Recommender systems are one of the most successful applications of data mining and
machine-learning technology in practice. Academic research in the field is historically often …

Most existing collaborative filtering techniques have focused on modeling the binary relation
of users to items by extracting from user ratings. Aside from users' ratings, their affiliated …

In order to model the evolution of user preference, we should learn user/item embeddings
based on time-ordered item purchasing sequences, which is defined as Sequential …

There is an increasing attention on next-item recommendation systems to infer the dynamic
user preferences with sequential user interactions. While the semantics of an item can …

Item-to-item collaborative filtering (aka. item-based CF) has been long used for building
recommender systems in industrial settings, owing to its interpretability and efficiency in real …

“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 …

User preferences are usually dynamic in real-world recommender systems, and a user» s
historical behavior records may not be equally important when predicting his/her future …

The cross-domain recommendation technique is an effective way of alleviating the data
sparse issue in recommender systems by leveraging the knowledge from relevant domains …

A filter bubble refers to the phenomenon where Internet customization effectively isolates
individuals from diverse opinions or materials, resulting in their exposure to only a select set …

Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …