Skip to main content
SpringerLink
Log in

Produktempfehlungssysteme mit minimalem Konsumentenaufwand und hoher Genauigkeit

Ein neuer Ansatz mit rangbasierter Pareto-Front

A Low-Effort Recommendation System with High Accuracy

A New Approach with Ranked Pareto-Fronts

  • Aufsatz
  • Published:
WIRTSCHAFTSINFORMATIK

Zusammenfassung

In aktuellen Arbeiten zu Produktempfehlungssystemen wird die wahlbasierte Conjoint-Analyse zur Messung von Benutzerpräferenzen vorgeschlagen. Diese Methode erzielt eine hohe Empfehlungsqualität und leidet nicht unter dem Start-up-Problem, weil sie auch für neue Nutzer und neue Produkte Empfehlungen generiert. Die Anwendung der wahlbasierten Conjoint- Analyse bedeutet für Konsumenten jedoch einen erheblichen Aufwand, der zu einer Abneigung gegenüber derartigen Empfehlungssystemen führt. In diesem Artikel werden mit einer Simulation die hohe Entscheidungsqualität und der hohe Benutzeraufwand eines nutzenbasierten Systems mit wahlbasierten Conjoint-Analysen mit hierarchischem Bayes’-Schätzer aufgezeigt. Um den Widerspruch zwischen hoher Empfehlungsgüte und niedrigem Aufwand aufzulösen wird ein neuer Ansatz entwickelt, der nur Pareto-effiziente Alternativen zeigt und diese anhand der Anzahl der dominierten Attribute sortiert. Es zeigt sich, dass diese rangbasierte Pareto-Front zu einer besseren Empfehlungsliste führt als die Anwendung der wahlbasierten Conjoint-Analyse. Zudem ist der Aufwand für Konsumenten sehr gering und vergleichbar mit sehr einfachen Sortierverfahren.

Abstract

In recent studies on recommendation systems, the choice-based conjoint analysis has been suggested as a method for measuring consumer preferences. This approach achieves high recommendation accuracy and does not suffer from the start-up problem because it is also applicable for recommendations for new consumers or of new products. However, this method requires massive consumer input, which causes consumer reluctance. In a simulation study, we demonstrate the high accuracy, but also the high user’s effort for using a utility-based recommendation system using a choice-based conjoint analysis with hierarchical Bayes estimation. In order to reduce the conflict between consumer effort and recommendation accuracy, we develop a novel approach that only shows Pareto-efficient alternatives and ranks them according to the number of dominated attributes. We demonstrate that, in terms of the decision accuracy of the recommended products, the ranked Pareto-front approach performs better than a recommendation system that employs choice-based conjoint analysis. Furthermore, the consumer’s effort is kept low and comparable to that of simple systems that require little consumer input.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Abb. 1
Abb. 2
Abb. 3

Notes

  1. Diese Ansätze benötigen Daten anderer Konsumenten in der gleichen Produktkategorie (100 Konsumenten in der gleichen Kategorie sind zumeist ausreichend).

  2. Für eine Einführung in die multiattributive Nutzentheorie siehe Wallenius et al. (2008).

  3. Ein D-optimales Design wird verwendet, um die Varianz der Beobachtungsvariable (hier die Produktauswahl) und die Anzahl an Stimuli, die notwendig sind, um die Haupteffekte (hier die Attribute) zu untersuchen, zu minimieren.

  4. Zum Vergleich der Güte und des Aufwands unserer Systeme berechnen wir ANOVAs mit Tukeys Post-hoc-Test mit einem Signifikanzniveau von 5 %.

  5. Das Sortieren von α Attributausprägungen kostet αld(α) READ- und COMPARE-Operationen.

Literatur

  • 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

    Article  Google Scholar 

  • Aksoy L, Cooil B, Lurie NH (2011) Decision quality measures in recommendation agents research. J Interact Mark 25(2):110–122

    Article  Google Scholar 

  • Ansari A, Essegaier S, Kohli R (2000) Internet recommendation systems. J Mark Res 37(3):363–375

    Article  Google Scholar 

  • Bettman JR, Johnson EJ, Payne JW (1990) A componential analysis of cognitive effort in choice. Organ Behav Hum Decis Process 45(1):111–129

    Article  Google Scholar 

  • Biggs SF, Bedard JC, Gaber BG, Linsmeier TJ (1985) The effect of task size and similarity on the decision behavior of bank loan officers. Manage Sci 31(8):970–987

    Article  Google Scholar 

  • Bröder A, Schiffer S (2003) Bayesian strategy assessment in multi-attribute decision research. J Behav Decis Mak 16(3):193–213

    Article  Google Scholar 

  • Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12(4):331–370

    Article  Google Scholar 

  • Butler JC, Dyer JS, Jia J, Tomak K (2008) Enabling e-transactions with multi-attribute preference models. Eur J Oper Res 186(2):748–765

    Article  Google Scholar 

  • Butler JC, Morrice DJ, Mullarkey PW (2001) A multiple attribute utility theory approach to ranking and selection. Manage Sci 47(6):800–816

    Article  Google Scholar 

  • Cao Y, Li Y (2007) An intelligent fuzzy-based recommendation system for consumer electronic products. Expert Syst Appl 33(1):230–240

    Article  Google Scholar 

  • De Bruyn A, Liechty JC, Huizingh EK, Lilien GL (2008) Offering online recommendations with minimum customer input through conjoint-based decision aids. Mark Sci 27(3):443–460

    Article  Google Scholar 

  • Denstadli JM, Lines R (2007) Conjoint respondents as adaptive decision makers. Int J Mark Res 49(1):117–132

    Google Scholar 

  • Dorner V, Scholz M (2013) Predicting and economically exploiting utility thresholds with utility-based recommendation systems. In: Proc of the 21st eur conf inform syst

    Google Scholar 

  • Fiebig DG, Keane MP, Louviere J, Wasi N (2010) The generalized multinomial logit model: accounting for scale and coefficient heterogeneity. Mark Sci 29(3):393–421

    Article  Google Scholar 

  • Fritz M, Schlereth C, Figge S (2011) Empirical evaluation of fair use flat rate strategies for mobile Internet. Bus Inf Syst Eng 3(5):269–277

    Article  Google Scholar 

  • Gensler S, Hinz O, Skiera B, Theysohn S (2012) Willingness-to-pay estimation with choice-based conjoint analysis: addressing extreme response behavior with individually adapted designs. Eur J Oper Res 219(2):368–378

    Article  Google Scholar 

  • Godfrey P, Shipley R, Gryz J (2006) Algorithms and analyses for maximal vector computation. VLDB J 16(1):5–28

    Article  Google Scholar 

  • Green PE, Krieger AM, Winder Y (2001) Thirty years of conjoint analysis: reflections and prospects. Interfaces 31(3):56–73

    Article  Google Scholar 

  • Häubl G, Trifts V (2000) Consumer decision making in online shopping environments: the effects of interactive decision aids. Mark Sci 19(1):4–21

    Article  Google Scholar 

  • Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28(1):75–105

    Google Scholar 

  • Hey JD (1982) Search for rules for search. J Econ Behav Organ 3:65–81

    Article  Google Scholar 

  • Hinz O, Eckert J (2010) The impact of search and recommendation systems on sales in electronic commerce. Bus Inf Syst Eng 2(2):66–77

    Article  Google Scholar 

  • Hostler RE, Yoon VY, Guo Z, Guimaraes T, Forgionne G (2011) Assessing the impact of recommender agents on on-line consumer unplanned purchase behavior. Inf Manag 48(8):336–343

    Article  Google Scholar 

  • Hoyer WD (1984) An examination of consumer decision making for a common repeat purchase product. J Consum Res 11(3):822–829

    Article  Google Scholar 

  • Huang S (2011) Designing utility-based recommender systems for e-commerce: evaluation of preference-elicitation methods. Electron Commer Res Appl 10(4):398–407

    Article  Google Scholar 

  • Jedidi K, Kohli R, DeSarbo WS (1996) Consideration sets in conjoint analysis. J Mark Res 33(3):364–372

    Article  Google Scholar 

  • Johnson EJ, Payne JW (1985) Effort and accuracy in choice. Manage Sci 31(4):395–414

    Article  Google Scholar 

  • Kamis A, Koufaris M, Stern T (2008) Using an attribute-based decision support system for user-customized products online: an experimental investigation. MIS Q 32(1):159–177

    Google Scholar 

  • Kanninen BJ (2002) Optimal design for multinomial choice experiments. J Mark Res 39(2):214–227

    Article  Google Scholar 

  • Kumar N, Benbasat I (2006) The influence of recommendations and consumer reviews on evaluations of websites. Inf Syst Res 17(4):425–439

    Article  Google Scholar 

  • Lohse GR, Johnson EJ (1996) A comparison of two process tracing methods for choice tasks. Organ Behav Hum Decis Process 68(1):28–43

    Article  Google Scholar 

  • Loomes G, Sugden R (1982) Regret theory: an alternative theory of rational choice under uncertainty. Econ J 92(368):805–824

    Article  Google Scholar 

  • Montgomery A, Hosanagar K, Krishnan R, Clay K (2004) Designing a better shopbot. Manage Sci 50:189–206

    Article  Google Scholar 

  • Moore WL (2004) A cross-validity comparison of rating-based and choice-based conjoint analysis models. Int J Res Mark 21(3):299–312

    Article  Google Scholar 

  • Moore WL, Gray-Lee J, Louviere JJ (1998) A cross-validity comparison of conjoint analysis and choice models at different levels of aggregation. Mark Lett 9(2):195–207

    Article  Google Scholar 

  • Olshavsky RW (1979) Task complexity and contingent processing in decision making: a replication and extension. Organ Behav Hum Perform 24:300–316

    Article  Google Scholar 

  • Payne JW, Bettman JR, Johnson EJ (1993) The adaptive decision maker. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Pfeiffer J (2012) Interactive decision aids in e-commerce. Springer, Berlin

    Book  Google Scholar 

  • Rand W, Rust RT (2011) Agent-based modeling in marketing: guidelines for rigor. Int J Mark Res 28(3):181–193

    Article  Google Scholar 

  • Rieskamp J, Hoffrage U (2008) Inferences under time pressure: how opportunity costs affect strategy selection. Acta Psychol 127(2):258–276

    Article  Google Scholar 

  • Rossi PE, Allenby GM, McCulloch R (2005) Bayesian statistics and marketing. Wiley, West Sussex

    Book  Google Scholar 

  • Schlereth C, Eckert C, Skiera B (2012) Using discrete choice experiments to estimate willingness-to-pay intervales. Mark Lett 23(3):761–776

    Article  Google Scholar 

  • Scholz M, Dorner V (2012) Estimating optimal recommendation set sizes for individual consumers. In: Proc of the 33rd int conf inform syst

    Google Scholar 

  • Svenson O (1979) Process descriptions of decision making. Organ Behav Hum Perform 23:86–112

    Article  Google Scholar 

  • Todd PA, Benbasat I (1992) An experimental investigation of the impact of computer based decision aids on processing effort. MIS Q 16(3):373–393

    Article  Google Scholar 

  • Todd PA, Benbasat I (1994) The influence of decision aids on choice strategies and conditions of high cognitive load. IEEE Trans Syst Man Cybern 24(4):537–547

    Article  Google Scholar 

  • van Ittersum K, Pennings J (2012) Attribute-value functions as global interpretations of attribute importance. Organ Behav Hum Decis Process 119(1):89–102

    Article  Google Scholar 

  • Wallenius J, Dyer JS, Fishburn PC, Steuer RE, Zionts S, Deb K (2008) Multiple criteria decision making, multiattribute utility theory: recent accomplishments and what lies ahead. Manage Sci 54(7):1336–1349

    Article  Google Scholar 

  • Wang W, Benbasat I (2009) Interactive decision aids for consumer decision making in e-commerce: the influence of perceived strategy restrictiveness. MIS Q 33(2):293–320

    Google Scholar 

  • Wohlin C, Runeson P, Höst M, Ohlsson MC, Regnell B, Wesslén A (2012) Experimentation in software engineering. Springer, Berlin

    Book  Google Scholar 

  • Xiao B, Benbasat I (2007) E-commerce product recommendation agents: use, characteristics, and impact. MIS Q 31(1):137–209

    Google Scholar 

  • Yee M, Dahan E, Hauser JR, Orlin J (2007) Greedoid-based noncompensatory inference. Mark Sci 26(4):532–549

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lehrstuhl für Wirtschaftsinformatik und BWL, Johannes Gutenberg-Universität Mainz, Jakob-Welder-Weg 9, 55128, Mainz, Deutschland

    Jella Pfeiffer

  2. Juniorprofessur für Wirtschaftsinformatik mit Schwerpunkt E-Commerce, Universität Passau, Innstr. 43, 94032, Passau, Deutschland

    Michael Scholz

Authors
  1. Jella Pfeiffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Scholz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jella Pfeiffer.

Additional information

Angenommen nach einer Überarbeitung durch Prof. Dr. Hinz.

This article is also available in English via http://www.springerlink.com and http://www.bise-journal.org: Pfeiffer J, Scholz M (2013) A Low-Effort Recommendation System with High Accuracy. A New Approach with Ranked Pareto-Fronts. Bus Inf Syst Eng. doi: 10.1007/s12599-013-0295-z.

Zusätzliche Information

Ergänzendes Onlinematerial kann unter folgendem Link abgerufen werden.

(PDF 500 kB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pfeiffer, J., Scholz, M. Produktempfehlungssysteme mit minimalem Konsumentenaufwand und hoher Genauigkeit. Wirtschaftsinf 55, 395–408 (2013). https://doi.org/10.1007/s11576-013-0388-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11576-013-0388-9

Schlüsselwörter

Keywords

Search

Navigation