Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems

Bernd Heinrich (), Marcus Hopf, Daniel Lohninger, Alexander Schiller and Michael Szubartowicz
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Bernd Heinrich: University of Regensburg
Marcus Hopf: University of Regensburg
Daniel Lohninger: University of Regensburg
Alexander Schiller: University of Regensburg
Michael Szubartowicz: University of Regensburg

Information Systems Frontiers, 2022, vol. 24, issue 1, No 15, 267-286

Abstract: Abstract The rapid development of e-commerce has led to a swiftly increasing number of competing providers in electronic markets, which maintain their own, individual data describing the offered items. Recommender systems are popular and powerful tools relying on this data to guide users to their individually best item choice. Literature suggests that data quality of item content data has substantial influence on recommendation quality. Thereby, the dimension completeness is expected to be particularly important. Herein resides a considerable chance to improve recommendation quality by increasing completeness via extending an item content data set with an additional data set of the same domain. This paper therefore proposes a procedure for such a systematic data extension and analyzes effects of the procedure regarding items, content and users based on real-world data sets from four leading web portals. The evaluation results suggest that the proposed procedure is indeed effective in enabling improved recommendation quality.

Keywords: Completeness; Data extension; Data quality; Recommender system (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10796-020-10071-y

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