This paper presents the state of art techniques in recommender systems (RS). The various
techniques are diagrammatically illustrated which on one hand helps a naïve researcher in …

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 …

Streaming session-based recommendation (SSR) is a challenging task that requires the
recommender system to do the session-based recommendation (SR) in the 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 …

Graph neural networks (GNNs) encounter significant computational challenges when
handling large-scale graphs, which severely restricts their efficacy across diverse …

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 …

Systems enabling the continuous processing of large data streams have recently attracted
the attention of the scientific community and industrial stakeholders. Data Stream Processing …