Empirical Research on the Impact of Personalized Recommendation Diversity

Simple item page
dc.contributor.authorZhang, Lin
dc.contributor.authorYan, Qiang
dc.contributor.authorLu, Junqiang
dc.contributor.authorChen, Yongqiang
dc.contributor.authorLiu, Yi
dc.date.accessioned2019-01-02T23:51:20Z
dc.date.available2019-01-02T23:51:20Z
dc.date.issued2019-01-08
dc.description.abstractPersonalized recommendation has important implications in raising online shopping efficiency and increasing product sales. There has been wide interest in finding ways to provide more efficient personalized recommendations. Most existing studies focus on how to improve the accuracy of the recommendation algorithms, or are more concerned on ways to increase consumer satisfaction. Unlike these studies, our study focuses on the process of decision-making, using long tail theory as a basis, to reveal the mechanisms involved in consumers’ adoption of recommendations. This paper analyzes the effect of personalized recommendations from two angles: product sales and ratings, and tries to point out differences in consumer preferences between mainstream products and niche products, high rating products and low rating products, search products and experience products. The study verifies that consumers demand diversity in the recommended content, and also provides suggestions on how to better plan and operate a personalized recommendation system.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.159
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59571
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Support for Smart Cities
dc.subjectDecision Analytics, Mobile Services, and Service Science
dc.subjectPersonalized recommendation, Product ratings, Product sales, Long tail, Diversity
dc.titleEmpirical Research on the Impact of Personalized Recommendation Diversity
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0131.pdf
Size:
773.09 KB
Format:
Adobe Portable Document Format
Download

Collections

Decision Support for Smart Cities