Digital platforms, such as social media and e-commerce websites, widely make use of
Recommender Systems to provide value to users. However, social consequences of such …

In a collaborative-filtering recommendation scenario, biases in the data will likely propagate
in the learned recommendations. In this paper we focus on the so-called mainstream bias …

Recommender systems are indispensable because they influence our day-to-day behavior
and decisions by giving us personalized suggestions. Services like Kindle, Youtube, and …

Albeit the widespread application of recommender systems (RecSys) in our daily lives,
rather limited research has been done on quantifying unfairness and biases present in such …

Biases and de-biasing in recommender systems (RS) have become a research hotspot
recently. This paper reveals an unexplored type of bias, ie, sentiment bias. Through an …

Recommender systems usually amplify the biases in the data. The model learned from
historical interactions with imbalanced item distribution will amplify the imbalance by over …

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 …

What we discover and see online, and consequently our opinions and decisions, are
becoming increasingly affected by automated machine learned predictions. Similarly, the …

Collaborative filtering (CF) is a common recommendation approach that relies on user-item
ratings. However, the natural sparsity of user-item rating data can be problematic in many …

Implicit feedback (eg., user clicks) is widely used in building recommender systems (RS).
However, the inherent notorious exposure bias significantly affects recommendation …