Recommender Systems (RS) have proven to be effective tools to help users overcome
information overload, and significant advances have been made in the field over the past …

Session-based Recommendation (SR) is the task of recommending the next item based on
previously recorded user interactions. In this work, we study SR in a practical streaming …

The increasing popularity of real-world recommender systems produces data continuously
and rapidly, and it becomes more realistic to study recommender systems under streaming …

With the increasing popularity of various social media and E-commerce platforms, large
volumes of user behaviour data (eg, user transaction data, rating and review data) are being …

Practical recommender systems need be periodically retrained to refresh the model with
new interaction data. To pursue high model fidelity, it is usually desirable to retrain the …

Personalized recommender systems have been widely studied and deployed to reduce
information overload and satisfy users' diverse needs. However, conventional …

Studying recommender systems under streaming scenarios has become increasingly
important because real-world applications produce data continuously and rapidly. However …

In the realm of personalized recommender systems, the challenge of adapting to evolving
user preferences and the continuous influx of new users and items is paramount …

Recommender Systems (RS) aim to provide personalized suggestions of items for users
against consumer over-choice. Although extensive research has been conducted to address …

With the continuous increase of users and items, conventional recommender systems
trained on static datasets can hardly adapt to changing environments. The high-throughput …