Generative models have been in existence for many decades. In the field of machine
learning, we come across many scenarios when directly learning a target is intractable …

Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback.
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of …

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

Recommender systems are effective tools of information filtering that are prevalent due to
increasing access to the Internet, personalization trends, and changing habits of computer …

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 …

Network embedding is an important method to learn low-dimensional representations of
vertexes in networks, aiming to capture and preserve the network structure. Almost all the …

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 …

Recommender system is a specific type of intelligent systems, which exploits historical user
ratings on items and/or auxiliary information to make recommendations on items to the …

Research about recommender systems emerges over the last decade and comprises
valuable services to increase different companies' revenue. While most existing …