Many current applications use recommendations in order to modify the natural user
behavior, such as to increase the number of sales or the time spent on a website. This …

The task of recommender systems is classically framed as a prediction of users' preferences
and users' ratings. However, its spirit is to answer a counterfactual question:“What would the …

Machine-learning based recommender system (RS) has become an effective means to help
people automatically discover their interests. Existing models often represent the rich …

In recommender systems, some features directly affect whether an interaction would
happen, making the happened interactions not necessarily indicate user preference. For …

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

Modern recommender systems learn user representations from historical interactions, which
suffer from the problem of user feature shifts, such as an income increase. Historical …

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 …

Most data for evaluating and training recommender systems is subject to selection biases,
either through self-selection by the users or through the actions of the recommendation …

Recommender systems may be confounded by various types of confounding factors (also
called confounders) that may lead to inaccurate recommendations and sacrificed …

Context-aware recommendation algorithms focus on refining recommendations by
considering additional information, available to the system. This topic has gained a lot of …