Information Systems and e-Business Management

, Volume 10, Issue 3, pp 367–393 | Cite as

Moving recommender systems from on-line commerce to retail stores

  • Frank E. WalterEmail author
  • Stefano Battiston
  • Mahir Yildirim
  • Frank Schweitzer
Original Article


The increasing diversity of consumers’ demand, as documented by the debate on the long tail of the distribution of sales volume across products, represents a challenge for retail stores. Recommender systems offer a tool to cope with this challenge. The recent developments in information technology and ubiquitous computing makes it feasible to move recommender systems from the on-line commerce, where they are widely used, to retail stores. In this paper, we aim to bridge the management literature and the computer science literature by analysing a number of issues that arise when applying recommender systems to retail stores: these range from the format of the stores that would benefit most from recommender systems to the impact of coverage and control of recommender systems on customer loyalty and competition among retail stores.


Recommender systems On-line commerce Retail stores Long tail Information overload 



We would like to thank Elgar Fleisch, Florian Michahelles, and Dirk Martignoni for their fruitful suggestions on drafts of this paper.


  1. Abdul-Rahman A, Hailes S (2000) Supporting trust in virtual communities. In: Proceedings of the 33th annual Hawaii international conference on system sciences. IEEE PressGoogle Scholar
  2. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749CrossRefGoogle Scholar
  3. Aggarwal CC, Wolf JL, Wu K-L, Yu PS (1999) Horting hatches an egg: a new graph-theoretic approach to collaborative filtering. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining (KDD ’99). ACM, New York, NY, USA, pp 201–212. doi: 10.1145/312129.312230
  4. Anderson C (2006) The long tail: how endless choice is creating unlimited demand. Random House, New YorkGoogle Scholar
  5. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence (UAI-98), pp 43–52Google Scholar
  6. Brynjolfsson E, Hu YJ, Smith MD (2006) From niches to riches: anatomy of the long tail. Sloan Manag Rev 47(4):67–71Google Scholar
  7. Cinicioglu EN, Shenoy PP, Kocabasoglu C (2007) Use of radio frequency identification for targeted advertising: a collaborative filtering approach using Bayesian networks. Lect Notes Comput Sci 4724:889CrossRefGoogle Scholar
  8. Decker C, Kubach U, Beigl M (2003) Revealing the retail black box by interaction sensing. In: ICDCSW ’03: proceedings of the 23rd international conference on distributed computing systems, IEEE Computer Society, p 328Google Scholar
  9. Dowling GR, Uncles M (1997) Do customer loyalty programs really work? MIT Sloan Manag Rev 38(4):71–82Google Scholar
  10. Ernst & Young (2003) Händler am ScheidewegGoogle Scholar
  11. Ernst & Young (2005) Consumer trends reportGoogle Scholar
  12. Fleisch E (2001) Business perspectives on ubiquitous computing. Technical report, M-Lab Working PaperGoogle Scholar
  13. Golbeck J (2005) Computing and applying trust in web-based social networks. PhD thesis, University of Maryland at College ParkGoogle Scholar
  14. Grandison T, Sloman M (2000) A survey of trust in internet applications. IEEE Commun Surv Tutorials 3(4):2–16Google Scholar
  15. Herlocker JL, Konstan JA, Borchers Al, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: SIGIR ’99: proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval. ACM Press, pp 230–237Google Scholar
  16. Herlocker JL, Konstan JA, Riedl J (2000) Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM conference on computer supported cooperative work (CSCW ’00). ACM Press, pp 241–250Google Scholar
  17. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53CrossRefGoogle Scholar
  18. Huang Z, Chung W, Chen H (2004) A graph model for E-commerce recommender systems. J Am Soc Inf Sci Technol 55(3):259–274CrossRefGoogle Scholar
  19. Kaufman PR (2000) Consolidation in food retailing. Economic Research Service/USDAGoogle Scholar
  20. Kim JH, Lee ES (2005) User XQuery pattern method based personalization recommender service. In: First international conference on semantics, knowledge and grid, Beijing, 99 p. doi: 10.1109/SKG.2005.137
  21. Kitts B, Freed D, Vrieze M (2000) Cross-sell: a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities. In: KDD ’00: proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM Press, pp 437–446Google Scholar
  22. Kotler P, Armstrong G (2004) Principles of marketing. Prentice Hall, Englewood CliffsGoogle Scholar
  23. Kourouthanassis P, Roussos G (2003) Developing consumer-friendly pervasive retail systems. IEEE Pervas Comput 2(2):32–39CrossRefGoogle Scholar
  24. KPMG (2006) Trends im Handel 2010Google Scholar
  25. Krohn A, Zimmer T, Beigl M, Decker C (2005) Collaborative sensing in a retail store using synchronous distributed jam signalling. In: Pervasive computing. Springer, Berlin, pp 237–254Google Scholar
  26. Lam SK, Riedl J (2004) Shilling recommender systems for fun and profit. In: WWW ’04: proceedings of the 13th international conference on world wide web. New York, NY, USA, ACM, pp 393–402Google Scholar
  27. Lawrence RD, Almasi GS, Kotlyar V, Viveros MS, Duri SS (2001) Personalization of supermarket product recommendations. Data Minining Kowl Discov 5(1–2):11–32CrossRefGoogle Scholar
  28. Linden G, Smith B, York J (2003) recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80CrossRefGoogle Scholar
  29. Liu Y-H, Yih J-S, Chieu TC (2004) A personalized offer presentation scheme for retail in-store applications. In: E-commerce and web technologies. Springer, Berlin, pp 296–304Google Scholar
  30. Marsh S (1994) Formalising trust as a computational concept. PhD thesis, University of StirlingGoogle Scholar
  31. Massa P (2006) Trust-aware decentralized recommender systems. PhD thesis, Università degli Studi di TrentoGoogle Scholar
  32. Mentasys (2007) Guided selling solutions company, case studiesGoogle Scholar
  33. Metro Group (2007) Future store initiativeGoogle Scholar
  34. Mirza BJ, Keller BJ, Ramakrishnan N (2003) Studying recommendation algorithms by graph analysis. J Intell Inf Syst 20(2):131–160CrossRefGoogle Scholar
  35. Montaner M, López B, de la Rosa JL (2002) Developing trust in recommender agents. In: Gini M, Ishida T, Castelfranchi C, Johnson WL (eds) Proceedings of the 1st international joint conference on autonomous agents and multiagent systems (AAMAS’02). ACM Press, pp 304–305Google Scholar
  36. Montaner M, López B, de la Rosa JL (2002) Opinion-based filtering through trust. In: Proceedings of the 6th international workshop on cooperative information agents (CIA 2002). Springer, pp 164–178Google Scholar
  37. Mundt K, Almquist E, César J (2002) Profitable retailing in a zero-sum game. Mercer Manag J (14):60–69Google Scholar
  38. Newman MEJ, Watts DJ, Strogatz SH (2002) Random graph models of social networks. Proc Natl Acad Sci USA 99(90001):2566–2572CrossRefGoogle Scholar
  39. O’Donovan J, Smyth B (2005) Trust in recommender systems. In: Proceedings of the 10th international conference on intelligent user interfaces (IUI ’05). ACM Press, pp 167–174Google Scholar
  40. Prudsys AG (2006) Prudsys recommendation engine goes online at Quelle.deGoogle Scholar
  41. Reichheld FF (2003) The one number you need to grow. Harvard Bus Rev 81:46–54Google Scholar
  42. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: CSCW ’94: proceedings of the 1994 ACM conference on computer supported cooperative work. ACM Press, pp 175–186Google Scholar
  43. Rigby DK, Vishwanath V (2006) Localization—the revolution in consumer markets. Harvard Bus Rev 84:82–92Google Scholar
  44. Sabater J, Sierra C (2005) Review on computational trust and reputation models. Artif Intell Rev 24(1):33–60CrossRefGoogle Scholar
  45. Sackmann S, Strüker J, Accorsi R (2006) Personalization in privacy-aware highly dynamic systems. Commun ACM 49(9):32–38CrossRefGoogle Scholar
  46. Sarwar B, Karypis G, Konstan J, Riedl J (2002) Recommender systems for large-scale e-commerce: scalable neighborhood formation using clustering. In: Proceedings of the fifth international conference on computer and information technologyGoogle Scholar
  47. Sarwar B, Karypis G, Konstan J, Riedl J (2000) Analysis of recommendation algorithms for E-commerce. In: Proceedings of the 2nd ACM conference on electronic commerce (EC-2000). ACM Press, pp 158–167Google Scholar
  48. Sarwar BM, Karypis G, Konstan JA, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: World wide web, pp 285–295Google Scholar
  49. Schafer JB, Konstan JA, Riedl J (2001) E-commerce recommendation applications. Data Min Knowl Discov 5(1/2):115–153CrossRefGoogle Scholar
  50. Schröder H, Feller M, Zimmermann G (2003) Retail-Studie. Mercer Management ConsultingGoogle Scholar
  51. Sackmann S, Strüker J (2004) Success factors for electronic customer communication in brick-and-mortar retailing. In: Proceedings of the third international mobile business conferenceGoogle Scholar
  52. Walter FE, Battiston S, Schweitzer F (2008) Coping with information overload through trust-based networks. In: Helbing D (ed) Managing complexity: insights, concepts, applications. Springer, Berlin, pp 273–300Google Scholar
  53. Walter FE, Battiston S, Schweitzer F (2008) A model of a trust-based recommendation system on a social network. J Auton Agents Multi Agent Syst 16(1):57–74CrossRefGoogle Scholar
  54. Walter FE, Battiston S, Schweitzer F (2009) Personalised and dynamic trust in social networks. In: RecSys ’09: proceedings of the third ACM conference on recommender systems. ACM Press, pp 197–204Google Scholar
  55. Wang Y-F, Chuang Y-L, Hsu M-H, Huan-Chao K (2004) A personalized recommender system for the cosmetic business. Exp Syst Appl 26(3):427–434CrossRefGoogle Scholar
  56. Weisbuch G, Kirman A, Herreiner D (2000) Market organisation and trading relationships. Econ J 110:411–436CrossRefGoogle Scholar
  57. Weiser M (1991) The computer for the 21st century. Sci Am 265(3):94–104CrossRefGoogle Scholar
  58. Yildirim M, Walter FE, Battiston S, Schweitzer F (2011) Towards a unified framework for recommender systems (under preparation)Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Frank E. Walter
    • 1
    Email author
  • Stefano Battiston
    • 1
  • Mahir Yildirim
    • 1
    • 2
  • Frank Schweitzer
    • 1
  1. 1.Chair of Systems DesignETH ZurichZurichSwitzerland
  2. 2.Institut für InformatikUniversität FreiburgFreiburgGermany

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