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 …

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

In recommender systems, the collected data used for training is always subject to selection
bias, which poses a great challenge for unbiased learning. Previous studies proposed …

Recommendation systems aim to predict users' feedback on items not exposed to them yet.
Confounding bias arises due to the presence of unmeasured variables (eg, the socio …

The logged feedback for training recommender systems is usually subject to selection bias,
which could not reflect real user preference. Thus, many efforts have been made to learn the …

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 …

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 …

Recommendation datasets are prone to selection biases due to self-selection behavior of
users and item selection process of systems. This makes explicitly combating selection …