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 …

We propose Meta-Prod2vec, a novel method to compute item similarities for
recommendation that leverages existing item metadata. Such scenarios are frequently …

In many real-world deployments of machine learning systems, data arrive piecemeal. These
learning scenarios may be passive, where data arrive incrementally due to structural …

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

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 …

Point-of-Interest (POI) recommendation has become an important means to help people
discover attractive and interesting places, especially when users travel out of town …

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 …

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

With the arrival of the big data era, opportunities as well as challenges arise in both industry
and academia. As an important service in most web applications, accurate real-time …