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 …

Recommender systems should answer the intervention question" if recommending an item
to a user, what would the feedback be", calling for estimating the causal effect of a …

How to effectively mitigate the bias of feedback in recommender systems is an important
research topic. In this paper, we first describe the generation process of the biased and …

Recommender systems learn from historical user-item interactions to identify preferred items
for target users. These observed interactions are usually unbalanced following a long-tailed …

Recommender systems have been applied successfully in a number of different domains,
such as, entertainment, commerce, and employment. Their success lies in their ability to …

Deep candidate generation (DCG) that narrows down the collection of relevant items from
billions to hundreds via representation learning has become prevalent in industrial …

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 …

Recommender systems are seen as an effective tool to address information overload, but it
is widely known that the presence of various biases makes direct training on large-scale …

Current recommender systems have achieved great successes in online services, such as E-
commerce and social media. However, they still suffer from the performance degradation in …

The ubiquity of implicit feedback makes them the default choice to build online
recommender systems. While the large volume of implicit feedback alleviates the data …