k nearest neighbours (kNN) queries are fundamental in many applications, ranging from
data mining, recommendation system and Internet of Things, to Industry 4.0 framework …

There are now over 20 commercial vector database management systems (VDBMSs), all
produced within the past five years. But embedding-based retrieval has been studied for …

Recently, there has been a pressing need to manage high-dimensional vector data in data
science and AI applications. This trend is fueled by the proliferation of unstructured data and …

The core of information retrieval (IR) is to identify relevant information from large-scale
resources and return it as a ranked list to respond to user's information need. In recent years …

Music can produce a positive effect in runners' motivation and performance. Nevertheless,
these effects vary depending on the user's location, the emotions that she/he feels at each …

Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for
document retrieval, wherein a sequence-to-sequence model is learned to directly map …

Abstract Machine learning (ML) continues to rise as an effective and affordable method of
tackling engineering problems. Unlike other disciplines, the integration of ML into structural …

As Approximate Nearest Neighbor Search (ANNS)-based dense retrieval becomes
ubiquitous for search and recommendation scenarios, efficiently answering filtered ANNS …

The approximate nearest neighbor (ANN) search in high-dimensional spaces is a
fundamental but computationally very expensive problem. Many methods have been …

Traditional online continual learning (OCL) research has primarily focused on mitigating
catastrophic forgetting with fixed and limited storage allocation throughout an agent's …