With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …

Attention is an important mechanism that can be employed for a variety of deep learning
models across many different domains and tasks. This survey provides an overview of the …

Multi-task learning (MTL) has been successfully applied to many recommendation
applications. However, MTL models often suffer from performance degeneration with …

Deep learning techniques have become the method of choice for researchers working on
algorithmic aspects of recommender systems. With the strongly increased interest in …

With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …

These days, many recommender systems (RS) are utilized for solving information overload
problem in areas such as e-commerce, entertainment, and social media. Although classical …

The design of algorithms that generate personalized ranked item lists is a central topic of
research in the field of recommender systems. In the past few years, in particular …

Fashion recommendation has attracted increasing attention from both industry and
academic communities. This paper proposes a novel neural architecture for fashion …