A review on individual and multistakeholder fairness in tourism recommender systems
A Banerjee, P Banik, W Wörndl - Frontiers in big Data, 2023 - frontiersin.org
The growing use of Recommender Systems (RS) across various industries, including e-
commerce, social media, news, travel, and tourism, has prompted researchers to examine …
commerce, social media, news, travel, and tourism, has prompted researchers to examine …
[HTML][HTML] Recognizing misogynous memes: Biased models and tricky archetypes
Warning: This paper contains examples of language and images which may be offensive.
Misogyny is a form of hate against women and has been spreading exponentially through …
Misogyny is a form of hate against women and has been spreading exponentially through …
The many faces of fairness: Exploring the institutional logics of multistakeholder microlending recommendation
Recommender systems have a variety of stakeholders. Applying concepts of fairness in such
systems requires attention to stakeholders' complex and often-conflicting needs. Since …
systems requires attention to stakeholders' complex and often-conflicting needs. Since …
EqBal-RS: Mitigating popularity bias in recommender systems
Recommender systems are deployed heavily by many online platforms for better user
engagement and providing recommendations. Despite being so popular, several works …
engagement and providing recommendations. Despite being so popular, several works …
Dual disentanglement of user–item interaction for recommendation with causal embedding
C Wang, Y Ye, L Ma, D Li, L Zhuang - Information Processing & …, 2023 - Elsevier
To achieve personalized recommendations, the recommender system selects the items that
users may like by learning the collected user–item interaction data. However, the acquisition …
users may like by learning the collected user–item interaction data. However, the acquisition …
BCE4ZSR: Bi-encoder empowered by teacher cross-encoder for zero-shot cold-start news recommendation
MA Rauf, MMY Khalil, W Wang, Q Wang… - Information Processing …, 2024 - Elsevier
In the realm of news recommendations, the persistent challenge of the cold-start problem
continues to impede progress. Existing approaches rely heavily on information exchange …
continues to impede progress. Existing approaches rely heavily on information exchange …
Popularity bias in personality perspective: An analysis of how personality traits expose individuals to the unfair recommendation
Recommender systems are subject to well‐known popularity bias issues, that is, they
expose frequently rated items more in recommendation lists than less‐rated ones. Such a …
expose frequently rated items more in recommendation lists than less‐rated ones. Such a …
Fairness and sustainability in multistakeholder tourism recommender systems
A Banerjee - Proceedings of the 31st ACM Conference on User …, 2023 - dl.acm.org
In the travel industry, Tourism Recommender Systems (TRS) are gaining popularity as they
simplify trip planning for travelers by offering personalized recommendations for …
simplify trip planning for travelers by offering personalized recommendations for …
Bias assessment approaches for addressing user-centered fairness in GNN-based recommender systems
In today's technology-driven society, many decisions are made based on the results
provided by machine learning algorithms. It is widely known that the models generated by …
provided by machine learning algorithms. It is widely known that the models generated by …
Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals
Building teams and promoting collaboration are two very common business activities. An
example of these are seen in the TeamingForFunding problem, where research institutions …
example of these are seen in the TeamingForFunding problem, where research institutions …