The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations

Ningxia Wang, Li CHEN, Yonghua Yang

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

1 Citation (Scopus)

Abstract

Serendipity-oriented recommender systems have increasingly been recognized as useful to overcome the "filter bubble" problem of accuracy-oriented recommenders, by recommending unexpected and relevant items to users. However, most of existing systems are based on researchers' assumptions about the effect of item features on serendipity, but less from users' perspective to study what item features and even user characteristics might affect their perceived serendipity. In this paper, we have attempted to fill in this vacancy based on results of a large-scale user survey (involving over 10,000 users). We have analyzed the correlation between different types of features (i.e., numerical and categorical) with user perceptions, and furthermore identified the interaction effect from user characteristics (such as personality traits and curiosity). We finally discuss the implications of our work to augment the effectiveness of current serendipity-oriented recommender systems.

Original languageEnglish
Title of host publicationUMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages266-274
Number of pages9
ISBN (Electronic)9781450368612
DOIs
Publication statusPublished - 7 Jul 2020
Event28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020 - Genoa, Italy
Duration: 14 Jul 202017 Jul 2020

Publication series

NameUMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020
Country/TerritoryItaly
CityGenoa
Period14/07/2017/07/20

Scopus Subject Areas

  • Software

User-Defined Keywords

  • curiosity
  • item features
  • recommender systems
  • serendipity
  • user personality
  • user survey

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