Fairness in recommendation: A survey
As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …
playing an important role on assisting human decision making. The satisfaction of users and …
Fairness in recommendation: Foundations, methods, and applications
As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision-making. The satisfaction of users and …
playing an important role on assisting human decision-making. The satisfaction of users and …
P-MMF: Provider max-min fairness re-ranking in recommender system
In this paper, we address the issue of recommending fairly from the aspect of providers,
which has become increasingly essential in multistakeholder recommender systems …
which has become increasingly essential in multistakeholder recommender systems …
A next basket recommendation reality check
The goal of a next basket recommendation (NBR) system is to recommend items for the next
basket for a user, based on the sequence of their prior baskets. We examine whether the …
basket for a user, based on the sequence of their prior baskets. We examine whether the …
[HTML][HTML] A survey on personalized itinerary recommendation: From optimisation to deep learning
The tourism industry is a significant contributor to the global economy, responsible for
generating nearly 10% of the world's GDP and employing around 9% of the global …
generating nearly 10% of the world's GDP and employing around 9% of the global …
[HTML][HTML] Capacity-aware fair poi recommendation combining transformer neural networks and resource allocation policy
Point of Interest (POI) recommendations have primarily focused on maximising user
satisfaction, while neglecting the needs of POIs and their operators. One such need is …
satisfaction, while neglecting the needs of POIs and their operators. One such need is …
Fair assortment planning
Many online platforms, ranging from online retail stores to social media platforms, employ
algorithms to optimize their offered assortment of items (eg, products and contents). These …
algorithms to optimize their offered assortment of items (eg, products and contents). These …
Learning with exposure constraints in recommendation systems
O Ben-Porat, R Torkan - Proceedings of the ACM Web Conference 2023, 2023 - dl.acm.org
Recommendation systems are dynamic economic systems that balance the needs of
multiple stakeholders. A recent line of work studies incentives from the content providers' …
multiple stakeholders. A recent line of work studies incentives from the content providers' …
ReCon: Reducing Congestion in Job Recommendation using Optimal Transport
Recommender systems may suffer from congestion, meaning that there is an unequal
distribution of the items in how often they are recommended. Some items may be …
distribution of the items in how often they are recommended. Some items may be …
A Taxation Perspective for Fair Re-ranking
Fair re-ranking aims to redistribute ranking slots among items more equitably to ensure
responsibility and ethics. The exploration of redistribution problems has a long history in …
responsibility and ethics. The exploration of redistribution problems has a long history in …