Dissertation: On the Theoretical Foundation of Model Comparison and Evaluation for Recommender System

TL;DR

This paper explores the theoretical foundations of comparing and evaluating recommender systems, emphasizing the importance of rigorous methods for assessing their effectiveness in personalized recommendations.

Contribution

It introduces a formal framework for model comparison and evaluation in recommender systems, addressing gaps in existing methodologies.

Findings

Provides a theoretical basis for model evaluation

Highlights limitations of current evaluation metrics

Suggests new approaches for fair comparison

Abstract

Recommender systems have become increasingly important with the rise of the web as a medium for electronic and business transactions. One of the key drivers of this technology is the ease with which users can provide feedback about their likes and dislikes through simple clicks of a mouse. This feedback is commonly collected in the form of ratings, but can also be inferred from a user's browsing and purchasing history. Recommender systems utilize users' historical data to infer customer interests and provide personalized recommendations. The basic principle of recommendations is that significant dependencies exist between user- and item-centric activity, which can be learned in a data-driven manner to make accurate predictions. Collaborative filtering is one family of recommendation algorithms that uses ratings from multiple users to predict missing ratings or uses binary click information to predict potential clicks. However, recommender systems can be more complex and incorporate auxiliary data such as content-based attributes, user interactions, and contextual information.