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Data Quality in Recommender Systems: The Impact of Completeness of Item Content Data on Prediction Accuracy of Recommender Systems

Heinrich, Bernd, Hopf, Marcus, Lohninger, Daniel, Schiller, Alexander and Szubartowicz, Michael (2019) Data Quality in Recommender Systems: The Impact of Completeness of Item Content Data on Prediction Accuracy of Recommender Systems. Electronic Markets (EM).

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Other URL: https://rdcu.be/bPOiB, https://doi.org/10.1007/s12525-019-00366-7


Abstract

Recommender systems strive to guide users, especially in the field of e-commerce, to their individually best choice when a large number of alternatives is available. In general, literature suggests that the quality of data which a recommender system is based on may have important impact on recommendation quality. In this paper, we focus on the data quality dimension completeness of item content ...

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Item type:Article
Date:29 August 2019
Institutions:Business, Economics and Information Systems > Institut für Wirtschaftsinformatik > Lehrstuhl für Wirtschaftsinformatik II (Prof. Dr. Bernd Heinrich)
Identification Number:
ValueType
10.1007/s12525-019-00366-7DOI
Keywords:Completeness, Data Quality, Prediction Accuracy, Recommender Systems
Dewey Decimal Classification:000 Computer science, information & general works > 004 Computer science
300 Social sciences > 330 Economics
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Yes
Item ID:41005
Owner only: item control page
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