Group Validation in Recommender Systems: Framework for Multi-layer Performance Evaluation

TL;DR

This paper introduces a neighborhood-based evaluation framework for recommender systems that enhances understanding of performance variations in data subsets, addressing limitations of traditional metrics.

Contribution

The paper proposes a novel modular neighborhood assessment method for recommender systems, improving detection of performance variations in data subsets.

Findings

Better detection of performance variations in data subsets

Enhanced understanding of system weaknesses

Supports applications like model testing and fraud detection

Abstract

Interpreting the performance results of models that attempt to realize user behavior in platforms that employ recommenders is a big challenge that researchers and practitioners continue to face. Although current evaluation tools possess the capacity to provide solid general overview of a system's performance, they still lack consistency and effectiveness in their use as evident in most recent studies on the topic. Current traditional assessment techniques tend to fail to detect variations that could occur on smaller subsets of the data and lack the ability to explain how such variations affect the overall performance. In this article, we focus on the concept of data clustering for evaluation in recommenders and apply a neighborhood assessment method for the datasets of recommender system applications. This new method, named neighborhood-based evaluation, aids in better understanding critical performance variations in more compact subsets of the system to help spot weaknesses where such variations generally go unnoticed with conventional metrics and are typically averaged out. This new modular evaluation layer complements the existing assessment mechanisms and provides the possibility of several applications to the recommender ecosystem such as model evolution tests, fraud/attack detection and a possibility for hosting a hybrid model setup.