Serendipity in Recommender Systems Beyond the Algorithm: A Feature Repository and Experimental Design

Annelien Smets, Lien Michiels, Toine Bogers, Lennart Björneborn

Publikation: Artikel i tidsskrift og konference artikel i tidsskriftKonferenceartikelForskningpeer review

Abstract

Serendipity in recommender systems is ought to improve the quality and usefulness of recommendations. However, despite the increasing amount of attention in both research and practice, designing for serendipity in recommenders continues to be challenging. We argue that this is due to the narrow interpretation of serendipity as an evaluation metric for algorithmic performance. Instead, we venture that serendipity in recommenders should be understood as a user experience that can be influenced by a broad range of system features that go beyond mere algorithmic improvements. In this paper, we propose a first feature repository for serendipity in recommender systems that identifies which elements could theoretically contribute to serendipitous encounters. These include design aspects related to the content, user interface and information access. Furthermore, we outline an experimental design for evaluating the influence of these features on the serendipitous encounters by users. The experiment design is described in such a way that it can be easily reproduced in different recommendation scenarios to contribute empirical insights in various settings. This work aspires to represent a first step towards fostering a more integrated and user-centric view on serendipity in recommender systems and thereby improving our ability to design for it.

OriginalsprogEngelsk
TidsskriftCEUR Workshop Proceedings
Vol/bind3222
Sider (fra-til)46-66
Antal sider21
ISSN1613-0073
StatusUdgivet - 2022
Udgivet eksterntJa
Begivenhed9th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2022 - Seattle, USA
Varighed: 22 sep. 2022 → …

Konference

Konference9th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2022
Land/OmrådeUSA
BySeattle
Periode22/09/2022 → …

Emneord

  • Serendipity
  • Recommender Systems
  • User Experience
  • Feature Repository
  • Experimental Design

Fingeraftryk

Dyk ned i forskningsemnerne om 'Serendipity in Recommender Systems Beyond the Algorithm: A Feature Repository and Experimental Design'. Sammen danner de et unikt fingeraftryk.

Citationsformater