This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

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 …

In an era dominated by information overload, effective recommender systems are essential
for managing the deluge of data across digital platforms. Multi-stage cascade ranking …

Neural retrievers based on pre-trained language models (PLMs), such as dual-encoders,
have achieved promising performance on the task of open-domain question answering …

Electroencephalography (EEG) and language have been widely explored independently for
many downstream tasks (eg, sentiment analysis, relation detection, etc.). Multimodal …

Abstract Brain Signals, such as Electroencephalography (EEG), and human languages have
been widely explored independently for many downstream tasks, however, the connection …

Recently transformer-based ranking models have been shown to deliver high relevance for
document search and the relevance-efficiency tradeoff becomes important for fast query …

Multi-view clustering aims to employ semantic information from multiple perspectives to
accomplish the clustering task. However, a crucial concern in this domain is the selection of …

Abstract Information Retrieval (IR) provides access to unstructured data to satisfy user
information needs within an extensive collection. In probabilistic IR models, the probability of …

Abstract Community-based Question and Answering (CQA) platforms have a huge number
of users, resulting in numerous duplicate questions with similar intent from different users …