Recommender systems provide personalized information access to users of Internet
services from social networks to e-commerce to media and entertainment. As is appropriate …

Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

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 …

While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

As a highly data-driven application, recommender systems could be affected by data bias,
resulting in unfair results for different data groups, which could be a reason that affects the …

Recommendation algorithms are known to suffer from popularity bias; a few popular items
are recommended frequently while the majority of other items are ignored. These …

Recommender systems rely on user behavior data like ratings and clicks to build
personalization model. However, the collected data is observational rather than …

We investigate the problem of fair recommendation in the context of two-sided online
platforms, comprising customers on one side and producers on the other. Traditionally …

Recommender systems are gaining increasing and critical impacts on human and society
since a growing number of users use them for information seeking and decision making …

Recommender systems are known to suffer from the popularity bias problem: popular (ie
frequently rated) items get a lot of exposure while less popular ones are under-represented …