Abstract
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.
Similar content being viewed by others
References
Abdul-Rahman A, Hailes S (2000) Supporting trust in virtual communities. In: Proceedings of the 33th annual Hawaii international conference on system sciences. IEEE Press
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):734–749
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
Anderson C (2006) The long tail: how endless choice is creating unlimited demand. Random House, New York
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 43–52
Brynjolfsson E, Hu YJ, Smith MD (2006) From niches to riches: anatomy of the long tail. Sloan Manag Rev 47(4):67–71
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 4724:889
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 328
Dowling GR, Uncles M (1997) Do customer loyalty programs really work? MIT Sloan Manag Rev 38(4):71–82
Ernst & Young (2003) Händler am Scheideweg
Ernst & Young (2005) Consumer trends report
Fleisch E (2001) Business perspectives on ubiquitous computing. Technical report, M-Lab Working Paper
Golbeck J (2005) Computing and applying trust in web-based social networks. PhD thesis, University of Maryland at College Park
Grandison T, Sloman M (2000) A survey of trust in internet applications. IEEE Commun Surv Tutorials 3(4):2–16
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 230–237
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 241–250
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53
Huang Z, Chung W, Chen H (2004) A graph model for E-commerce recommender systems. J Am Soc Inf Sci Technol 55(3):259–274
Kaufman PR (2000) Consolidation in food retailing. Economic Research Service/USDA
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
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 437–446
Kotler P, Armstrong G (2004) Principles of marketing. Prentice Hall, Englewood Cliffs
Kourouthanassis P, Roussos G (2003) Developing consumer-friendly pervasive retail systems. IEEE Pervas Comput 2(2):32–39
KPMG (2006) Trends im Handel 2010
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 237–254
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 393–402
Lawrence RD, Almasi GS, Kotlyar V, Viveros MS, Duri SS (2001) Personalization of supermarket product recommendations. Data Minining Kowl Discov 5(1–2):11–32
Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80
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 296–304
Marsh S (1994) Formalising trust as a computational concept. PhD thesis, University of Stirling
Massa P (2006) Trust-aware decentralized recommender systems. PhD thesis, Università degli Studi di Trento
Mentasys (2007) Guided selling solutions company, case studies
Metro Group (2007) Future store initiative
Mirza BJ, Keller BJ, Ramakrishnan N (2003) Studying recommendation algorithms by graph analysis. J Intell Inf Syst 20(2):131–160
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 304–305
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 164–178
Mundt K, Almquist E, César J (2002) Profitable retailing in a zero-sum game. Mercer Manag J (14):60–69
Newman MEJ, Watts DJ, Strogatz SH (2002) Random graph models of social networks. Proc Natl Acad Sci USA 99(90001):2566–2572
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 167–174
Prudsys AG (2006) Prudsys recommendation engine goes online at Quelle.de
Reichheld FF (2003) The one number you need to grow. Harvard Bus Rev 81:46–54
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 175–186
Rigby DK, Vishwanath V (2006) Localization—the revolution in consumer markets. Harvard Bus Rev 84:82–92
Sabater J, Sierra C (2005) Review on computational trust and reputation models. Artif Intell Rev 24(1):33–60
Sackmann S, Strüker J, Accorsi R (2006) Personalization in privacy-aware highly dynamic systems. Commun ACM 49(9):32–38
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 technology
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 158–167
Sarwar BM, Karypis G, Konstan JA, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: World wide web, pp 285–295
Schafer JB, Konstan JA, Riedl J (2001) E-commerce recommendation applications. Data Min Knowl Discov 5(1/2):115–153
Schröder H, Feller M, Zimmermann G (2003) Retail-Studie. Mercer Management Consulting
Sackmann S, Strüker J (2004) Success factors for electronic customer communication in brick-and-mortar retailing. In: Proceedings of the third international mobile business conference
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 273–300
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):57–74
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 197–204
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):427–434
Weisbuch G, Kirman A, Herreiner D (2000) Market organisation and trading relationships. Econ J 110:411–436
Weiser M (1991) The computer for the 21st century. Sci Am 265(3):94–104
Yildirim M, Walter FE, Battiston S, Schweitzer F (2011) Towards a unified framework for recommender systems (under preparation)
Acknowledgments
We would like to thank Elgar Fleisch, Florian Michahelles, and Dirk Martignoni for their fruitful suggestions on drafts of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Walter, F.E., Battiston, S., Yildirim, M. et al. Moving recommender systems from on-line commerce to retail stores. Inf Syst E-Bus Manage 10, 367–393 (2012). https://doi.org/10.1007/s10257-011-0170-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10257-011-0170-8