The recommender canvas: A model for developing and documenting recommender system design

Guido van Capelleveen, Chintan Amrit, Devrim Murat Yazan, Henk Zijm

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Scopus)
1183 Downloads (Pure)

Abstract

The task of designing a recommender system is a complex process. Because of the many technological advancements that may be included in a recommender system, engineers are faced with a fast growing number of design related decisions to be taken. Unfortunately, there is no general approach yet for decision makers that can act as a framework guiding the design of a recommender system. The rich collection of literature on recommender systems, though, offers a great source to identify the key areas where these decisions need to be taken. In this paper, we survey existing literature with the aim of building a recommender system model inspired by Osterwalder’s canvas theory. The result of our semi-structured synthesis is a novel design approach in the form of a canvas for designing recommender systems. This work provides a better understanding and can serve as a guide for decision making in recommender system design.
Original languageEnglish
Pages (from-to)97-117
Number of pages21
JournalExpert systems with applications
Volume129
Early online date1 Apr 2019
DOIs
Publication statusPublished - 1 Sept 2019

Keywords

  • 2023 OA procedure

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