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 …

Recommendation models are usually trained on observational interaction data. However,
observational interaction data could result from users' conformity towards popular items …

The general aim of the recommender system is to provide personalized suggestions to
users, which is opposed to suggesting popular items. However, the normal training …

Propensity-based weighting methods have been widely studied and demonstrated
competitive performance in debiased recommendations. Nevertheless, there are still many …

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 …

In this article, we study methods for estimating causal effects in settings with panel data,
where some units are exposed to a treatment during some periods and the goal is …

Recommender systems (RS) have demonstrated great success in information seeking.
Recent years have witnessed a large number of work on inventing recommendation models …

Sequential recommendation holds the promise of understanding user preference by
capturing successive behavior correlations. Existing research focus on designing different …

Recently, recommender system (RS) based on causal inference has gained much attention
in the industrial community, as well as the states of the art performance in many prediction …

Recommender systems usually face the issue of filter bubbles: over-recommending
homogeneous items based on user features and historical interactions. Filter bubbles will …