Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis

The effectiveness of Recommender Systems (RS) is closely tied to the quality and distinctiveness of user profiles, yet despite many advancements in raw performance, the sensitivity of RS to user profile quality remains under-researched. This paper introduces novel information-theoretic measures for understanding recommender systems: a "surprise" measure quantifying users' deviations from popular choices, and a "conditional surprise" measure capturing user interaction coherence. We evaluate 7 recommendation algorithms across 9 datasets, revealing the relationships between our measures and standard performance metrics. Using a rigorous statistical framework, our analysis quantifies how much user profile density and information measures impact algorithm performance across domains. By segmenting users based on these measures, we achieve improved performance with reduced data and show that simpler algorithms can match complex ones for low-coherence users. Additionally, we employ our measures to analyze how well different recommendation algorithms maintain the coherence and diversity of user preferences in their predictions, providing insights into algorithm behavior. This work advances the theoretical understanding of user behavior and practical heuristics for personalized recommendation systems, promoting more efficient and adaptive architectures.

Causal Inference Tools for a Better Evaluation of Machine Learning

We present a comprehensive framework for applying rigorous statistical techniques from econometrics to analyze and improve machine learning systems. We introduce key statistical methods such as Ordinary Least Squares (OLS) regression, Analysis of Variance (ANOVA), and logistic regression, explaining their theoretical foundations and practical applications in machine learning evaluation. The document serves as a guide for researchers and practitioners, detailing how these techniques can provide deeper insights into model behavior, performance, and fairness. We cover the mathematical principles behind each method, discuss their assumptions and limitations, and provide step-by-step instructions for their implementation. The paper also addresses how to interpret results, emphasizing the importance of statistical significance and effect size. Through illustrative examples, we demonstrate how these tools can reveal subtle patterns and interactions in machine learning models that are not apparent from traditional evaluation metrics. By connecting the fields of econometrics and machine learning, this work aims to equip readers with powerful analytical tools for more rigorous and comprehensive evaluation of AI systems. The framework presented here contributes to developing more robust, interpretable, and fair machine learning technologies.