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Context-Aware Interactive Knowledge-Based Recommendation

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Abstract

Recommender systems have widely been used in the past few years as a recipe to success in e-commerce. Already, 35 percent of what consumers purchase on Amazon and 75 percent of what they watch on Netflix come from product recommendation (MacKenzie et al. in How retailers can keep up with consumers. Technical report. McKinsey & Company, London, 2013). However, in the context of high-end products such as cars, computers, apartments, or financial services, traditional approaches are not the best choice since it is infeasible to collect enough customer behavioral information or feedback over numerous items (Burke et al. in Ai Mag 32:13–8, 2011). Accordingly, since brick and mortar outlets remain the channel of choice for those kinds of products, recommender systems need to adjust to a context in which inventory and customer information are limited. To achieve this goal, we propose a context-aware interactive knowledge-based recommendation to make recommendation to customers who walk into a store, even when no previous information about the customers is available. This technique works in 3 stages: stage 1, contextual pre-filtering, stage 2, interactive exploration and stage 3, constraint-based recommendation. The methodology was developed with an industrial partner in the domain of sport vehicles and is illustrated step by step with an example inspired from this partnership. The proposed method enables an iterative exploration of the products in-store, creates a learning base to learn customers’ interests and recommends a suitable product with regards to logistics, contracts and product’s availability in the network. The method has been tested on our industrial partners’ data and validated by domain experts. This work is a step toward the creation of a continuously evolving learning base of customers’ interests that can be used to adjust sale’s strategy and to develop new products, designs and features that better meet the ever-changing demand of customers.

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References

  1. MacKenzie I, Meyer C, Noble S. How retailers can keep up with consumers. Technical report. McKinsey & Company, London; 2013.

  2. Burke R, Felfernig A, Göker MH. Recommender systems: an overview. Ai Mag. 2011;32:13–8.

    Google Scholar 

  3. Rajamma RK, Paswan AK, Ganesh G. Services purchased at brick and mortar versus online stores, and shopping motivation. J Serv Market. 2007;21(3):200–12.

    Article  Google Scholar 

  4. He C, Parra D, Verbert K. Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst Appl. 2016;56:9–27.

    Article  Google Scholar 

  5. Herlocker JL, Konstan JA, Riedl J. Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM conference on computer supported cooperative work, ACM; 2000. pp. 241–50.

  6. Adomavicius G, Tuzhilin A. Context-aware recommender systems. Recommender systems handbook. Berlin: Springer; 2011. p. 217–53.

    Book  Google Scholar 

  7. Shen Y, Deng Y, Ray A, Jin H. Interactive recommendation via deep neural memory augmented contextual bandits. In: Proceedings of the 12th ACM conference on recommender systems, ACM; 2018. pp. 122–30.

  8. Felfernig A. Reducing development and maintenance efforts for web-based recommender applications. Int J Web Eng Technol. 2007;3(3):329–51.

    Article  Google Scholar 

  9. Derhami S, Montreuil B, Bau G. Assessing product availability in omnichannel retail networks in the presence of on-demand inventory transshipment and product substitution. Omega. 2021;102:102315.

    Article  Google Scholar 

  10. Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng. 2005;17(6):734–49.

    Article  Google Scholar 

  11. Candillier L, Meyer F, Boullé M. Comparing state-of-the-art collaborative filtering systems. In: International workshop on machine learning and data mining in pattern recognition, Springer; 2007. pp. 548–62.

  12. Schafer JB, Frankowski D, Herlocker J, Sen S. Collaborative filtering recommender systems. Berlin, Heidelberg: Springer; 2007. p. 291–324.

    Google Scholar 

  13. Bobadilla J, Ortega F, Hernando A, Gutiérrez A. Recommender systems survey. Knowl-Based Syst. 2013;46:109–32.

    Article  Google Scholar 

  14. Abbas A, Zhang L, Khan SU. A survey on context-aware recommender systems based on computational intelligence techniques. Computing. 2015;97(7):667–90.

    Article  MathSciNet  Google Scholar 

  15. Verbert K, Manouselis N, Ochoa X, Wolpers M, Drachsler H, Bosnic I, Duval E. Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans Learn Technol. 2012;5(4):318–35.

    Article  Google Scholar 

  16. Burke R. Knowledge-based recommender systems. Encycl Libr Inf Syst. 2000;69(Supplement 32):175–86.

    Google Scholar 

  17. Felfernig A, Kiener A. Knowledge-based interactive selling of financial services with fsadvisor. In: Proceedings of the national conference on artificial intelligence, vol. 20, Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999; 2005. p. 1475.

  18. Jiang Z, Wang W, Benbasat I. Multimedia-based interactive advising technology for online consumer decision support. Commun ACM. 2005;48(9):92–8.

    Article  Google Scholar 

  19. Thompson CA, Goker MH, Langley P. A personalized system for conversational recommendations. J Artif Intell Res. 2004;21:393–428.

    Article  Google Scholar 

  20. Towle B, Quinn C. Knowledge based recommender systems using explicit user models. In: Proceedings of the AAAI workshop on knowledge-based electronic markets; 2000. pp. 74–77

  21. Hariri N, Mobasher B, Burke R. Context adaptation in interactive recommender systems. In: Proceedings of the 8th ACM conference on recommender systems, ACM; 2014. pp. 41–48.

  22. Yu T, Mengshoel O, Meroux D, Jiang Z. Machine learning with decision trees and multi-armed bandits: an interactive vehicle recommender system. In: Technical Report 2019-01-1079, SAE International; 2019.

  23. Lacerda A. Multi-objective ranked bandits for recommender systems. Neurocomputing. 2017;246:12–24.

    Article  Google Scholar 

  24. Cunningham MS. The major dimensions of perceived risk. Risk Taking Inf Handl Consumer Behavior. 1967;1967:82–111.

    Google Scholar 

  25. Mitchell V-W. Understanding consumers’ behaviour: can perceived risk theory help? Manage Dec. 1992;30:3.

    Google Scholar 

  26. Munthiu M-C. The buying decision process and types of buying decision behavior. Sibiu Alma Mater Univ J Ser A Econ Sci. 2009;2(4):27–33.

    Google Scholar 

  27. Dinsdale A, Berdichevskiy A. The future is now Transforming the automotive customer experience. In: Technical report, Deloitte Development LLC; 2018.

  28. Bitner MJ. Servicescapes: the impact of physical surroundings on customers and employees. J Market. 1992;56(2):57–71.

    Article  Google Scholar 

  29. Kotler P. Atmospherics as a marketing tool. J Retail. 1973;49(4):48–64.

    Google Scholar 

  30. Solomon MR, Surprenant C, Czepiel JA, Gutman EG. A role theory perspective on dyadic interactions: the service encounter. J Market. 1985;49(1):99–111.

    Article  Google Scholar 

  31. Crosby LA, Stephens N. Effects of relationship marketing on satisfaction, retention, and prices in the life insurance industry. J Market Res. 1987;24(4):404–11.

    Article  Google Scholar 

  32. Crosby LA, Evans KR, Cowles D. Relationship quality in services selling: an interpersonal influence perspective. J Market. 1990;54(3):68–81.

    Article  Google Scholar 

  33. Forbes. Car-buyers want to spend more time with salespeople, study says; 2019. https://www.forbes.com/sites/jacknerad2/2019/01/23/car-buyers-want-to-spend-more-time-with-salespeople-study-says/#af2990260bc4

  34. Helander MG, Khalid HM, Lim TY, Peng H, Yang X. Emotional needs of car buyers and emotional intent of car designers. Theor Issues Ergon Sci. 2013;14(5):455–74.

    Article  Google Scholar 

  35. Khalid HM. Making use of scenarios in human factors assessment validation of citarasa analysis system. In: 2012 Southeast Asian network of ergonomics societies conference (SEANES), IEEE; 2012. pp. 1–6.

  36. Tsai H-C, Hsiao S-W. Evaluation of alternatives for product customization using fuzzy logic. Inf Sci. 2004;158:233–62.

    Article  Google Scholar 

  37. Salvador F, Forza C. Principles for efficient and effective sales configuration design. Int J Mass Custom. 2007;2(1–2):114–27.

    Article  Google Scholar 

  38. Kandaswami K, Tiwar A. Driving through the consumer’s mind: Steps in the buying process. In: Technical report, Deloitte Development LLC; 2014.

  39. Schreiner T, Rese A, Baier D. Multichannel personalization: identifying consumer preferences for product recommendations in advertisements across different media channels. J Retail Consumer Serv. 2019;48:87–99.

    Article  Google Scholar 

  40. Li Q, Li K, You X, Bu S, Liu Z. Place recognition based on deep feature and adaptive weighting of similarity matrix. Neurocomputing. 2016;199:114–27.

    Article  Google Scholar 

  41. Nowak E, Jurie F. Learning visual similarity measures for comparing never seen objects. In: 2007 IEEE conference on computer vision and pattern recognition, IEEE; 2007. pp. 1–8.

  42. Gomaa WH, Fahmy AA, et al. A survey of text similarity approaches. Int J Comput Appl. 2013;68(13):13–8.

    Google Scholar 

  43. Kusiak A, Smith M. Data mining in design of products and production systems. Annu Rev Control. 2007;31(1):147–56.

    Article  Google Scholar 

  44. Nöbauer M, Seyff N, Groher I. Similarity analysis within product line scoping: an evaluation of a semi-automatic approach. In: International conference on advanced information systems engineering, Springer; 2014. pp. 165–79.

  45. Dadouchi C, Agard B. Lowering penalties related to stock-outs by shifting demand in product recommendation systems. Dec Support Syst. 2018;114:61–9.

    Article  Google Scholar 

  46. Subramanian S, Harsha P. Demand modeling in the presence of unobserved lost sales. Manage Sci. 2021;67(6):3803–33.

    Article  Google Scholar 

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Authors and Affiliations

  1. Départment de mathématique et génie industriel, Polytechnique Montréal, Montréal, H3T1J4, QC, Canada

    Camélia Dadouchi & Bruno Agard

  2. Physical Internet Center, H. Milton Stewart School of Industrial and Systems Engineering, Atlanta, 30332, GA, USA

    Benoit Montreuil

Authors
  1. Camélia Dadouchi
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  2. Bruno Agard
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  3. Benoit Montreuil
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Correspondence to Camélia Dadouchi.

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The authors would like to acknowledge the National Sciences and Engineering Research Council of Canada (NSERC) for funding this work under grant RDCPJ 492021-15, and for providing other support for this research. The authors also declare that they have no conflict of interest.

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Dadouchi, C., Agard, B. & Montreuil, B. Context-Aware Interactive Knowledge-Based Recommendation. SN COMPUT. SCI. 3, 472 (2022). https://doi.org/10.1007/s42979-022-01328-1

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