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Conference proceeding
Candidate Set Sampling for Evaluating Top-N Recommendation
2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp 88-94
26 Oct 2023
Abstract
The strategy for selecting candidate sets - the set of items that the recommendation system is expected to rank for each user - is an important decision in carrying out an offline top- N recommender system evaluation. The set of candidates is composed of the union of the user's test items and an arbitrary number of non-relevant items that we refer to as decoys. Previous studies have aimed to understand the effect of different candidate set sizes and selection strategies on evaluation. In this paper, we extend this knowledge by studying the specific interaction of candidate set selection strategies with popularity bias, and use simulation to assess whether sampled candidate sets result in metric estimates that are less biased with respect to the true metric values under complete data that is typically unavailable in ordinary experiments.
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Details
- Title
- Candidate Set Sampling for Evaluating Top-N Recommendation
- Creators
- Ngozi Ihemelandu - Boise State UniversityMichael D. Ekstrand - Boise State University
- Publication Details
- 2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp 88-94
- Publisher
- IEEE
- Grant note
- IIS 17–51278 / National Science Foundation (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-85182524430
- Other Identifier
- 991021818385804721