Fairness in recommender systems: research landscape and future directions
Recommender systems can strongly influence which information we see online, eg, on
social media, and thus impact our beliefs, decisions, and actions. At the same time, these …
social media, and thus impact our beliefs, decisions, and actions. At the same time, these …
Trustworthy recommender systems
Recommender systems (RSs) aim at helping users to effectively retrieve items of their
interests from a large catalogue. For a quite long time, researchers and practitioners have …
interests from a large catalogue. For a quite long time, researchers and practitioners have …
Generative slate recommendation with reinforcement learning
Recent research has employed reinforcement learning (RL) algorithms to optimize long-term
user engagement in recommender systems, thereby avoiding common pitfalls such as user …
user engagement in recommender systems, thereby avoiding common pitfalls such as user …
Evolution of deep learning-based sequential recommender systems: from current trends to new perspectives
The recommender system which gets higher in practical use in applying the Apriori
algorithm in the early 2000s has revolutionized our daily life as it currently is widely used by …
algorithm in the early 2000s has revolutionized our daily life as it currently is widely used by …
Stationary algorithmic balancing for dynamic email re-ranking problem
Email platforms need to generate personalized rankings of emails that satisfy user
preferences, which may vary over time. We approach this as a recommendation problem …
preferences, which may vary over time. We approach this as a recommendation problem …
Data Scarcity in Recommendation Systems: A Survey
Z Chen, W Gan, J Wu, K Hu, H Lin - ACM Transactions on …, 2024 - dl.acm.org
The prevalence of online content has led to the widespread adoption of recommendation
systems (RSs), which serve diverse purposes such as news, advertisements, and e …
systems (RSs), which serve diverse purposes such as news, advertisements, and e …
New machine learning model based on the time factor for e-commerce recommendation systems
Nowadays, thanks to the development of e-commerce websites, businesses can capitalize
on many benefits, for example, there are many methods of approaching customers online …
on many benefits, for example, there are many methods of approaching customers online …
EvalRS 2023: Well-Rounded Recommender Systems for Real-World Deployments
EvalRS aims to bring together practitioners from industry and academia to foster a debate on
rounded evaluation of recommender systems, with a focus on real-world impact across a …
rounded evaluation of recommender systems, with a focus on real-world impact across a …
It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for Recommendation
Linear autoencoder models learn an item-to-item weight matrix via convex optimization with
L2 regularization and zero-diagonal constraints. Despite their simplicity, they have shown …
L2 regularization and zero-diagonal constraints. Despite their simplicity, they have shown …
Towards interactive explanation-based nutrition virtual coaching systems
The awareness about healthy lifestyles is increasing, opening to personalized intelligent
health coaching applications. A demand for more than mere suggestions and mechanistic …
health coaching applications. A demand for more than mere suggestions and mechanistic …