5 research outputs found
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis
Multimodal-aware recommender systems (MRSs) exploit multimodal content (e.g.,
product images or descriptions) as items' side information to improve
recommendation accuracy. While most of such methods rely on factorization
models (e.g., MFBPR) as base architecture, it has been shown that MFBPR may be
affected by popularity bias, meaning that it inherently tends to boost the
recommendation of popular (i.e., short-head) items at the detriment of niche
(i.e., long-tail) items from the catalog. Motivated by this assumption, in this
work, we provide one of the first analyses on how multimodality in
recommendation could further amplify popularity bias. Concretely, we evaluate
the performance of four state-of-the-art MRSs algorithms (i.e., VBPR, MMGCN,
GRCN, LATTICE) on three datasets from Amazon by assessing, along with
recommendation accuracy metrics, performance measures accounting for the
diversity of recommended items and the portion of retrieved niche items. To
better investigate this aspect, we decide to study the separate influence of
each modality (i.e., visual and textual) on popularity bias in different
evaluation dimensions. Results, which demonstrate how the single modality may
augment the negative effect of popularity bias, shed light on the importance to
provide a more rigorous analysis of the performance of such models
Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting
Current AI regulations require discarding sensitive features (e.g., gender,
race, religion) in the algorithm's decision-making process to prevent unfair
outcomes. However, even without sensitive features in the training set,
algorithms can persist in discrimination. Indeed, when sensitive features are
omitted (fairness under unawareness), they could be inferred through non-linear
relations with the so called proxy features. In this work, we propose a way to
reveal the potential hidden bias of a machine learning model that can persist
even when sensitive features are discarded. This study shows that it is
possible to unveil whether the black-box predictor is still biased by
exploiting counterfactual reasoning. In detail, when the predictor provides a
negative classification outcome, our approach first builds counterfactual
examples for a discriminated user category to obtain a positive outcome. Then,
the same counterfactual samples feed an external classifier (that targets a
sensitive feature) that reveals whether the modifications to the user
characteristics needed for a positive outcome moved the individual to the
non-discriminated group. When this occurs, it could be a warning sign for
discriminatory behavior in the decision process. Furthermore, we leverage the
deviation of counterfactuals from the original sample to determine which
features are proxies of specific sensitive information. Our experiments show
that, even if the model is trained without sensitive features, it often suffers
discriminatory biases
Counterfactual Fair Opportunity: Measuring Decision Model Fairness with Counterfactual Reasoning
The increasing application of Artificial Intelligence and Machine Learning
models poses potential risks of unfair behavior and, in light of recent
regulations, has attracted the attention of the research community. Several
researchers focused on seeking new fairness definitions or developing
approaches to identify biased predictions. However, none try to exploit the
counterfactual space to this aim. In that direction, the methodology proposed
in this work aims to unveil unfair model behaviors using counterfactual
reasoning in the case of fairness under unawareness setting. A counterfactual
version of equal opportunity named counterfactual fair opportunity is defined
and two novel metrics that analyze the sensitive information of counterfactual
samples are introduced. Experimental results on three different datasets show
the efficacy of our methodologies and our metrics, disclosing the unfair
behavior of classic machine learning and debiasing models
Formalizing Multimedia Recommendation through Multimodal Deep Learning
Recommender systems (RSs) offer personalized navigation experiences on online
platforms, but recommendation remains a challenging task, particularly in
specific scenarios and domains. Multimodality can help tap into richer
information sources and construct more refined user/item profiles for
recommendations. However, existing literature lacks a shared and universal
schema for modeling and solving the recommendation problem through the lens of
multimodality. This work aims to formalize a general multimodal schema for
multimedia recommendation. It provides a comprehensive literature review of
multimodal approaches for multimedia recommendation from the last eight years,
outlines the theoretical foundations of a multimodal pipeline, and demonstrates
its rationale by applying it to selected state-of-the-art approaches. The work
also conducts a benchmarking analysis of recent algorithms for multimedia
recommendation within Elliot, a rigorous framework for evaluating recommender
systems. The main aim is to provide guidelines for designing and implementing
the next generation of multimodal approaches in multimedia recommendation