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Suche nach „[J.] [Pfeiffer]“ hat 2 Publikationen gefunden
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    DigitalF: Angewandte WirtschaftswissenschaftenS: TC Grafenau

    Zeitschriftenartikel

    Michael Scholz, J. Pfeiffer, F. Rothlauf

    Using PageRank for non-personalized default rankings in dynamic markets

    European Journal of Operational Research, vol. 260, no. 1, pp. 388-401

    2017

    DOI: 10.1016/j.ejor.2016.12.022

    Abstract anzeigen

    Default ranking algorithms are used to generate non-personalized product rankings for standard consumers, for example, on landing pages of online stores. Default rankings are created without any information about the consumers’ preferences. This paper proposes using the product centrality ranking algorithm (PCRA), which solves some problems of existing default ranking algorithms: Existing approaches either have low accuracy, because they rely on only one product attribute, or they are unable to estimate ranks for new or updated products, because they use past consumer behavior, such as previous sales or ratings. The PCRA uses the PageRank centrality of products in a product domination graph to determine their ranks. The product domination graph models products as nodes and the dominance relations between the products’ attribute levels as edges. In a laboratory experiment with three product categories (energy saving lamps, hotel rooms, and washing machines), the PCRA leads to more accurate rankings than existing approaches provide. The PCRA ranks the lamps and washing machines that consumers prefer up to 1.5 positions higher in the default ranking than any of the existing algorithms. Only sorting hotel rooms’ price in ascending order beats the PCRA. Price is by far the most important attribute of hotel rooms for our consumer sample; therefore, a ranking that only considers price can beat a multi-attribute ranking like the PCRA, which assumes equal attribute weights. In summary, the PCRA is especially applicable to products where consumers consider more than one attribute and in markets where the product assortments change constantly.

    DigitalF: Angewandte WirtschaftswissenschaftenS: TC Grafenau

    Zeitschriftenartikel

    J. Pfeiffer, Michael Scholz

    A Low-Effort Recommendation System with High Accuracy

    Business & Information Systems Engineering, vol. 5, no. 6, pp. 397-408

    2013

    DOI: 10.1007/s12599-013-0295-z

    Abstract anzeigen

    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. In a simulation study, we demonstrate that recommendation systems using a choice-based conjoint analysis with hierarchical Bayes estimation require up to three times higher mental effort for the consumer than simple sorting mechanisms. However, consumers benefit from a choice-based conjoint analysis in terms of a significantly higher utility of the selected product. We further introduce the concept of a ranked Pareto-front which allows consumers to select a product with a better utility than they will select when using a choice-based conjoint analysis for the same low costs that using a simple sorting mechanism require.