Causal intervention for leveraging popularity bias in recommendation
Recommender system usually faces popularity bias issues: from the data perspective, items
exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method …
exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method …
Elliot: A comprehensive and rigorous framework for reproducible recommender systems evaluation
Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …
problem and provide accurate and tailored recommendations. However, the impressive …
Co-training disentangled domain adaptation network for leveraging popularity bias in recommenders
Recommender system usually faces popularity bias. From the popularity distribution shift
perspective, the normal paradigm trained on exposed items (most are hot items) identifies …
perspective, the normal paradigm trained on exposed items (most are hot items) identifies …
A flexible framework for evaluating user and item fairness in recommender systems
One common characteristic of research works focused on fairness evaluation (in machine
learning) is that they call for some form of parity (equality) either in treatment—meaning they …
learning) is that they call for some form of parity (equality) either in treatment—meaning they …
A re-visit of the popularity baseline in recommender systems
Popularity is often included in experimental evaluation to provide areference performance
for a recommendation task. To understand how popularity baseline is defined and …
for a recommendation task. To understand how popularity baseline is defined and …
Adversarial machine learning in recommender systems (aml-recsys)
Recommender systems (RS) are an integral part of many online services aiming to provide
an enhanced user-oriented experience. Machine learning (ML) models are nowadays …
an enhanced user-oriented experience. Machine learning (ML) models are nowadays …
Recommender systems fairness evaluation via generalized cross entropy
Fairness in recommender systems has been considered with respect to sensitive attributes
of users (eg, gender, race) or items (eg, revenue in a multistakeholder setting). Regardless …
of users (eg, gender, race) or items (eg, revenue in a multistakeholder setting). Regardless …
[PDF][PDF] Are We Forgetting Something? Correctly Evaluate a Recommender System With an Optimal Training Window.
Recommender systems are deployed in dynamic environments with constantly changing
interests and availability of items, articles and products. The hyperparameter optimisation of …
interests and availability of items, articles and products. The hyperparameter optimisation of …
On the discriminative power of hyper-parameters in cross-validation and how to choose them
Hyper-parameters tuning is a crucial task to make a model perform at its best. However,
despite the well-established methodologies, some aspects of the tuning remain unexplored …
despite the well-established methodologies, some aspects of the tuning remain unexplored …
Irec: An interactive recommendation framework
Nowadays, most e-commerce and entertainment services have adopted interactive
Recommender Systems (RS) to guide the entire journey of users into the system. This task …
Recommender Systems (RS) to guide the entire journey of users into the system. This task …