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 language | English |
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Pages (from-to) | 124-133 |
Number of pages | 10 |
Journal | CEUR Workshop Proceedings |
Volume | 3815 |
Publication status | Published - 2024 |
Event | 11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2024 - Bari, Hybrid, Italy Duration: 18 Oct 2024 → 18 Oct 2024 |
Keywords
- Constraint solving
- Critiquing-based recommender system
- Decision-making
ASJC Scopus subject areas
- General Computer Science