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 …

Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

Recent research has shown the advantages of using autoencoders based on deep neural
networks for collaborative filtering. In particular, the recently proposed Mult-VAE model …

Recommender systems are an important part of the information and e-commerce ecosystem.
They represent a powerful method for enabling users to filter through large information and …

Collaborative filtering (CF) methods produce recommendations based on usage patterns
without the need of exogenous information about items or users. CF algorithms have shown …

Recommending products to consumers means not only understanding their tastes, but also
understanding their level of experience. For example, it would be a mistake to recommend …

As the Netflix Prize competition has demonstrated, matrix factorization models are superior
to classic nearest neighbor techniques for producing product recommendations, allowing …

In the modern e-commerce, the behaviors of customers contain rich information, eg,
consumption habits, the dynamics of preferences. Recently, session-based …

Automatic multimedia learning resources recommendation has become an increasingly
relevant problem: it allows students to discover new learning resources that match their …