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 …
Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges
Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …
developing algorithms that generate recommendations. The resulting research progress has …
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 …
Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems
M Kaminskas, D Bridge - ACM Transactions on Interactive Intelligent …, 2016 - dl.acm.org
What makes a good recommendation or good list of recommendations? Research into
recommender systems has traditionally focused on accuracy, in particular how closely the …
recommender systems has traditionally focused on accuracy, in particular how closely the …
The connection between popularity bias, calibration, and fairness in recommendation
Recently there has been a growing interest in fairness-aware recommender systems
including fairness in providing consistent performance across different users or groups of …
including fairness in providing consistent performance across different users or groups of …
Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation
Recommender system usually suffers from severe popularity bias—the collected interaction
data usually exhibits quite imbalanced or even long-tailed distribution over items. Such …
data usually exhibits quite imbalanced or even long-tailed distribution over items. Such …
How do recommender systems affect sales diversity? A cross-category investigation via randomized field experiment
D Lee, K Hosanagar - Information Systems Research, 2019 - pubsonline.informs.org
We investigate the impact of collaborative filtering recommender algorithms (eg, Amazon's
“Customers who bought this item also bought”) commonly used in e-commerce on sales …
“Customers who bought this item also bought”) commonly used in e-commerce on sales …
Deep learning models for serendipity recommendations: a survey and new perspectives
Serendipitous recommendations have emerged as a compelling approach to deliver users
with unexpected yet valuable information, contributing to heightened user satisfaction and …
with unexpected yet valuable information, contributing to heightened user satisfaction and …
Connecting user and item perspectives in popularity debiasing for collaborative recommendation
Recommender systems learn from historical users' feedback that is often non-uniformly
distributed across items. As a consequence, these systems may end up suggesting popular …
distributed across items. As a consequence, these systems may end up suggesting popular …
Investigating and counteracting popularity bias in group recommendations
Popularity bias is an undesirable phenomenon associated with recommendation algorithms
where popular items tend to be suggested over long-tail ones, even if the latter would be of …
where popular items tend to be suggested over long-tail ones, even if the latter would be of …