  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.
