Metric@CustomerN: Evaluating Metrics at a Customer Level in E-Commerce
Information Retrieval
2023-08-01 v1
Abstract
Accuracy measures such as Recall, Precision, and Hit Rate have been a standard way of evaluating Recommendation Systems. The assumption is to use a fixed Top-N to represent them. We propose that median impressions viewed from historical sessions per diner be used as a personalized value for N. We present preliminary exploratory results and list future steps to improve upon and evaluate the efficacy of these personalized metrics.
Cite
@article{arxiv.2307.16832,
title = {Metric@CustomerN: Evaluating Metrics at a Customer Level in E-Commerce},
author = {Mayank Singh and Emily Ray and Marc Ferradou and Andrea Barraza-Urbina},
journal= {arXiv preprint arXiv:2307.16832},
year = {2023}
}
Comments
Accepted to evalRS 2023@KDD