Integrating the Mechanisms of Critiquing-based Recommendation into Constraint Solving

Pavle Knežević*, Alexander Felfernig, Sebastian Lubos

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Critiquing-based recommender systems enhance decision-making by guiding users through a product space to find items that meet their preferences. By incorporating feedback in the form of critiques that constrain feature value spaces, these systems refine user profiles to provide more accurate and tailored recommendations. This paper presents a novel approach that integrates critiquing into constraint solving, offering particular benefits for configurable products where finding optimal configurations is complex. We conducted a preliminary offline evaluation of unit-critiquing in the prototype system to gain initial insights into the approach’s efficiency and flexibility. The results suggest that this method has the potential to efficiently generate relevant recommendations, highlighting its promise for addressing challenges in configurable product recommendations.

Original languageEnglish
Pages (from-to)124-133
Number of pages10
JournalCEUR Workshop Proceedings
Volume3815
Publication statusPublished - 2024
Event11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2024 - Bari, Hybrid, Italy
Duration: 18 Oct 202418 Oct 2024

Keywords

  • Constraint solving
  • Critiquing-based recommender system
  • Decision-making

ASJC Scopus subject areas

  • General Computer Science

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