3 papers
cs.IR2023
Metric@CustomerN: Evaluating Metrics at a Customer Level in E-Commerce
Mayank Singh, Emily Ray, Marc Ferradou +1
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. W…
cs.IR2021
Towards Creating a Standardized Collection of Simple and Targeted Experiments to Analyze Core Aspects of the Recommender Systems Problem
Andrea Barraza-Urbina
Imagine you are a teacher attempting to assess a student's level in a particular subject. If you design a test with only hard questions, and the student fails, this mostly proves t…
cs.IR2019
Towards Sharing Task Environments to Support Reproducible Evaluations of Interactive Recommender Systems
Andrea Barraza-Urbina, Mathieu d'Aquin
Beyond sharing datasets or simulations, we believe the Recommender Systems (RS) community should share Task Environments. In this work, we propose a high-level logical architecture…