systems lead to consumer purchases? A causal mediation analysis of a field experiment. Inform. Systems Res. 33(2):620–637.Link, Google Scholar
- (2017) A social route recommender mechanism for store shopping support. Decision Support Systems 94:97–108.Crossref, Google Scholar
- (2020) Geography-aware sequential location recommendation. Proc. 26th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 2009–2019.Google Scholar
- (2016) Modeling user exposure in recommendation. Proc. 25th Internat. Conf. World Wide Web (International World Wide Web Conferences Steering Committee), 951–961.Google Scholar
- (2019) An empirical study of free product sampling and rating bias. Inform. Systems Res. 30(1):260–275.Link, Google Scholar
- (2023) Mitigating popularity bias for users and items with fairness-centric adaptive recommendation. ACM Trans. Inform. Systems 41(3):1–27.Crossref, Google Scholar
- (2020) Towards context-aware collaborative filtering by learning context-aware latent representations. Knowledge-Based Systems 199:1–13.Crossref, Google Scholar
- (2013) Learning geographical preferences for point-of-interest recommendation. Proc. 19th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1043–1051.Google Scholar
- (2015) Developing visibility analysis for a retail store: A pilot study in a bookstore. Environ. Planning B Planning Desing 42(1):95–109.Crossref, Google Scholar
- (2023) The decoy effect and recommendation systems. Inform. Systems Res. 34(4):1533–1553.Link, Google Scholar
- (2019) The impact of rack layout on visual experience in a retail store. Inform. Systems Oper. Res. 57(1):75–98.Google Scholar
- (2013) CoFiSet: Collaborative filtering via learning pairwise preferences over item-sets. Ghosh J, Obradovic Z, Dy J, Zhou Z-H, Kamath C, Parthasarathy S, eds. Proc. SIAM Internat. Conf. Data Mining (Society for Industrial and Applied Mathematics, Philadelphia), 180–188.Google Scholar
- (2008) One-class collaborative filtering. Proc. 8th IEEE Internat. Conf. Data Mining (IEEE, New York), 502–511.Google Scholar
- (2002) Space syntax based agent simulation. Schreckenberg M, Sharma SD, eds. Pedestrian and Evacuation Dynamics (Springer-Verlag, Berlin), 99–114.Google Scholar
- (2020) Eye-tracking-based classification of information search behavior using machine learning: Evidence from experiments in physical shops and virtual reality shopping environments. Inform. Systems Res. 31(3):675–691.Link, Google Scholar
- (1996) As the crow flies: Bias in consumers’ map-based distance judgments. J. Consumer Res. 23(1):26–39.Crossref, Google Scholar
- (2010) Factorizing personalized Markov chains for next-basket recommendation. Proc. 19th Internat. Conf. World Wide Web (ACM, New York), 811–820.Google Scholar
- (2009) BPR: Bayesian personalized ranking from implicit feedback. Proc. 25th Conf. Uncertainty Artificial Intelligence (AUAI Press, Arlington, VA), 452–461.Google Scholar
- (2009) Specification, estimation and validation of a pedestrian walking behavior model. Transportation Res. Part B: Methodological 43(1):36–56.Crossref, Google Scholar
- (2020) Unbiased recommender learning from missing-not-at-random implicit feedback. Proc. 13th Internat. Conf. Web Search Data Mining (ACM, New York), 501–509.Crossref, Google Scholar
- (2001) Item-based collaborative filtering recommendation algorithms. Proc. 10th Internat. Conf. World Wide Web (ACM, New York), 285–295.Google Scholar
- (2011) Evaluating recommendation systems. Ricci F, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer US, Boston), 257–297.Crossref, Google Scholar
- (2022) Recommendation in offline stores: A gamification approach for learning the spatiotemporal representation of indoor shopping. Proc. 28th ACM SIGKDD Conf. Knowledge Discovery Data Mining (ACM, New York), 3878–3888.Google Scholar
- (2022) Predicting stages in omnichannel path to purchase: A deep learning model. Inform. Systems Res. 33(2):429–445.Link, Google Scholar
- (2020) Are we evaluating rigorously? Benchmarking recommendation for reproducible evaluation and fair comparison. Proc. 14th ACM Conf. Recommender Systems (ACM, New York), 23–32.Google Scholar
- Synced (2020) Cheetah Mobile deploys 8,000 shopping mall robots boosting offline retail. Accessed July 1, 2025, https://syncedreview.com/2020/09/18/cheetah-mobile-deploys-8000-shopping-mall-robots-boosting-offline-retail/.Google Scholar
- (2004) Retail location and consumer spatial choice behavior. Barlow M, Bailly A, Gibson LJ, eds. Applied Geography, vol. 77 (Springer Netherlands, Dordrecht, the Netherlands), 133–147.Crossref, Google Scholar
- (1970) A computer movie simulating urban growth in the Detroit region. Econom. Geography 46:234–240.Crossref, Google Scholar
- (2008) Why We Buy: The Science of Shopping: Updated and Revised for the Internet, the Global Consumer, and Beyond (Simon & Schuster, New York).Google Scholar
- (2012) Moving recommender systems from on-line commerce to retail stores. Inform. Systems E-Bus. Management 10(3):367–393.Crossref, Google Scholar
- (2022) Cross pairwise ranking for unbiased item recommendation. Proc. ACM Web Conf. (ACM, New York), 2370–2378.Google Scholar
- (2018) Position bias estimation for unbiased learning to rank in personal search. Proc. Eleventh ACM Internat. Conf. Web Search Data Mining (ACM, New York), 610–618.Google Scholar
- (2023) Dynamic Bayesian network–based product recommendation considering consumers’ multistage shopping journeys: A marketing funnel perspective. Inform. Systems Res. 35(3):1382–1402.Link, Google Scholar
- (2020) SSE-PT: Sequential recommendation via personalized transformer. Proc. 14th ACM Conf. Recommender Systems (ACM, New York), 328–337.Google Scholar
- (2015) Designing warning messages for detecting biased online product recommendations: An empirical investigation. Inform. Systems Res. 26(4):793–811.Link, Google Scholar
- (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. Proc. 34th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 325–334.Google Scholar
- (2022) Diversity preference-aware link recommendation for online social networks. Inform. Systems Res. 34(4):1398–1414.Link, Google Scholar
- (2021) Location-aware real-time recommender systems for brick-and-mortar retailers. INFORMS J. Comput. 33(4):1608–1623.Abstract, Google Scholar
- (2017) Mining business opportunities from location-based social networks. Proc. 40th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 1037–1040.Google Scholar
- (2016) STELLAR: Spatial-temporal latent ranking for successive point-of-interest recommendation. Proc. AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 315–321.Google Scholar
- (2020) Discovering subsequence patterns for next POI recommendation. Christian B, ed. Proc. 29th Internat. Joint Conf. Artificial Intelligence (International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan), 3216–3222.Google Scholar
- (2008) Cut-off models for the ‘go-home’ decision of pedestrians in shopping streets. Environment. Planning. B Planning Design 35(2):248–260.Crossref, Google Scholar

