Using PageRank for non-personalized default rankings in dynamic markets
- Publikationstyp:
- Zeitschriftenaufsatz
- Metadaten:
-
- Autoren
- Michael Scholz
- Jella Pfeiffer
- Franz Rothlauf
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000396952000032&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1016/j.ejor.2016.12.022
- eISSN
- 1872-6860
- Externe Identifier
- Clarivate Analytics Document Solution ID: EO8PM
- ISSN
- 0377-2217
- Ausgabe der Veröffentlichung
- 1
- Zeitschrift
- EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
- Schlüsselwörter
- E-commerce
- Product ranking algorithm
- Product domination graph
- Laboratory experiment
- PageRank
- Paginierung
- 388 - 401
- Datum der Veröffentlichung
- 2017
- Status
- Published
- Titel
- Using PageRank for non-personalized default rankings in dynamic markets
- Sub types
- Article
- Ausgabe der Zeitschrift
- 260
Datenquelle: Web of Science (Lite)
- Andere Metadatenquellen:
-
- Autoren
- Michael Scholz
- Jella Pfeiffer
- Franz Rothlauf
- DOI
- 10.1016/j.ejor.2016.12.022
- ISSN
- 0377-2217
- Ausgabe der Veröffentlichung
- 1
- Zeitschrift
- European Journal of Operational Research
- Sprache
- en
- Paginierung
- 388 - 401
- Datum der Veröffentlichung
- 2017
- Status
- Published
- Herausgeber
- Elsevier BV
- Herausgeber URL
- http://dx.doi.org/10.1016/j.ejor.2016.12.022
- Datum der Datenerfassung
- 2018
- Titel
- Using PageRank for non-personalized default rankings in dynamic markets
- Ausgabe der Zeitschrift
- 260
Datenquelle: Crossref
- Autoren
- Michael Scholz
- Jella Pfeiffer
- Franz Rothlauf
- DOI
- 10.1016/j.ejor.2016.12.022
- Zeitschrift
- Eur. J. Oper. Res.
- Artikelnummer
- 1
- Paginierung
- 388 - 401
- Datum der Veröffentlichung
- 2017
- Titel
- Using PageRank for non-personalized default rankings in dynamic markets.
- Ausgabe der Zeitschrift
- 260
Datenquelle: DBLP
- Abstract
- 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.
- Autoren
- Michael Scholz
- Jella Pfeiffer
- Franz Rothlauf
- Ausgabe der Veröffentlichung
- 1
- Schlüsselwörter
- E-commerce
- Product ranking algorithm
- Product domination graph
- Laboratory experiment
- PageRank
- Paginierung
- 388 - 401
- Datum der Veröffentlichung
- 2017
- Titel
- Using PageRank for non-personalized default rankings in dynamic markets
- Ausgabe der Zeitschrift
- 260
Datenquelle: RePEc
- Beziehungen:
- Eigentum von