# Argumentation meets matrix factorization: A dual perspective for explainable recommendations

Jinfeng Zhong, Elsa Negre

► To cite this version:

Jinfeng Zhong, Elsa Negre. Argumentation meets matrix factorization: A dual perspective for explainable recommendations. Applied Soft Computing, 2025, 183, pp.113652. 10.1016/j.asoc.2025.113652 hal-05397350

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# Argumentation meets matrix factorization: A dual perspective for
explainable recommendations

Jinfeng Zhong<sup>a,*</sup>, Elsa Negre<sup>b,†</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

**Keywords:**

Argumentation

Explainable recommendation

Matrix factorization

## ABSTRACT

Factorization-based models gained prominence during the Netflix Challenge (2007) and have since demonstrated strong performance in predicting user ratings. However, their limited interpretability often hinders users from understanding the rationale behind recommendations. In contrast, argumentation-based methods offer a different perspective: they model human-like reasoning by structuring information as arguments and counterarguments. They excel in explainability but typically fall short in accuracy. To address this trade-off, we propose a novel framework, Context-Aware Feature-Attribution Through Argumentation (CA-FATA), which combines the predictive power of matrix factorization with the interpretability of argumentation frameworks. In CA-FATA, each user–item interaction is modeled using an argumentation framework. Items' features are represented as arguments, and users' ratings determine the arguments' strengths. Additionally, the model incorporates users' contextual information (e.g., time, location) to further improve predictive performance. Empirical evaluations on real-world datasets show that CA-FATA excels in predictive accuracy and interpretability. It outperforms existing argumentation-based methods and achieves comparable results with state-of-the-art context-free and context-aware models. CA-FATA also supports multiple explanation formats, including template-based explanations, interactive feedback, and contrastive reasoning. Furthermore, it alleviates the cold-start problem by clustering users based on feature preferences.

## 1. Introduction

Recommender systems play a central role in modern digital platforms, enabling personalized experiences across domains such as e-commerce [1], streaming services [2], and social media [3]. By analyzing user preferences to suggest relevant content, these systems enhance user satisfaction [4], alleviate information overload, and support informed decision-making for both users and businesses.

Among the recommender system approaches, factorization-based models have attracted significant attention due to their ability to handle large-scale data efficiently. Following their success in the Netflix Challenge (2007) [5], these models have evolved to incorporate increasingly complex mechanisms. Matrix Factorization (MF) [6] predicts user preferences through the inner product of user and item embeddings. Factorization Machines (FM) [7] extend this capability by modeling second-order interactions among additional data, such as items' features. Neural Factorization Machines (NFM) [8] and Attentional Factorization Machines (AFM) [9] introduce neural components and attention mechanisms to increase expressiveness. Neural Matrix Factorization (NeuMF) [10] further integrates MF with a Multi-Layer Perceptron

(MLP), improving generalization. More recently, factorization-based methods have been enhanced with deep learning methods to achieve stronger expressive power [11–20].

Despite their predictive strength, these models are often criticized for their lack of transparency. For example, although MLPs enhance generalization, they do so at the cost of reduced interpretability. As a result, explaining the rationale behind recommendations generated by these models remains challenging. Efforts to improve explainability often rely on identifying “relevant users” to justify recommendations [21]. However, this provides limited insight, as end-users cannot access other users’ ratings. This limitation raises a critical research question: Can the predictive accuracy of factorization-based methods be retained while enhancing the explainability of their recommendations?

In parallel, Argumentation Frameworks (AFs) have emerged as promising tools in explainable artificial intelligence (XAI) [22–24]. An AF provides a graphical representation of decision-making processes, enabling reasoning about argument strength and acceptability [23,24]. In recommender systems, AFs have been employed to generate explainable recommendations by structuring interactions as

<footnote><sup>a</sup> Paris-Dauphine University, PSL Research Universities, CEREMADE, UMR CNRS 7534, 75016 Paris, France</footnote>

<footnote><sup>b</sup> Paris-Dauphine University, PSL Research Universities, LAMSADE, UMR CNRS 7243, 75016 Paris, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: jinfeng.zhong@dauphine.eu (J. Zhong), elsa.negre@lamsade.dauphine.fr (E. Negre).</footnote>
arguments and counterarguments [25–27]. However, despite their ex-
planatory strength, these approaches face several limitations, including
the following.

*   **Fixed feature importance across users:** The importance of feature types (e.g., genre, director, or actor in movie recommendations) is set empirically and remains the same for all users, ignoring the variability in user preferences [28].

*   **Lack of user context:** These methods fail to incorporate contextual information, which is essential for capturing dynamic user preferences and tailoring recommendations [11,14,29–32].

*   **Limited predictive accuracy:** Argumentation-based approaches generally underperform compared to factorization-based methods in terms of prediction accuracy [25–27].

To address these issues, we propose Context-Aware Feature-At-
tribution Through Argumentation (CA-FATA), a novel framework
that unifies the strengths of factorization-based models and AFs. Build-
ing upon argumentation-based recommenders [25–27], CA-FATA in-
troduces two key innovations: (1) It dynamically computes feature
importance tailored to individual users, and (2) It integrates contextual
information (e.g., temporal and spatial signals) to reflect users’ evolving
preferences.

Our work is validated through theoretical analysis and empirical
studies, demonstrating its effectiveness in improving both prediction
accuracy and interpretability. Theoretical analysis establishes that the
strength function of arguments under the AF for each user–item in-
teraction satisfies two key properties: weak balance [25] and weak
monotonicity [26]. These properties ensure that the explanations de-
rived from our framework are both intuitive and logically coherent
within an AF. Complementing this, empirical evaluations on real-world
datasets reveal that our model and its variations not only outperform
existing argumentation-based methods, but also compete effectively
with state-of-the-art context-free and context-aware recommender sys-
tems. In addition, we identify three principal applications for our
framework: (1) generating explanation templates; (2) enabling inter-
active explanations; (3) producing contrastive explanations. Finally,
our model supports user clustering, which helps address the cold-start
problem<fnref n="1" /> in recommender systems [33].

In summary, this work offers two primary contributions:

*   **From the perspective of argumentation:** We leverage MF [5] to calculate the strengths of the arguments, thus improving predictive precision while retaining explainability.

*   **From the perspective of MF:** We embed the rating prediction steps into an AF following [25–27], mapping computations to explicit and interpretable stages, thereby enhancing the explainability of factorization-based methods.

The organization of this paper is as follows: Section 2 reviews related work and background on factorization-based methods and argumentation frameworks. Section 3 describes our framework in detail. Section 4 explains how our framework facilitates the derivation of argumentative explanations and outlines three envisioned applications. Section 5 presents the results of our experiments using two real-world datasets, demonstrating that our framework maintains explainability without compromising accuracy. Finally, Section 6 concludes the paper and suggests directions for future research.

## 2. Related work and background

To standardize the terminology used in the rest of this paper, we present the key notions in Table 1.

<figure><img src="image_4.png" /><figcaption>Fig. 1. The core idea of Vanilla MF [6]. $\mathcal{R}$ is the rating matrix, each row is a user, each column is an item, e.g. $r_{(i,1)}$ denotes the rating that first user gives to the first item; $\mathcal{P}$ is the matrix representing users (e.g. $\mathbf{p}_i$ is the vector that represents the first user); $\mathcal{Q}^T$ is the matrix representing items (e.g. $\mathbf{q}_i^T$ is the vector that represents the first item). For example, to predict $r_{(u,1)}$ (the blue space in the upper left of the matrix $\mathcal{R}$), Vanilla MF uses the inner product of $\mathbf{p}_1$ (the vector marked as blue in matrix $\mathcal{P}$) and $\mathbf{q}_i^T$ (the vector marked as blue in matrix $\mathcal{Q}^T$).</figcaption></figure>

2.1. Factorization-based methods

Factorization-based models, also known as latent factor approaches [34], aim to uncover hidden structures in user–item interactions that effectively predict ratings. These models encompass a range of techniques, including probabilistic Latent Semantic Analysis (pLSA) [35], Latent Dirichlet Allocation (LDA) [36], and Matrix Factorization (MF) [6]. Among these, MF has emerged as the most widely adopted, largely due to its success in the Netflix Prize competition [5] and its scalability on large datasets. In this section, we provide a comparative overview of existing factorization-based methods. Additionally, we address the explainability challenges inherent to these models, offering insights into their limitations and potential improvements.

Vanilla MF [6] predicts ratings by computing the inner product of user and item vectors. Each user is associated with a vector $\mathbf{p}_u \in \mathbb{R}^d$, each item with a vector $\mathbf{q}_i \in \mathbb{R}^d$, and $\hat{r}_{(u,i)}$ (the predicted rating that user $u$ gives to item $i$) is computed as the inner product of $\mathbf{p}_u$ and $\mathbf{q}_i$. The core idea of Vanilla MF is depicted in Fig. 1. For example, when predicting the rating of the first user to the first item (upper left of matrix $\mathcal{R}$, in Fig. 1), Vanilla MF uses the inner product of $\mathbf{p}_1$ (the vector marked blue in $\mathcal{P}$ of Fig. 1) and $\mathbf{q}_1^T$ (the vector marked blue in $\mathcal{Q}^T$ of Fig. 1).

$$
\hat{r}_{(u,i)} = \mathbf{p}_u \mathbf{q}_i^T \qquad (1)
$$

Factorization Machines (FM) [7] extend MF by modeling not only user–item interactions but also higher-order interactions among additional features, such as users’ contexts and items’ attributes. It captures the second-order interactions of feature vectors (see Eq. (2)), thereby enriching FM’s expressiveness. However, interactions involving less relevant features may introduce noise, since all feature interactions are weighted equally [9].

$$
\hat{y}_{FM}(\mathbf{X}) = w_0 + \sum_{j=1}^{n} w_j x_j + \sum_{j=1}^{n} \sum_{k=j+1}^{n} \mathbf{v}_j^T \mathbf{v}_k x_j x_k \quad (2)
$$

where $\mathbf{X} \in \mathbb{R}^n$ is the feature vector, $n$ denotes the number of features, $w_0$ is the global bias, $w_j$ is the bias of the $j$th feature and $\mathbf{v}_j^T \mathbf{v}_k$ denotes the bias of interaction between the $j$th feature and the $k$th feature, $\mathbf{v}_j \in \mathbb{R}^d$ is the vector representing $j$th feature. Note that $w_0 + \sum_{j=1}^n w_j x_j$ corresponds to the output of a linear regression; $\sum_{j=1}^n \sum_{k=j+1}^n \mathbf{v}_j^T \mathbf{v}_k x_j x_k$ is the second-order interaction; $x_j$ and $x_k$ are one-hot encodings, which ensures that only non-zero features are considered.

Neural Factorization Machines (NFM) [8] build upon FM [7] by
introducing a Bilinear Interaction: $f_{BI}(V_x) = \sum_{j=1}^{n} \sum_{k=j+1}^{n} x_j v_j \odot v_k x_k$, where $\odot$ denotes the element-wise product. The primary idea is that the Bilinear Interaction encodes the second-order interactions of vectors representing features. Subsequently, a stack of fully connected layers is incorporated to capture the high-order interactions of vectors, as shown in Eq. (3).

$$
\hat{y}_{NFM}(\mathbf{X}) = w_0 + \sum_{j=1}^{n} w_j x_j + h^T \sigma_L (\mathbf{W}_L (\dots \sigma_1 (\mathbf{W}_1 f_{BI}(V_x) + \mathbf{b}_1) \dots) + \mathbf{b}_L) \quad (3)
$$

<footnote n="1">¹ When new users arrive, the system has no historical interaction data for them. Therefore, it is challenging to provide accurate recommendation.</footnote>
<table><caption>**Table 1**
The key notions in this paper.</caption><thead><tr><th>Concept</th><th>Description</th></tr></thead><tbody><tr><td>R<sup>+</sup>, R<sup>-</sup>, R<sup>0</sup></td><td>Relations: support, attack, and neutralize</td></tr><tr><td>R<sup>+</sup>(a,b)</td><td>Argument a supports argument b</td></tr><tr><td>R<sup>-</sup>(a,b)</td><td>Argument a attacks argument b</td></tr><tr><td>R<sup>0</sup>(a,b)</td><td>Argument a neutralizes argument b</td></tr><tr><td>A</td><td>A set of arguments</td></tr><tr><td>R<sup>+</sup>(a) = {b|(b,a) ∈ R<sup>+</sup>}</td><td>A set of arguments that support argument a</td></tr><tr><td>R<sup>-</sup>(a) = {b|(b,a) ∈ R<sup>-</sup>}</td><td>A set of arguments that attack argument a</td></tr><tr><td>R<sup>0</sup>(a) = {b|(b,a) ∈ R<sup>0</sup>}</td><td>A set of arguments that neutralize argument a</td></tr><tr><td>&lt;A, R<sup>-</sup>, R<sup>+</sup>, R<sup>0</sup>&gt;</td><td>A tripolar argumentation framework</td></tr><tr><td>&lt;A, R<sup>-</sup>, R<sup>+</sup>&gt;</td><td>A bipolar argumentation framework</td></tr><tr><td>&lt;A, R<sup>+</sup>&gt;</td><td>An abstract argumentation framework</td></tr><tr><td>σ(a)</td><td>The strength of argument a</td></tr><tr><td>r<sub>(a,i)</sub></td><td>The rating that user i gives to item i</td></tr><tr><td>attr<sub>i</sub></td><td>The features of item i</td></tr><tr><td>π<sup>t</sup><sub>u</sub></td><td>The importance of type t to user u</td></tr><tr><td>t<sub>i</sub></td><td>The feature types of item i</td></tr><tr><td>attr<sup>t</sup><sub>i</sub></td><td>The features of item i of type t (e.g. A movie may have several genres)</td></tr><tr><td>P<sup>attr</sup><sub>u</sub></td><td>The predicted rating of user u towards feature attr</td></tr><tr><td>rec<sup>t</sup></td><td>An argument stating “the item can be recommended to the target user”</td></tr><tr><td>cf</td><td>A contextual factor</td></tr><tr><td>C</td><td>The contextual factors to characterize the situation of users</td></tr><tr><td>cd</td><td>A contextual condition</td></tr><tr><td>π<sup>c f</sup><sub>u</sub></td><td>The importance of contextual factor cf to user u</td></tr><tr><td>cs = (cd<sub>1</sub>, cd<sub>2</sub>, cd<sub>3</sub>, ...)</td><td>A contextual situation</td></tr><tr><td>π<sup>t</sup><sub>u,i</sub></td><td>The importance of type t to user u under cs</td></tr></tbody></table>

where L is the number of fully connected layers. By setting L = 0 and h<sup>T</sup> = 1, FM can be derived as a special case. NFM extends FM by integrating an MLP to capture higher-order feature interactions.

**Attentional Factorization Machines (AFM) [9]** capture the second-order interactions of vectors representing users, items, and other features, similar to FM. Additionally, it models the attention of each interaction using an MLP (see Eq. (4)).

$$ \hat{y}_{AFM}(\mathbf{X}) = w_0 + \sum_{j=1}^{n} w_j x_j + \mathbf{p}^T \sum_{j=1}^{n} \sum_{k=j+1}^{n} a_{jk}(\mathbf{v}_j \odot \mathbf{v}_k) x_j x_k \quad (4) $$

where $a_{jk}$ is the importance of feature interaction, $\odot$ is the element-wise product. By setting $\mathbf{p}^T$ to 1 and $a_{jk}$ to 1, FM is exactly recovered. AFM extends FM by learning an attention weight for each feature interaction, enabling more nuanced modeling of their importance.

Factorization-based models encompass a wide range of approaches. They are extensively utilized in recommender systems due to their scalability and efficiency [11–14]. However, explaining the recommendations generated by these models remains a significant challenge. The latent factors they learn often lack human-comprehensible interpretations, hindering transparent and meaningful explanations [21]. As a result, researchers frequently turn to post-hoc explanation techniques to clarify the reasoning behind these recommendations [37–39]. To address this limitation, we propose combining factorization-based approaches with AFs to enable direct and interpretable explanations for recommendations. The following subsection introduces the foundational concepts of argumentation that underpin our model.

## 2.2. Argumentation frameworks

An AF models reasoning as a set of arguments along with the relations between them [40]. An argument represents a proposition, claim, or piece of evidence, and relations such as attack, support, or neutralize capture how arguments interact with each other. In recent years, the prominence of argumentation-based methods in XAI has grown significantly due to the clarity of relations presented by an AF [22–24].

Among the various types of AFs, we focus on three commonly used ones: the Abstract Argumentation Framework (AAF) [41], the Bipolar Argumentation Framework (BAF) [42] and Tripolar Argumentation Framework (TAF) [43]:

* An AAF is a pair consisting of a set of arguments and a set of attacks: $< \mathcal{A}, R^{-} >$, where $R^{-}$ denotes a set of attack relations between arguments such that $\forall a_1, a_2 \in \mathcal{A}, (a_1, a_2) \in R^{-}$ denotes that argument $a_1$ attacks argument $a_2$. The relation “attacks” indicates a contradiction between two arguments. For example, considering argument $a_1$ “This user does not like war movies.” and argument $a_2$ “This war movie can be recommended to this user”. In this case, $a_1$ attacks $a_2$.

* A BAF extends an AAF by adding a support relation: $< \mathcal{A}, R^{-}, R^{+} >$, where $\mathcal{A}$ is a set of arguments, $R^{-}$ represents attack as in an AAF. Meanwhile, $R^{+}$ denotes a set of support relations between arguments such that $\forall a_1, a_2 \in \mathcal{A}, (a_1, a_2) \in R^{+}$ denotes that argument $a_1$ supports argument $a_2$. For example, considering argument $a_1$ “This user likes war movies.” and argument $a_2$ “This war movie can be recommended to this user”. Then, $a_1$ supports $a_2$.

* A TAF further introduces a neutralizing relation: $< \mathcal{A}, R^{-}, R^{+}, R^{0} >$, where $\mathcal{A}$ is a set of arguments, $R^{-}$ represents the attack relations, $R^{+}$ denotes the support relation and $R^{0}$ means neutralizing relation. Suppose a user strongly favors films with Actor X. Consider two movies: one belonging solely to the user's favorite genre (e.g., science fiction), and another that combines this genre with several others for which the user has neutral or no particular preference (e.g., romance, documentary, or musical). In this case, the presence of additional genres dilutes the positive effect of the preferred genre, thereby diminishing the overall strength of the recommendation.

In the AFs applied to recommender systems, it is common to associate each argument a with a numeric strength score $\sigma(a)$, which quantifies its persuasive strength [25,26]. In this paper, $\sigma(a)$ is derived from user preferences, items' features, and contextual information (e.g., temporal and spatial signals). The design of this strength function plays a key role in producing logically consistent and user-aligned recommendations. Building on prior work [25,26], we ensure that the strength function satisfies desirable properties such as weak balance and weak monotonicity, which are further discussed in Section 2.3. Following the work of [25,26], we adopt a TAF that incorporates three types of relations between arguments: attack, support, and neutralize. As highlighted in [26], the inclusion of a novel dialectical neutralizing relationship is essential to fully represent the behavior of models as
an AF. This relation complements the traditional attack and support relations in a BAF by capturing items' features that exert neither a positive nor a negative influence on other arguments. While [25,26] are related to [27], two key differences distinguish them: (1) [25,26] employ a TAF, whereas [27] adopts a BAF; and (2) while [25,26] apply collaborative filtering directly to each item to influence its predicted rating, [27] incorporates collaborative filtering indirectly by leveraging the influence of similar users' ratings on the predicted ratings of the item's associated features.

This paper builds upon the **Attribute-Aware Argumentative Recommender ($A^3R)^2$** introduced in [28], and its context-aware extension, **Context-Aware Feature Attribution Through Argumentation (CA-FATA)**, introduced in [44]. The FATA we propose in [28] is closely related to a recently proposed argumentative recommender system: the **Aspect-Item framework (A-I)** introduced by [25,26]. Both A-I and FATA use argumentation to predict users' ratings toward items, treating items and features as arguments that may attack, support, or neutralize one another.

Having introduced the conceptual role of argument strength and its importance in our framework, we now introduce the two key properties that govern how arguments influence one another: weak balance and weak monotonicity. These properties, defined by prior works [25,26], guide the design of our strength function to ensure logical consistency in the face of support, attack, and neutralize relations.

### 2.3. Weak balance and weak monotonicity

The concept of weak balance, as defined by [25], generalizes the notion of “strict balance” [45]. Similarly, “weak monotonicity”, defined by [26], extends the concept of “strict monotonicity” as defined by [46].

**Weak balance:** One way to analyze the effects of one argument on another is to isolate the affecter (an argument that is affecting other arguments) and examine its impact on the affectee (an argument that is being affected). The intuition behind this approach is: Given that an affecter is the only argument affecting<fnref n="3" /> the affectee, if the affecter supports/attacks/neutralizes the affectee, then the strength of the affectee should be positive/negative/zero. This idea was formalized as the weak balance property in [25]. We follow [25,26] and define argument strengths within the interval $[-1, 1]^d$. Formally, weak balance can be defined as follows:

**Definition 1.** Given a TAF $< \mathcal{A}, \mathcal{R}^{-}, \mathcal{R}^{+}, \mathcal{R}^{0} >$, $\sigma$ satisfies the property of weak balance if, for any $x, y \in \mathcal{A}$:

* if $\mathcal{R}^{+}(x) = \{y\}, \mathcal{R}^{-}(x) = \emptyset$ and $\mathcal{R}^{0}(x) = \emptyset$, then $\sigma(x) > 0$;

* if $\mathcal{R}^{-}(x) = \{y\}, \mathcal{R}^{+}(x) = \emptyset$ and $\mathcal{R}^{0}(x) = \emptyset$, then $\sigma(x) < 0$;

* if $\mathcal{R}^{0}(x) = \{y\}, \mathcal{R}^{+}(x) = \emptyset$ and $\mathcal{R}^{-}(x) = \emptyset$, then $\sigma(x) = 0$.

**Weak monotonicity:** An alternative approach is to mute the affecter by setting its strength to 0 and observing how this impacts the strength of the affectee. The intuition is straightforward: if the affecter supports the affectee, muting the affecter will reduce the strength of the affectee; if the affecter attacks the affectee, muting the affecter will increase the strength of the affectee; and if the affecter neutralizes the affectee, muting the affecter will not alter the strength of the affectee. This intuitive concept has been formalized

as the property of weak monotonicity by [26]. Weak monotonicity is defined for two TAFs, the original $< \mathcal{A}, \mathcal{R}^{-}, \mathcal{R}^{+}, \mathcal{R}^{0} >$, and a modified version $< \mathcal{A}', \mathcal{R}^{-}', \mathcal{R}^{+}', \mathcal{R}^{0}' >$, obtained by muting one or more arguments. Formally, weak monotonicity is expressed as follows:

**Definition 2.** Given two TAFs $< \mathcal{A}, \mathcal{R}^{-}, \mathcal{R}^{+}, \mathcal{R}^{0} >$ and $< \mathcal{A}', \mathcal{R}^{-}', \mathcal{R}^{+}', \mathcal{R}^{0}' >$, $(x, y) \in \{\mathcal{R}^{-} \cup \mathcal{R}^{+} \cup \mathcal{R}^{0}\} \cap \{\mathcal{R}^{-}' \cup \mathcal{R}^{+}' \cup \mathcal{R}^{0}'\}$. $\sigma$ satisfies weak monotonicity at $(x, y)$ if, as long as $\sigma(x) = 0$ in $< \mathcal{A}', \mathcal{R}^{-}', \mathcal{R}^{+}', \mathcal{R}^{0}' >$ and $\forall z \in \{(\mathcal{R}^{-}(y) \cup \mathcal{R}^{+}(y)) \cup \mathcal{R}^{0}(y) \cap \mathcal{R}^{-}(y) \cup \mathcal{R}^{+}(y) \cup \mathcal{R}^{0'}(y)\} \setminus \{x\}$ and for $\sigma(z) = s$ in $< \mathcal{A}', \mathcal{R}^{-}', \mathcal{R}^{+}', \mathcal{R}^{0}' >$ and $\sigma(z) = s'$ in $< \mathcal{A}', \mathcal{R}^{-}', \mathcal{R}^{+}', \mathcal{R}^{0}' >$, $s = s'$. Then the following holds for $\sigma(y) = v$ in $< \mathcal{A}, \mathcal{R}^{-}, \mathcal{R}^{+}, \mathcal{R}^{0} >$ and $\sigma(y) = v'$ in $< \mathcal{A}', \mathcal{R}^{-}', \mathcal{R}^{+}', \mathcal{R}^{0}' >$ • if $x \in \mathcal{R}^{-}(y) \cap \mathcal{R}^{-'}(y)$, then $v' > v$;

* if $x \in \mathcal{R}^{+}(y) \cap \mathcal{R}^{+'}(y)$, then $v' < v$;

* if $x \in \mathcal{R}^{0}(y) \cap \mathcal{R}^{0'}(y)$, then $v' = v$.

In summary, weak balance [25] and weak monotonicity [26] formalize two key perspectives on how arguments influence one another within a TAF. These principles provide the foundation for the explanation of recommendations, as discussed in Section 4.

### 3. Our proposition

This section first outlines the problem setting, followed by a detailed description of the proposed framework, **Context-Aware Feature-Attribution Through Argumentation (CA-FATA).**

#### 3.1. Problem setting

The concept of “context” has been studied in various domains, yet it lacks a universally accepted definition. In this paper, we adopt the definition proposed in [47], which states that context is any information that can be used to characterize the situation of an entity. In the remainder of this paper, we distinguish between the following notions:

* A contextual factor refers to a specific dimension used to characterize an entity's situation. For example, a user's situation can be characterized using contextual factors such as “Companion”, “Day of the week”, and “Location”. Each of these contextual factors can take on several possible values, called contextual conditions. For example, the contextual factor “Companion” could have possible values such as “With family” or “With friends”.

* A contextual situation is defined as a combination of multiple contextual conditions. Therefore, a user's situation can be described as $cs = (Sunday, at_home, with_family)$, indicating that the situation of the user is: a Sunday, at home and with family.

We consider a scenario where we need to provide recommendations to a target user $u$ under a contextual situation $cs$. Before describing the main steps of our model, we formally define the key concepts and notations used throughout the framework.

Let $i$ denote an item, and let $t$ denote a feature type (e.g., **actor**, **genre**, **director** in movie recommendations). The feature types in this paper are analogous to the relations in knowledge graphs, which are typical directed graphs consisting of entity–relation–entity triplets [48]. As an example, the triplet (Titanic, hasDirector, JamesCameron) indicates that the movie *Titanic* is directed by *James Cameron*. Here, *hasDirector* is a relation in the knowledge graph that pertains to movies, and in this paper, it corresponds to the feature type *director*. Each item $i$ has a set of features $attr_i$, which are partitioned by feature type: $attr_i = \bigcup_{j \in T} attr_j$, where $attr_j$ denotes the set of features of type $t$ for item $i$ (e.g., $attr_i^{Actor} = \{\text{Kate Winslet, Leonardo DiCaprio}\}$ for item $i = \text{Titanic}$).

<footnote n="2">² Note that the “attribute” here has the same meaning as the “feature” used in the remainder of this paper. For consistency, in this paper, we refer to $A^3R$ as FATA (Feature Attribution Through Argumentation).</footnote>

<footnote n="3">³ Let $\langle A, \mathcal{R}^{-}, \mathcal{R}^{+}, \mathcal{R}^{0} \rangle$ be a TAF. An argument $a \in \mathcal{A}$ affects another argument $b \in \mathcal{A}$ if and only if $(a, b) \in \{\mathcal{R}^{-} \cup \mathcal{R}^{+} \cup \mathcal{R}^{0}\}$.</footnote>

<footnote n="4">⁴ The authors adopt a linear transformation. For example, in a movie recommendation, $r_{(u,i)} \in \{1, 2, 3, 4, 5\}$, then the rating can be transformed onto a rating $y \in [-1, 1]$ using $y = \frac{r_{(u,i)-1}}{2} - 1$.</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 2. Overview of CA-FATA’s steps. u, t, cf, attr, cd are vectors that represent user, feature type, contextual factor, feature and contextual condition respectively; $\pi_u^t$ is the importance of feature type t to user u; $\pi_u^{cf}$ is the importance of contextual factor cf to user u; $\otimes$ denotes the inner product operation; $\times$ denotes numerical multiplication; $\oplus$ denotes addition of vectors; + denotes scalar addition.</figcaption></figure>

## 3.2. Main steps of CA-FATA

The structure of CA-FATA, represented in Fig. 2, comprises four steps. Compared with the structure of FATA [28], CA-FATA introduces an additional “Step 1”. Below, we describe each step in detail:

**Step 1, Computing user representation:** As illustrated in Fig. 2, the user’s representation is adapted to the current contextual situation of the user. In this step, our goal is to compute the representation of users that is adapted to the target contextual situation. We begin by computing the score that a contextual factor cf receives from a user u:

$$ \rho_u^{cf} = u \otimes cf \qquad (5) $$

where $\otimes$ is the inner product.<fnref n="5" /> After calculating the score $\rho_u^{cf}$ of all contextual factors to this user, we normalize the score using Eq. (6), following [49] to obtain the importance of each contextual factor. This computed importance is conceptually similar to the relevance weights proposed in prior work [50,51]. However, unlike in these two works, where the relevance weight of the context is set empirically, our model learns the importance of contextual factors using a data-driven approach. Intuitively, $\pi_u^{cf}$ quantifies how important the contextual factor cf is to user u. Eq. (6) provides the normalized importance of contextual factors, extending the approach in [49]:

$$ \pi_u^{cf} = \frac{\exp(\text{LeakyReLU}(\rho_u^{cf}))}{\sum_{c,f \in C} \exp(\text{LeakyReLU}(\rho_u^{cf}))} \qquad (6) $$

In sequence, we compute the representation of the contextual situation cs by summing up all the vectors representing contextual conditions multiplied by $\pi_u^{cf}$.

$$ cs = \sum_{cd \in cs} \pi_u^{cf} \times cd \qquad (7) $$

The next step is to aggregate the representation of contextual situation cs with the representation of user u to obtain a specific representation of user u under the contextual situation cs. To limit model complexity,<fnref n="6" />

we sum the vectors u and cs, resulting in a context-aware representation $u_{cs}$ for each user.

$$ u_{cs} = u \oplus cs \qquad (8) $$

**Step 2, Computing feature type importance:** We quantify the score between a feature type t and a user u as proposed by [52]:

$$ \beta_{u_{cs}}^{t} = u_{cs} \otimes t \qquad (9) $$

where $u_{cs}$ is computed in the previous step, t denotes the vector that represents feature type t, $\otimes$ is the inner product operation. After calculating the score $\beta_{u_{cs}}^t$ of all feature types for this user, we normalize the score using Eq. (10) as described by [49] to obtain the importance of each feature type.

$$ \pi_{u_{cs}}^{t} = \frac{\exp(\text{LeakyReLU}(\beta_{u_{cs}}^{t}))}{\sum_{t' \in t} \exp(\text{LeakyReLU}(\beta_{u_{cs}}^{t'}))} \qquad (10) $$

**Step 3, Computing users’ ratings toward features:** To estimate users’ ratings toward features, we adopt the inner product again:

$$ p_{u_{cs}}^{attr} = u_{cs} \otimes attr \qquad (11) $$

According to Eqs. (6), (7), and (8), the representation of a user under one context differs from that under another context. As a result, the representation of user $u_{cs}$ is specific to each context, and the importance of feature type and user’s rating toward features is also context-aware.

**Step 4, Aggregating ratings toward features:** After calculating the importance of each feature type and the users’ ratings for each feature, the next step is to calculate the user u’s rating toward the item i by computing the contribution of each feature type t following the A-I framework in [25,26]:

$$ contr_i = \frac{\sum_{attr_i \in attr_i} p_{u_{cs}}^{attr}}{|attr_i^t|} \qquad (12) $$

where $attr_i^t$ denotes the features belonging to type t of item i. The final predicted rating of user u for item i under contextual situation cs is given by:

$$ \hat{r}_{(u,i)} = \sum_{t' \in t} \pi_{u_{cs}}^{t'} \times contr_t \qquad (13) $$

where $t_i$ denotes all the feature types of item i.

Fig. 3 illustrates the rating aggregation in Eqs. (12) and (13) with an example. Suppose that we want to predict the rating a user u will give

<footnote n="5">⁵ Other operations, such as an MLP, could be used. However, for simplicity and interpretability, we use the inner product in our implementation.</footnote>

<footnote n="6">⁶ Note that other aggregation methods such as concatenation are also possible but more parameters are induced. We leave this exploration for future work.</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 3. An illustration of rating aggregation in CA-FATA. On the left, each layer of the cube corresponds to a distinct feature type, with each small cube representing the predicted rating for an individual feature. On the right, each cube depicts the aggregated rating for a feature type, computed according to Eq. (12).</figcaption></figure>

to the movie *Titanic*, which is a romantic and historical movie, directed by James Cameron, starred by Kate Winslet and Leonardo DiCaprio. Suppose that we have $\pi_u^{\text{Actor}} = 0.4$; $\pi_u^{\text{Director}} = 0.3$; $\pi_u^{\text{Genre}} = 0.3$; this means that user *u* accords slightly more importance to the actors when choosing a movie. Suppose that by using our model, we have $P_u^{\text{Kate Winslet}} = 0.5$, $P_u^{\text{Leonardo DiCaprio}} = 0.6$, $P_u^{\text{James Cameron}} = 0.7$, $P_u^{\text{Romance}} = 0.8$, $P_u^{\text{Historical movie}} = 0$, $P_u^{\text{Horror}} = -0.2$. According to Eq. (12), we have $\text{contr}_{\text{Actor}} = \frac{0.5+0.6}{2} = 0.55$; $\text{contr}_{\text{Director}} = \frac{0.7}{1} = 0.7$, $\text{contr}_{\text{Genre}} = \frac{-0.2+0+0.8}{3} = 0.2$. Applying Eq. (13) yields: $\hat{r}_{(u,Titanic)} = 0.2 \times 0.3 + 0.7 \times 0.3 + 0.55 \times 0.4 = 0.49$.

Notably, Eq. (13) closely resembles the structure of Generalized Additive Models (GAMs)<fnref n="7" /> [54]. It signifies that our model belongs to the extended family of generalized additive models. Therefore, CA-FATA can be considered decomposable [55], as each computation bears explicit meanings, which can be expressed in an intelligent manner [53].

# 4. Argumentative explanations

This section details the explanation process used in CA-FATA.

## 4.1. Argumentation scaffolding

This subsection demonstrates how the computational steps presented in Section 3.2 can be mapped onto a TAF. We begin by constructing a TAF that is tailored to each user–item interaction. Subsequently, we delineate a strength function for arguments that satisfies weak balance [25] and weak monotonicity [26]. Before proceeding, we briefly recall key notations used in this section. We use $\sigma(a)$ to represent the strength function of arguments; $rec^l$ denotes an argument stating “the item can be recommended to the target user”; $R^+(a) = \{b|(b,a) \in R^+\}$, $R^-(a) = \{b|(b,a) \in R^-\}$, $R^0(a) = \{b|(b,a) \in R^0\}$ denote the set of arguments that support/attack/neutralize argument *a*, respectively.

### 4.1.1. Argumentation setting

CA-FATA begins by estimating user preferences for individual features, which are then aggregated to predict overall item ratings. Items' features are treated as arguments, and the user ratings toward those features determine the corresponding argument strengths. We set $\sigma(\text{attr}) = P_{u,cs}^{\text{attr}}$ and adopt a TAF that contains support, attack, and neutralize. Moreover, we set $\sigma(\text{rec}^l) = \hat{r}(u, i)$. When $\hat{r}(u, i) > 0$, the argument

<figure><img src="image_7.png" /><figcaption>Fig. 4. A graphical representation of rating prediction in a movie recommendation scenario, adapted from Fig. 3. Each node represents an argument, and the values on the arcs indicate the strength and polarity of features (attr1, attr2, and attr3): “+” denotes support, “-” denotes attack, and “0” denotes neutralize. The values 0.5, 0, and -0.2 represent the user's ratings of these features, corresponding to the strengths of the associated arguments.</figcaption></figure>

$rec^l$ is stronger if $\hat{r}_{(u,i)}$ is larger.<fnref n="8" /> Following [25,26], we transform the true rating $r_{(u,i)}$ into interval [-1,1], then the co-domain of $P_{u,cs}^{\text{attr}}$ is also expected to be between -1 and 1. The TAF corresponding to a user–item interaction $(u, i)$ under $cs$ can be defined as follows:

**Definition 3.** The TAF corresponding to $(u, i)$ under $cs$ is a 4-tuple: $<A, R^-, R^+, R^0>$ such that:

* $R^- = \{(\text{attr}, \text{rec}^l) | P_{u,cs}^{\text{attr}} < 0\}$;

* $R^+ = \{(\text{attr}, \text{rec}^l) | P_{u,cs}^{\text{attr}} > 0\}$;

* $R^0 = \{(\text{attr}, \text{rec}^l) | P_{u,cs}^{\text{attr}} = 0\}$.

As a running example, Fig. 4 illustrates the TAF tailored to user–item interaction (extracted from Fig. 3).

* **Attr 1:** The strength is +0.5, indicating that the user likes the actress *Kate Winslet*. This feature supports recommending the movie to the user.

* **Attr 2:** The strength is -0.2, indicating that the user dislikes *horror* movies. This feature attacks recommending the movie to the user.

* **Attr 3:** The strength is 0, indicating that the feature (*Historical movie*) has a neutral impact on recommending the movie to the user.

<footnote n="7">⁷ GAMs extend linear regression models by incorporating non-linear relationships between dependent and independent variables and are typically in the following form: $g(y) = f_1(x_1) + f_2(x_2) + f_3(x_3) + \dots + f_n(x_n)$, where *g* is the link function, *f* is the shape function, $x_i$ is the *i*-th feature [53].</footnote>

<footnote n="8">⁸ Note that if $\hat{r}_{(u,i)} < 0$ then a lower predicted score corresponds to a stronger argument against recommending item *i*.</footnote>
Notably, based on the four steps outlined in Section 3.2, the prediction score may vary depending on the context, even for the same user and item: According to Eq. (8), under a different contextual situation `cs`, the user's representation is different. Consequently, $P_{u_{cs}}^{attr}$ is also different. Thus, the TAF differs accordingly.

### 4.1.2. Theoretical analysis

We now show that by setting $\sigma(attr) = P_{u_{cs}}^{attr}$ and $\sigma(rec^l) = \hat{r}(u, i)$, the TAF corresponding to $(u, i)$ under `cs` satisfies weak balance and weak monotonicity.

**Proposition 1.** Given the TAF corresponding to $(u, i)$ under `cs`, $\sigma(attr) = P_{u_{cs}}^{attr}$ and $\sigma(rec^l) = \hat{r}(u, i)$ satisfy weak balance.

**Proof.** From Eqs. (12), (13), we note that since user ratings are normalized to the interval $[-1, 1]$, the co-domain of $\sigma$ is also $[-1, 1]$.

* Case (i): If $R^+(rec^l) = \{attr\}$, $R^-(rec^l) = \emptyset$ and $R^0(rec^l) = \emptyset$ then $\sigmaattr > 0$, therefore, $P_{u_{cs}}^{attr} > 0$, according to Eqs. (12) and (13), $\hat{r}(u, i) > 0$, indicating that $\sigma(rec^l) > 0$.

* Case (ii): If $R^-(rec^l) = \{attr\}$, $R^+(rec^l) = \emptyset$ and $R^0(rec^l) = \emptyset$ then $\sigmaattr < 0$, therefore, $P_{u_{cs}}^{attr} < 0$, according to Eqs. (12) and (13), $\hat{r}(u, i) < 0$, indicating that $\sigma(rec^l) < 0$.

* Case (iii): If $R^0(rec^l) = \{attr\}$, $R^+(rec^l) = \emptyset$ and $R^-(rec^l) = \emptyset$ then $\sigmaattr = 0$, therefore, $P_{u_{cs}}^{attr} = 0$, according to Eqs. (12) and (13), $\hat{r}(u, i) = 0$, indicating that $\sigma(rec^l) = 0$.

**Proposition 2.** Given the TAF corresponding to $(u, i)$ under `cs`, $\sigma(attr) = P_{u_{cs}}^{attr}$ and $\sigma(rec^l) = \hat{r}(u, i)$ satisfy weak monotonicity.

**Proof.** Weak monotonicity is formulated for two TAFs: from $< \mathcal{A}, R^-, R^+, R^0 >$ to $< \mathcal{A}', R^-, R^+, R^0' >$, after modifying certain arguments (e.g. muting certain features).

* Case (i): If $attr \in R^-(rec^l)$, then according to Definition 3, $P_{u_{cs}}^{attr} < 0$, when $attr$ is muted then $P_{u_{cs}}^{attr'} = 0$. According to Eqs. (12) and (13), $\hat{r}'_{(u,i)} > \hat{r}_{(u,i)}$, indicating that $\sigma(rec^l)' > \sigma(rec^l)$. (In this case, the item is more likely to be recommended.)

* Case (ii): If $attr \in R^+(rec^l)$, then according to Definition 3, $P_{u_{cs}}^{attr'} > 0$, when $attr$ is muted then $P_{u_{cs}}^{attr'} = 0$. According to Eqs. (12) and (13), $\hat{r}'_{(u,i)} < \hat{r}_{(u,i)}$, indicating that $\sigma(rec^l)' < \sigma(rec^l)$. (In this case, the item is less likely to be recommended.)

* Case (iii): If $attr \in R^0(rec^l)$, then according to Definition 3, $P_{u_{cs}}^{attr} = 0$, when $attr$ is muted then $P_{u_{cs}}^{attr'} = 0$. According to Eqs. (12) and (13), $\hat{r}'_{(u,i)} = \hat{r}_{(u,i)}$, indicating that $\sigma(rec^l)' = \sigma(rec^l)$. (In this case, the possibility that the item to be recommended remains unchanged.)

Based on Proposition 2, the following corollary holds.

**Corollary 1.** If the predicted rating of an $attr \in attr_i$ is increased: $P_{u_{cs}}^{attr'} > P_{u_{cs}}^{attr}$, then the predicted rating of item $i$ also increases: $\hat{r}'_{(u,i)} > \hat{r}_{(u,i)}$. Accordingly, if the predicted rating on $attr \in attr_i$ is decreased: $P_{u_{cs}}^{attr'} < P_{u_{cs}}^{attr}$, then the predicted rating of item $i$ also decreases: $\hat{r}'_{(u,i)} < \hat{r}_{(u,i)}$.

**Proof.** By inspecting Eqs. (12), (13), Proposition 2, it is trivial to see that Corollary 1 holds.

### 4.2. Explanation applications

We now present three application scenarios that leverage the properties of weak balance and weak monotonicity. For the toy explanation templates and the interactive explanation, we follow the spirit of [25, 26]; contrastive explanation is an original contribution.

<caption>**Table 2**</caption>

Three explanation templates for user-item interaction under contextual situation `cs` = `(cd_1, cd_2, cd_3, ... )`, `SR` denotes “strong recommendation”, `WR` denotes “weak recommendation”, `NR` “not recommended”.

<table><thead><tr><th>Scenario</th><th>Content</th><th>Example</th></tr></thead><tbody><tr><td>SR</td><td>attr<sub>1</sub> = arg max <i>P</i><sub>u<sub>si</sub></sub><sup>attr</sup><br>attr<sub>2</sub> = arg max <i>P</i><sub>u<sub>si</sub></sub><sup>attr</sup><br>attr<sub>3</sub> = arg max <i>P</i><sub>u<sub>si</sub></sub><sup>attr</sup><br>attr<sub>4</sub> = arg max <i>P</i><sub>u<sub>si</sub></sub><sup>attr</sup></td><td>When <i>cd</i>, we recommend you this item<br>because you like attr<sub>1</sub>.</td></tr><tr><td>WR</td><td>attr<sub>1</sub> = arg max <i>P</i><sub>u<sub>si</sub></sub><sup>attr</sup><br>attr<sub>2</sub> = arg min <i>P</i><sub>u<sub>si</sub></sub><sup>attr</sup><br>attr<sub>3</sub> = arg min <i>P</i><sub>u<sub>si</sub></sub><sup>attr</sup><br>attr<sub>4</sub> = arg max <i>P</i><sub>u<sub>si</sub></sub><sup>attr</sup></td><td>When <i>cd</i>, we recommend you this item<br>because you like attr<sub>1</sub> although you<br>dislike attr<sub>2</sub>.</td></tr><tr><td>NR</td><td>attr<sub>1</sub> = arg min <i>P</i><sub>u<sub>si</sub></sub><sup>attr</sup><br>attr<sub>2</sub> = arg max <i>P</i><sub>u<sub>si</sub></sub><sup>attr</sup></td><td>When <i>cd</i>, we do not recommend you<br>this item because you dislike attr<sub>1</sub>.</td></tr></tbody></table>

### 4.2.1. Template-based explanations

Building on the previous analyses, we introduce three explanation templates, summarized in Table 2. For each scenario, we select the most influential contextual condition based on Eq. (5). For the “strong recommendation” scenario, our approach proposes choosing the strongest argument that supports the recommendation for the item in question. Within the “weak recommendation” scenario,<fnref n="9" /> we select the strongest supportive argument along with the most persuasive counterargument. For situations where an item is “not recommended”, our model suggests identifying the most compelling argument that undermines the item’s recommendation. In summary, CA-FATA can explain both recommended and non-recommended items in a transparent and structured manner. To prevent information overload, no more than three features are used in each explanation scenario, as recommended in [56].

### 4.2.2. Interactive recommender systems

Another application of CA-FATA is its integration with interactive recommender systems, which take into account immediate user feedback to improve and adapt recommendations on the go [26,27,57]. This enables a dynamic and adaptive recommendation process, in contrast to traditional static systems [57].

* When a user receives a recommendation $i$ from the recommender system, it is accompanied by explanations generated by CA – FATA, such as “We recommend this item because you like feature attr”. If the user expresses disinterest in the feature attr, the system can subsequently reduce the rating of this argument. For example, the system may adjust $P_u^{attr}$ to $P_u^{attr'}$, where $P_u^{attr'} < P_u^{attr}$. According to Corollary 1 in Section 4.1.2, this ensures that $\hat{r}(u, i)' < \hat{r}(u, i)$, thereby reducing the likelihood of recommending item $i$ in the future. An illustration is shown in Fig. 5.

* Conversely, in scenarios where a user appreciates item $i$ despite that it is not recommended (as in the NR scenario in Table 2), the system may explain, “We do not recommend this item because you dislike feature attr”. If users express that they actually favor feature attr, the system can increase the rating of this feature. For instance, the system may modify $P_u^{attr}$ to $P_u^{attr'}$, where $P_u^{attr'} > P_u^{attr}$. According to Corollary 1, this adjustment ensures that $\hat{r}(u, i)' > \hat{r}(u, i)$. Consequently, item $i$ is more likely to be recommended, and other items possessing feature attr are prioritized in future recommendations. This process is illustrated in Fig. 6.

<footnote n="9">⁹ Note that if no supporters are available, the system will not recommend the item, as the predicted rating will be negative, which corresponds to the “not recommended” scenario; if no attackers are available, the predicted rating will be positive, indicating that all attributes support recommending the item, which corresponds to the “strong recommendation” scenario.</footnote>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** CA-FATA recommends an item and explains this recommendation by highlighting a feature it believes the user likes. The user, however, expresses dislike for this feature, and so the system decreases the feature's contribution to the predicted rating. As a result, the predicted score for the item drops, making it less likely to be recommended in the future.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** CA-FATA does not recommend an item and explains this behavior by pointing out a feature the user is assumed to dislike. However, the user indicates he/she actually likes the feature, and so the system increases the feature's contribution to the predicted rating. Consequently, the predicted score for the item improves, increasing the chances of it being recommended later.</figcaption></figure>

It is important to note that such adjustments to $P_u^{attr}$ will only affect the predicted score $\hat{r}(u, i)$ if the current value of $P_u^{attr}$ is not already at its upper or lower bound (i.e., within the interval $[-1, 1]$).

### 4.2.3. Contrastive explanations

As detailed in Section 2.3, at the core of weak balance and weak monotonicity is counterfactual reasoning to analyze the influence of arguments. Therefore, it would be natural to include contrastive explanations [58], which highlight not only the features of the recommended item, but also how these features differ from those of other similar items that were not recommended (see the NR scenario in Table 2). Note that contrastive explanations and counterfactual explanations are closely related but distinct forms of explanations. Contrastive explanations rely on comparative questions: “Why A rather than B?” [58], while counterfactual explanations are often expressed in hypothetical scenarios: “If X were different, then Y would change”. [59] The following is an example of a counterfactual explanation.

**Example 1.** You rated 5 for both Movie A and Movie B. Had these ratings not been provided, Movie C would have replaced Movie D in the recommendations.

Algorithm 1 illustrates the process of generating contrastive explanations using CA-FATA. We note that item similarity in the line 3 of Algorithm 1 can be computed using various approaches, e.g. we may use the Jaccard-like index: $Sim(i_c, i_{rec}) = \frac{\sum_{i \in T} |attr'_{i_c} \cap attr'_{i_{rec}}|}{\sum_{i \in T} |attr'_{i_c} \cup attr'_{i_{rec}}|}$. The

**Algorithm 1: Contrastive Explanation Generation**

**Input:** $i_{rec}$: recommended item
**I:** full item set (excluding $i_{rec}$)
$attr'_{i}$: features of item i for type $t \in T$
$\pi'_{i}$: importance of type t for user u
$P_u^{attr}$: predicted preference score of user u for feature attr
$\theta$: similarity threshold
**Output:** A contrastive explanation for recommending $i_{rec}$
1 $I_{contrast} \leftarrow \emptyset$; // Plausible contrastive items
2 **foreach** $i_c \in I \setminus \{i_{rec}\}$ **do**
3 &nbsp;&nbsp;&nbsp;&nbsp;Compute Similarity($i_c$, $i_{rec}$)
4 &nbsp;&nbsp;&nbsp;&nbsp;**if** Similarity($i_c$, $i_{rec}$) $\ge \theta$ **then**
5 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Compute predicted rating $\hat{r}_u(i_c)$ from CA-FATA
6 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Add $(i_c, \hat{r}_u(i_c))$ to $I_{contrast}$
7 Select contrastive item: $i_{con} = \text{argmax}_{(i, \hat{r}) \in I_{contrast}} \hat{r}$
8 **foreach** $t \in T$ **do**
9 &nbsp;&nbsp;&nbsp;&nbsp;Compute contribution: $S^t(i) = \sum_{attr \in attr'_{t}} \pi_u^t \cdot P_u^{attr}$ for $i \in \{i_{rec}, i_{con}\}$
10 Find most discriminative type: $t^* = \text{argmax}_t (S^t(i_{rec}) - S^t(i_{con}))$
11 **return** Explanation: “$i_{rec}$ was recommended instead of $i_{con}$ because its $t^*$ features better match your preferences.”

following provides an illustrative contrastive explanation in a movie recommendation:
**Example 2.** Assume that the system recommends *Titanic* to the user. A similar movie, *Pearl Harbor*, shares the same genres (Romance, Drama) and has historical themes. However, the model ranks *Titanic* higher because it includes preferred actors (e.g., Leonardo DiCaprio) and a preferred director (James Cameron), which align better with the user's past preferences.

In conclusion, CA-FATA is a highly adaptable model that provides explanations for both recommended and non-recommended items. Furthermore, it offers users the flexibility to define their own templates according to their specific requirements. We have also demonstrated that CA-FATA can be incorporated into interactive recommender systems to generate recommendations that align closely with users' preferences. Moreover, the model facilitates the generation of contrastive explanations by leveraging the inherent counterfactual reasoning within CA-FATA.

### 4.3. Relations with other models

The A-I framework [25,26] is a hybrid approach: it combines collaborative filtering and a content-based method. It defines user profiles using two components: collaborative filtering factors and feature type importance. The “similar users” are defined as follows: $w_{u,v} = \frac{u \cdot v}{\|u\| \cdot \|v\|}$ where u and v are vectors that represent users' preferences toward each genre (in their work, the genre of movies). Type importance is the same as $\pi_u^l$ in our work, which defines how important each feature type is to users.

We highlight the distinguishing characteristics of CA-FATA compared with the A-I framework:

* **No reliance on genre-based similar users:** In the A-I framework [25,26], the notion of “similar users” is defined using shared preferences for movie genres. While effective in certain settings, we argue this similarity metric may be too limited: User preferences over actors and directors are also highly influential in real-world recommendation tasks. CA-FATA does not require manually curated user groups; instead, it learns these preferences across diverse feature types in a data-driven way.

* **Personalized feature-type importance via collaborative filtering:** Unlike the A-I framework [25,26], which sets the same importance value for each feature type across all users, CA-FATA instead learns user-specific importance weights via MF, a widely used collaborative filtering approach [34]. This enables a finer-grained modeling of feature relevance. In Section 5, we further show that CA-FATA achieves higher accuracy and clusters users based on their learned preferences, highlighting its superior interpretability compared to the A-I framework's uniform feature-type weighting.

* **Interaction-tailored vs. user-tailored argumentation frameworks, a trade-off:** CA-FATA maps users' ratings toward items and features onto an interaction-tailored TAF,<fnref n="10" /> whereas the A-I framework [25–27] maps these ratings onto a user-tailored TAF<fnref n="11" /> (see Fig. 7). A user-tailored AF can be large, as it encompasses all features and user–item interactions for a target user. Consequently, a sub-graph (see Fig. 8) must be extracted to provide explanations for recommendations. In contrast, CA-FATA utilizes an interaction-tailored TAF where all arguments (features) are directly linked to items, allowing features to influence users' ratings without additional graph traversal or sub-graph extraction. While finding adjacent nodes in a graph is trivial, sub-graph extraction in a user-tailored AF becomes less interpretable as the graph size increases. Finally, we do not claim that the lack of graph depth is inherently a strength; rather, it is a design choice:

<table><caption>**Table 3**
Comparison of the A-I framework, FATA and CA-FATA.</caption><thead><tr><td></td><td>Features type importance</td><td>AF</td><td>Context-aware</td></tr></thead><tbody><tr><td>A-I</td><td>Set empirically</td><td>User-tailored</td><td>No</td></tr><tr><td>FATA</td><td>Data-driven</td><td>Interaction-tailored</td><td>No</td></tr><tr><td>CA-FATA</td><td>Data-driven</td><td>Interaction-tailored</td><td>Yes</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>**Fig. 7.** An example of the TAF taken from [25,26], where $f_1$ and $f_2$ are movies, other nodes denote feature (actors, directors, and genres) of movies. The user has rated $f_2$ and the target is to predict this user's rating toward $f_1$. In this case, the user has only rated one movie, however, as the number increases the user-tailored TAF grows quickly.</figcaption></figure>

- Importantly, our interaction-tailored AF design in CA-FATA trades off graph depth for directness and modularity: Each user–item interaction is modeled by a TAF in which arguments (features) directly influence the recommendation decision. This flat structure aligns more naturally with MF-based models, which typically lack the capacity to capture long-range feature dependencies. The subgraph in Fig. 8 still contains many arguments. Indeed, the authors of [25] note that refined explanations can be obtained by selecting the strongest supporter (see Figure 4 of [25]).

- During the early stages of this work, we experimented with adopting a similar recursive structure to that in the A-I framework [25,26], replacing the rating prediction in Definition 5 of [37] with MF. However, this approach led to substantially increased training time without noticeable improvements in predictive accuracy or explanation fidelity. These observations motivated our choice of a lightweight design.

* **Contextual modeling:** CA-FATA differs from the A-I framework [25,26] by leveraging user contexts. Specifically, CA-FATA computes a representation for each user tailored to specific contextual situations. This allows feature importance to be dynamically adjusted for each contextual situation. Additionally, the TAF designed for user–item interactions is also adapted to the given context. As a result, CA-FATA is capable of generating recommendations and providing explanations that are both context-aware and grounded in argumentation.

A detailed comparison of the A-I framework, FATA, and CA-FATA models can be found in Table 3. The FATA model improves upon the A-I framework by leveraging the strength of latent factor models to learn the importance of feature types in a data-driven manner and by constructing an interaction-tailored AF to minimize the effort of extracting subgraphs (see Figs. 7 and 8). CA-FATA further enhances FATA by harnessing user contexts.

Table 4 presents the comparison of CA-FATA and other related models. It can be concluded that CA-FATA considers both user–item interactions and features of items like *FM*, *NFM*, and *AFM*. For more details about these latent factor models, please refer to Section 2. Meanwhile, CA-FATA can provide model-intrinsic explanations. Compared with the A-I framework [25,26], CA-FATA can model the importance of feature types for each user, allowing a more personalized representation of user preferences.

<footnote n="10">¹⁰ By interaction-tailored, we mean that the TAF is defined for each user–item interaction.</footnote>

<footnote n="11">¹¹ By user-tailored, we mean that the TAF is defined for each user.</footnote>
<figure><img src="image_11.png" /><figcaption>Fig. 8. An example of the TAF taken from [25,26], this is a sub-graph of Fig. 7.</figcaption></figure>

<table><caption>**Table 4**
Comparison of MF, FM, NFM, AFM, A-I, NeuMF, CA-FATA, FATA, HyperCARS and GEM in terms of context-awareness, items' features and explanations. Technical details can be found in Section 2.1.</caption><thead><tr><th></th><th>Context</th><th>Items' features</th><th>Intrinsic explanations</th></tr></thead><tbody><tr><td>MF</td><td>No</td><td>No</td><td>No</td></tr><tr><td>FM</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>NFM</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>AFM</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>NeuMF</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>A-I</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>FATA</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>HyperCARS</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>GEM</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>CA-FATA</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

# 5. Experiments

In the previous section, we described how CA-FATA generates ex-
planations and demonstrated its practical applications. In this section,
we conduct experiments on two real-world datasets to address the
following research questions:

RQ1 Can CA-FATA achieve competitive performance compared to baseline methods?

RQ2 How does context influence the performance of CA-FATA?

RQ3 How does the importance of feature type affect the performance of CA-FATA?

RQ4 Can CA-FATA mitigate the cold-start problem in recommender systems?

## 5.1. Experimental setup

### 5.1.1. Datasets

We conducted experiments on the following real-world datasets:

Frappé: This dataset is collected by [60]. This dataset originates from Frappé, a context-aware app recommender. There are 96 303 logs of usage from 957 users under different contextual situations, 4 082 apps are included in the dataset. Following [13], we applied log transformation to the number of interactions. As a result, the number of interactions is scaled to 0 – 4.46. Each contextual situation includes 7 contextual conditions: “daytime”, “weekday”, “isweekend”, “home-work”, “weather”, “country”, “city” and each of them corresponds to a contextual factor. The features of each app include: “category”, “number of downloads”, “language”, “price”, “average rating (given by other users)”.

Yelp<fnref n="12" />: This dataset contains users' reviews on bars and restaurants in metropolitan areas in the USA and Canada. Consistent with previous studies by [61,62], we use the records between January 1, 2019 to December 31, 2019, which contains 904 648 observations. As contextual factors, we have derived “month”, “day of week”, “isweekend” (a Boolean indicator), “time of the day” from the timestamp of the records; “alone_or_companion” is extracted from the reviews of users,

<table><caption>**Table 5**
Basic statistics of the two datasets.</caption><thead><tr><th>Dataset</th><th>Frappé</th><th>Yelp</th></tr></thead><tbody><tr><td>#users</td><td>585</td><td>9976</td></tr><tr><td>#items</td><td>3923</td><td>52298</td></tr><tr><td>#ratings</td><td>94716</td><td>904648</td></tr><tr><td>sparsity (users × items)</td><td>94.47%</td><td>99.84%</td></tr><tr><td>#Contextual factors</td><td>7</td><td>8</td></tr><tr><td>#Feature types</td><td>5</td><td>3</td></tr><tr><td>Scale</td><td>0-4.46</td><td>1-5</td></tr></tbody></table>

“state” and “city” are provided. The features available include: “stars” (average ratings given by other users), “review count” (the number of reviews received), and “item type”.

For the two datasets, we adopted the 10-core setting, following [63], to ensure data quality. This means that only users with at least 10 interactions are kept. Table 5 summarizes the key statistics of the pre-processed datasets.

### 5.1.2. Baselines

We compare CA-FATA against several factorization-based methods, including **Vanilla MF** [6], **CAMF-C** [64], **FM** [7], **NeuMF** [10], **ECAM-NeuMF** [13], **HyperCARS** [11], Generalized embedding machine (GEM) [12]. For methods based on argumentation, we compare the A-I framework [25,26]. For further details on these methods, please refer to Section 2.

### 5.1.3. Hyperparameter settings

As both datasets contain explicit user ratings, we use squared loss to optimize the parameters of CA-FATA:

$$L = \sum_{(u,i,cs) \in \mathcal{T}} (\hat{r}_{(u,i,cs)} - r_{(u,i,cs)})^2 + \lambda \|\Theta\|_2^2 \quad (14)$$

where $\mathcal{T}$ is the training set, $\hat{r}_{(u,i,cs)}$ is the predicted rating and $r_{(u,i,cs)}$ denotes the actual rating, $\lambda$ denotes the regularization parameter to reduce overfitting, $\Theta$ denotes represents the model parameters. We implement CA-FATA using PyTorch<fnref n="13" /> and we optimize the parameters using *mini-batch Adam*. The testing platform is a Tesla P100-PCIE with 16 GB GPU memory. The hyper-parameters are tuned through a grid search: the learning rate is tuned on $[10^{-5}, 10^{-4}, 10^{-3}, 10^{-2}, 10^{-1}]$; the batch size on [128, 256, 512, 1024, 2048, 4096]; regularization parameter is tuned on range $[5 * 10^{-5}, 10^{-4}, 5 * 10^{-3}, 10^{-2}]$. The embedding size is tuned on [8, 16, 32, 64, 128, 256]. The root mean square error (RMSE) and the mean absolute error (MAE) are selected as the primary evaluation metrics. We follow the convention established by [26,65,66] by splitting the datasets into a training set and a test set with a ratio of 4 : 1.

## 5.2. Experiment results (RQ1)

Table 6 summarizes the prediction results for CA-FATA and baseline models, evaluated on the Yelp and Frappé datasets using RMSE and MAE metrics.

### 5.2.1. Rating prediction

**On the Yelp data set:** CA-FATA achieves notable accuracy improvements over most baselines. While it slightly underperforms GEM [12], CA-FATA offers additional advantages, such as context-aware explanations (discussed in Section 4) and interpretability (see Section 5.4). Compared to context-free models like MF and NeuMF, CA-FATA significantly benefits from leveraging user contexts and nuanced feature type importance, improving RMSE and MAE by achieving noticeably

<footnote n="12">¹² https://www.yelp.com/dataset</footnote>

<footnote n="13">¹³ Access to source code is provided in https://github.com/JinfengZh/ca-fata</footnote>
<table><caption>**Table 6**
Comparison between CA-FATA and baselines on RMSE and MAE, the best are in bold, and the second best are underlined.</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Yelp</th><th colspan="4">Frappé</th></tr><tr><th>RMSE ↓</th><th>MAE ↓</th><th>RMSE ↓</th><th>MAE ↓</th></tr></thead><tbody><tr><td rowspan="2">Context-free</td><td>MF</td><td>1.1809</td><td>0.9446</td><td>0.8761</td><td>0.6470</td></tr><tr><td>NeuMF</td><td>1.1710</td><td>0.8815</td><td>0.6841</td><td>0.5207</td></tr><tr><td rowspan="5">Context-aware</td><td>FM</td><td>1.1703</td><td>0.9412</td><td>0.7067</td><td>0.5796</td></tr><tr><td>CAMF-C</td><td>1.1693</td><td>0.9241</td><td>0.7283</td><td>0.5727</td></tr><tr><td>ECAM-NeuMF</td><td>1.1098</td><td>0.8636</td><td>0.5599</td><td>0.4273</td></tr><tr><td>HyperCARS</td><td>1.1904</td><td>0.9302</td><td>0.8643</td><td>0.7083</td></tr><tr><td>GEM</td><td><u>1.0539</u></td><td><u>0.8401</u></td><td><u>0.5189</u></td><td><u>0.4004</u></td></tr><tr><td>Argumentation-based</td><td>A-I</td><td>1.3978</td><td>1.1205</td><td>1.1711</td><td>0.9848</td></tr><tr><td>Our propositions</td><td>CA-FATA</td><td><u>1.1033</u></td><td><u>0.8519</u></td><td><u>0.5154</u></td><td><u>0.3910</u></td></tr></tbody></table>

lower RMSE and MAE values. Moreover, CA-FATA outperforms CAMF-C, FM, and ECAM-NeuMF among context-aware models, demonstrating its capability to model complex user preferences. Compared to the A-I framework [25,26], CA-FATA significantly reduces prediction errors. Specifically, the RMSE decreases from 1.3978 to 1.1033, and the MAE drops from 1.1205 to 0.8519 — where, for both metrics, lower values indicate better performance. This corresponds to a relative error reduction of 21.06% (RMSE) and 23.97% (MAE), computed as: relative reduction = $\frac{\text{Error}_{\text{Baseline}} - \text{Error}_{\text{CA-FATA}}}{\text{Error}_{\text{Baseline}}}$. This metric highlights the extent of error reduction and should not be confused with a normalized performance score. These gains are attributed to the following:

* Firstly, CA-FATA leverages the strengths of Vanilla MF, leading to more precise predictions.

* Secondly, CA-FATA learns the importance of different feature types to users under varying contexts, whereas the A-I framework assigns uniform importance to feature types for all users and does not take user contexts into account.

* Thirdly, the selection of the number of the *k* most similar users in the A-I framework is not straightforward — too few may overlook influential users, while too many can introduce noise from less similar users. CA-FATA avoids this issue by learning feature-type importance directly from data, eliminating the need for manual selection of similar users.

**On the dataset Frappé:** CA-FATA outperforms all baselines. Relative to MF and NeuMF, CA-FATA achieves RMSE reductions of 40.86% and 24.28%, respectively, and corresponding MAE reductions of 39.56% and 24.03%. Among context-aware models, CA-FATA demonstrates superior modeling of user preferences across diverse contexts and the ability to provide argumentative explanations, a unique advantage over FM, CAMF-C, and ECAM-NeuMF. Compared to the A-I framework, CA-FATA achieves significant RMSE and MAE reductions of 55.99% and 60.29%, respectively.

A cross-dataset comparison reveals that CA-FATA performs better on Frappé than on Yelp. We attribute this difference to the sparsity of the dataset, as Yelp is still highly sparse even after applying the 10-core setting, with a sparsity of 99.84% (see Table 5), while Frappé has a sparsity of 94.47%.

### 5.2.2. Ranking prediction

To account for variations in subjective ratings, we adopted a binary scale following [26]. Ratings ≥ 3 in Yelp and ≥ 0.9030 in Frappé (the dataset mean) are considered positive, while lower ratings are classified as negative. Table 7 presents precision, recall, and F1 scores, confirming trends from Table 6. The results in Table 7 confirm the trends observed in Table 6, showing that CA-FATA and its variants in most cases outperform the baselines.

The experimental results demonstrate that CA-FATA achieves competitive performance when compared to both context-free and context-aware baseline models. In particular, CA-FATA performs favorably

<table><caption>**Table 7**
Comparison between CA-FATA and baselines on precision, recall and f1 score.</caption><thead><tr><th rowspan="2">Model</th><th colspan="3">Yelp</th><th colspan="3">Frappé</th></tr><tr><th>Precision ↑</th><th>Recall ↑</th><th>f1 ↑</th><th>Precision ↑</th><th>Recall ↑</th><th>f1 ↑</th></tr></thead><tbody><tr><td>MF</td><td>0.8346</td><td>0.8907</td><td>0.8617</td><td>0.6889</td><td>0.7713</td><td>0.7278</td></tr><tr><td>NeuMF</td><td>0.8962</td><td>0.9522</td><td>0.9234</td><td>0.6882</td><td>0.8151</td><td>0.7463</td></tr><tr><td>FM</td><td>0.8654</td><td>0.9028</td><td>0.8837</td><td>0.6996</td><td>0.7896</td><td>0.7337</td></tr><tr><td>CAMF-C</td><td>0.8701</td><td>0.9176</td><td>0.8932</td><td>0.6913</td><td>0.7804</td><td>0.7331</td></tr><tr><td>ECAM-NeuMF</td><td><u>0.8849</u></td><td><u>0.9827</u></td><td><u>0.9313</u></td><td>0.7774</td><td>0.8124</td><td>0.7945</td></tr><tr><td>HyperCARS</td><td>0.8813</td><td>0.9787</td><td>0.9274</td><td>0.7746</td><td>0.8165</td><td>0.7949</td></tr><tr><td>GEM</td><td>0.8816</td><td>0.9800</td><td>0.9281</td><td>0.7906</td><td><u>0.8243</u></td><td><u>0.8070</u></td></tr><tr><td>A-I</td><td>0.8249</td><td>0.8828</td><td>0.8528</td><td>0.6731</td><td>0.7658</td><td>0.7164</td></tr><tr><td>CA-FATA</td><td><u>0.8888</u></td><td><u>0.9754</u></td><td><u>0.9303</u></td><td><u>0.8043</u></td><td><u>0.8336</u></td><td><u>0.8182</u></td></tr></tbody></table>

<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Cluster of users according to importance of each contextual factor.</figcaption></figure>

<table><caption>**Table 8**
Performance of the variants of CA-FATA.</caption><thead><tr><th rowspan="2">Model</th><th colspan="2">Yelp</th><th colspan="2">Frappé</th></tr><tr><th>RMSE ↓</th><th>MAE ↓</th><th>RMSE ↓</th><th>MAE ↓</th></tr></thead><tbody><tr><td>FATA</td><td>1.1434</td><td>0.9059</td><td>0.6950</td><td>0.5439</td></tr><tr><td>AVG-FATA</td><td>1.1611</td><td>0.9314</td><td>0.6970</td><td>0.5461</td></tr><tr><td>CA-FATA</td><td><u>1.1033</u></td><td><u>0.8519</u></td><td><u>0.5154</u></td><td><u>0.3910</u></td></tr><tr><td>AVG-CA-FATA</td><td><u>1.1035</u></td><td><u>0.8637</u></td><td><u>0.5254</u></td><td><u>0.4025</u></td></tr></tbody></table>

against recent context-aware methods [11,12], showing notable effectiveness on the Frappé dataset. Additionally, CA-FATA surpasses the A-I framework [25,26] and FATA [28].

### 5.3. Ablation study (RQ2 and RQ3)

To investigate the influence of feature type importance on our proposed model's performance, we introduce AVG-CA-FATA and AVG-FATA for CA-FATA and FATA, respectively (refer to rows 4 and 6 in Tables 8 and 9). The version with “AVG” means that the importance of each feature type is uniformly assigned across all users. For instance, in Frappé, where there are five feature types, each is assigned an importance value of 0.2 for all users, while in Yelp, where there are three feature types, the importance is set to 0.33. Results demonstrate that AVG-CA-FATA performs worse than CA-FATA, as does AVG-FATA when compared to FATA, thus answering the second research question, RQ2.

To evaluate the role of contextual information, we compare CA-FATA with FATA, which excludes user contexts and computes ratings using Eqs. (9), (10), and (13), identically to the method described
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** A case study on Frappé dataset that shows the clustering of users according to the contextual factor importance learned by CA-FATA. The histogram shows the average importance of each contextual factor in the cluster.</figcaption></figure>

<table><caption>**Table 9**
Performance of the variants of CA-FATA.</caption><thead><tr><th rowspan="2">Model</th><th colspan="3">Yelp</th><th colspan="3">Frappé</th></tr><tr><th>Precision ↑</th><th>Recall ↑</th><th>f1 ↑</th><th>Precision ↑</th><th>Recall ↑</th><th>f1 ↑</th></tr></thead><tbody><tr><td>FATA</td><td><u>0.8982</u></td><td>0.9361</td><td>0.9167</td><td>0.6752</td><td>0.8139</td><td>0.7381</td></tr><tr><td>AVG-FATA</td><td><u>0.8998</u></td><td>0.9277</td><td>0.9135</td><td>0.6748</td><td>0.8141</td><td>0.7380</td></tr><tr><td>CA-FATA</td><td>0.8888</td><td><u>0.9754</u></td><td><u>0.9303</u></td><td><u>0.8043</u></td><td><u>0.8336</u></td><td><u>0.8182</u></td></tr><tr><td>AVG-CA-FATA</td><td>0.8894</td><td><u>0.9748</u></td><td><u>0.9302</u></td><td><u>0.7982</u></td><td><u>0.8308</u></td><td><u>0.8142</u></td></tr></tbody></table>

in [28]. The results in Tables 8 and 9 (refer to rows 3 and 5) show that CA-FATA outperforms FATA, demonstrating that incorporating user contexts allows for a more nuanced modeling of user preferences and leads to improved prediction accuracy, thus addressing the second research question. Notably, FATA can be viewed as a special case of CA-FATA without contextual information. This conclusion is further supported by the superior performance of CAMF-C over MF and ECAM-NeuMF over NeuMF, highlighting the benefits of integrating user contexts to improve the precision of recommendations thus answering the third research question, RQ3.

## 5.4. Cold-start problem (RQ4)

In Section 4, we showed that our model can generate context-aware explanations based on argumentation. Section 5.2 demonstrated that it achieves competitive performance compared to recent context-aware models. In addition to these advantages, this section demonstrates the model's ability to address the cold-start problem [33]. To this end, we represent each user by their contextual factor importance, computed using Eq. (6). We use the Frappé dataset as an example, where a vector of seven dimensions represents each user: $(\pi_u^{daytime}, \pi_u^{weekday}, \pi_u^{weekend}, \pi_u^{homework}, \pi_u^{weather}, \pi_u^{country}, \pi_u^{city})$. Since the vectors are 7-dimensional, it is difficult to visualize the user

representations directly. We therefore first apply K-Means clustering for its simplicity and effectiveness [67], and find that four clusters best fit the dataset, as illustrated in Fig. 9. We then use UMAP [68] to visualize the clustering results. Note that other dimension reduction techniques (such as PCA and t-SNE [69], for the results of these methods, refer to https://github.com/JinfengZh/ca-fata) could also be used, but we choose UMAP because it can preserve the underlying information and the general structure of the data. The average importance of each contextual factor for users in the four groups is shown in Figs. 10(a), 10(b), 10(c), 10(d), revealing that users pay different levels of attention to contextual factors among user clusters. A similar visualization was produced for the Yelp dataset, but it is omitted here due to space constraints. The advantage of this approach is as follows: when a new user arrives, and his/her preferences are unknown, we can assign them to one of the clusters and use the average preferences of users within that cluster. Subsequently, the user can interact with the system following the workflow described in Section 4.2.2 and illustrated in Figs. 5 and 6, until the system proposes a recommendation that aligns with the user's preferences.

## 6. Conclusions

We proposed Context-Aware Feature-Attribution Through Argumentation (CA-FATA), a novel framework that integrates argumentation and matrix factorization to deliver both interpretable and accurate recommendations. From the perspective of argumentation, CA-FATA incorporates matrix factorization to compute argument strengths, thereby enhancing predictive accuracy. Conversely, from the perspective of matrix factorization, CA-FATA embeds recommendation steps into a tripartite argumentation framework, mapping computations to explicit and interpretable stages. This dual contribution strikes a balance between predictive performance and explainability. The argumentation scaffolding in CA-FATA adheres to two key properties: weak balance [25] and
weak monotonicity [26], offering a structured approach to identify
influential features and analyze their impact on predictions. The frame-
work supports three practical applications: generating explanation tem-
plates, enabling interactive explanations, and producing contrastive
explanations.

The framework also seamlessly integrates users' contextual situation, to generate context-aware recommendations and explanations. Empirical evaluations on real-world datasets demonstrate that CA-FATA and its variants not only outperform existing argumentation-based methods but also achieve competitive results when compared to state-of-the-art context-free and context-aware models. Additionally, CA-FATA addresses the cold-start problem by clustering users, enabling effective recommendations for new or infrequent users.

Our work also sheds light on the interpretability of neural networks. For example, fully connected layers — commonly used in neural networks — process input data by applying linear transformations with weight matrices. By interpreting these layers through the lens of argumentation, each neuron can be conceptualized as an argument contributing to the overall prediction. This perspective has the potential to make the inner workings of neural networks more transparent. Preliminary studies have begun exploring the integration of argumentation frameworks with neural networks [70,71], and further research into their application in recommender systems represents a promising avenue for future work.

We acknowledge the importance of evaluating the quality of ex-
planations for real-world deployment. As part of future work, we
plan to conduct user studies to assess preferences for different types
of explanations, particularly comparing context-aware vs. context-free
explanations and argumentative vs. non-argumentative explanations.

CRediT authorship contribution statement

**Jinfeng Zhong:** Writing – original draft, Visualization, Resources, Methodology, Conceptualization. **Elsa Negre:** Writing – review & editing, Supervision, Methodology.

Declaration of competing interest

The authors declare the following financial interests/personal rela-
tionships which may be considered as potential competing interests:
Jinfeng Zhong reports financial support was provided by Laboratory
for Systems Analysis and Modelling to Aid Decision-Making. Elsa Ne-
gre reports a relationship with Laboratory for Systems Analysis and
Modelling to Aid Decision-Making that includes: employment. If there
are other authors, they declare that they have no known competing
financial interests or personal relationships that could have appeared
to influence the work reported in this paper.

Acknowledgments

This work was mainly conducted while the first author was a re-
search and teaching assistant at the Paris-Dauphine University, PSL Re-
search Universities, we thank LAMSADE, UMR CNRS 7243 for the sup-
port. We also thank the CEREMADE, UMR CNRS 7534 for supporting
the first author during the revision of the article.

Data availability

Data will be made available on request.

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