How users perceive and appraise personalized recommendations

Nicolas Jones*, Pearl Pu, Li CHEN

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

7 Citations (Scopus)

Abstract

Traditional websites have long relied on users revealing their preferences explicitly through direct manipulation interfaces. However recent recommender systems have gone as far as using implicit feedback indicators to understand users' interests. More than a decade after the emergence of recommender systems, the question whether users prefer them compared to stating their preferences explicitly, largely remains a subject of study. Even though some studies were found on users' acceptance and perceptions of this technology, these were general marketing-oriented surveys. In this paper we report an in-depth user study comparing Amazon's implicit book recommender with a baseline model of explicit search and browse. We address not only the question "do people accept recommender systems" but also how or under what circumstances they do and more importantly, what can still be improved.

Original languageEnglish
Title of host publicationUser Modeling, Adaptation, and Personalization - 17th International Conference, UMAP 2009 formerly UM and AH, Proceedings
Pages461-466
Number of pages6
DOIs
Publication statusPublished - 2009
Event17th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2009 - Trento, Italy
Duration: 22 Jun 200926 Jun 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5535 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2009
Country/TerritoryItaly
CityTrento
Period22/06/0926/06/09

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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