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 …

Collaborative filtering analyzes user preferences for items (eg, books, movies, restaurants,
academic papers) by exploiting the similarity patterns across users. In implicit feedback …

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 …

In recommendation systems, the existence of the missing-not-at-random (MNAR) problem
results in the selection bias issue, degrading the recommendation performance ultimately. A …

Real-world datasets often have missing values associated with complex generative
processes, where the cause of the missingness may not be fully observed. This is known as …

We develop a causal inference approach to recommender systems. Observational
recommendation data contains two sources of information: which items each user decided to …

Automated course recommendation can help deliver personalized and effective college
advising and degree planning. Nearest neighbor and matrix factorization based …

In crowdsourcing systems, tasks are distributed to networked people to complete such that a
company's production cost can be greatly reduced. Obviously, it is not efficient that the …

Implicit feedback is widely used in collaborative filtering methods for recommendation. It is
well known that implicit feedback contains a large number of values that are\emph {missing …

This paper is concerned with how to make efficient use of social information to improve
recommendations. Most existing social recommender systems assume people share similar …