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 …
KGTORe: tailored recommendations through knowledge-aware GNN models
Knowledge graphs (KG) have been proven to be a powerful source of side information to
enhance the performance of recommendation algorithms. Their graph-based structure …
enhance the performance of recommendation algorithms. Their graph-based structure …
Kgflex: Efficient recommendation with sparse feature factorization and knowledge graphs
Collaborative filtering models have undoubtedly dominated the scene of recommender
systems in recent years. However, due to the little use of content information, they narrowly …
systems in recent years. However, due to the little use of content information, they narrowly …
Fourth knowledge-aware and conversational recommender systems workshop (kars)
In the last few years, a renewed interest of the research community in conversational
recommender systems (CRSs) has been emerging. This is likely due to the massive …
recommender systems (CRSs) has been emerging. This is likely due to the massive …
Usst: A two-phase privacy-preserving framework for personalized recommendation with semi-distributed training
Personalized recommendations are becoming indispensable for assisting online users in
discovering items of interest. However, existing recommendation algorithms rely heavily on …
discovering items of interest. However, existing recommendation algorithms rely heavily on …
Defending Federated Recommender Systems against Untargeted Attacks: A Contribution-Aware Robust Aggregation Scheme
Federated recommender systems (FedRSs) effectively tackle the tradeoff between
recommendation accuracy and privacy preservation. However, recent studies have revealed …
recommendation accuracy and privacy preservation. However, recent studies have revealed …
[HTML][HTML] PyCPFair: A framework for consumer and producer fairness in recommender systems
Fairness is a critical problem not only in scientific research but also in many real-life
applications. Recent work in recommender systems mainly focuses on fairness in …
applications. Recent work in recommender systems mainly focuses on fairness in …
Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)
Recommender systems, though widely used, often struggle to engage users effectively.
While deep learning methods have enhanced connections between users and items, they …
While deep learning methods have enhanced connections between users and items, they …
Pharmaceutical research and development: A key informant assessment of whether an" open-science" model could improve clinical research in terms of quality and …
TDN King - 2013 - search.proquest.com
The average cost to develop each new pharmaceutical drug is approximately $1 billion or
more and takes 12-15 years from laboratory concept to an approved drug on the shelf at the …
more and takes 12-15 years from laboratory concept to an approved drug on the shelf at the …
Multi-dimensional federated learning in recommender systems
S Liu - 2022 - search.proquest.com
A wide range of web services like e-commerce, job-searching, and target advertising heavily
rely on recommender systems that find products of interest to fulfill users' diverse and …
rely on recommender systems that find products of interest to fulfill users' diverse and …