When factorization meets argumentation: towards argumentative explanations
Jinfeng Zhong, Elsa Negre
TL;DR
This paper addresses the explainability gap in factorization-based recommender systems by introducing CA-FATA, which embeds a Tripolar Argumentation Framework into the recommendation process. It treats item features as arguments whose strengths are learned from user-contextualized ratings, enabling not only accurate predictions but also explicit, interpretable justifications. The approach yields context-aware predictions and supports explanation templates, interactive explanations, and contrastive explanations, outperforming context-free and context-aware baselines as well as prior argumentation-based methods on real-world datasets. The work emphasizes interpretability, structured reasoning, and practical applicability, with potential for user studies and integration with interactive systems. CA-FATA thus offers a principled pathway to transparent, context-sensitive recommendations grounded in argumentation theory.
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.