Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

Reading or viewing recommendations are a common feature on modern media sites. What
is shown to consumers as recommendations is nowadays often automatically determined by …

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 …

Deep learning has profoundly impacted many areas of machine learning. However, it took a
while for its impact to be felt in the field of recommender systems. In this article, we outline …

How to accurately predict unknown quality-of-service (QoS) data based on observed ones is
a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) …

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

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 …

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 …

In recommender systems, usually the ratings of a user to most items are missing and a
critical problem is that the missing ratings are often missing not at random (MNAR) in reality …

Recommender systems (RSs) commonly adopt a user-item rating matrix to describe users'
preferences on items. With users and items exploding, such a matrix is usually high …