When factorization meets argumentation: towards argumentative explanations

TL;DR

This paper introduces a novel factorization-argumentation hybrid model that enhances interpretability of recommendations by explicitly linking features to arguments, while also improving prediction accuracy with side information.

Contribution

It combines factorization models with argumentation frameworks to produce clear, understandable explanations for recommendations, integrating feature attribution as structured argumentation.

Findings

Outperforms existing argumentation-based methods in accuracy

Competitively matches current context-aware recommendation models

Provides interpretable explanations through explicit argumentation structure

Abstract

Factorization-based models have gained popularity since the Netflix challenge {(2007)}. Since that, various factorization-based models have been developed and these models have been proven to be efficient in predicting users' ratings towards items. A major concern is that explaining the recommendations generated by such methods is non-trivial because the explicit meaning of the latent factors they learn are not always clear. In response, we propose a novel model that combines factorization-based methods with argumentation frameworks (AFs). The integration of AFs provides clear meaning at each stage of the model, enabling it to produce easily understandable explanations for its recommendations. In this model, for every user-item interaction, an AF is defined in which the features of items are considered as arguments, and the users' ratings towards these features determine the strength and polarity of these arguments. This perspective allows our model to treat feature attribution as a structured argumentation procedure, where each calculation is marked with explicit meaning, enhancing its inherent interpretability. Additionally, our framework seamlessly incorporates side information, such as user contexts, leading to more accurate predictions. We anticipate at least three practical applications for our model: creating explanation templates, providing interactive explanations, and generating contrastive explanations. Through testing on real-world datasets, we have found that our model, along with its variants, not only surpasses existing argumentation-based methods but also competes effectively with current context-free and context-aware methods.