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 …

Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

Dense retrieval has achieved impressive advances in first-stage retrieval from a large-scale
document collection, which is built on bi-encoder architecture to produce single vector …

This paper presents Structure Aware Dense Retrieval (SANTA) model, which encodes user
queries and structured data in one universal embedding space for retrieving structured data …

Contextual ranking models have delivered impressive performance improvements over
classical models in the document ranking task. However, these highly over-parameterized …

Candidate retrieval is a key part of the modern search engines whose goal is to find
candidate items that are semantically related to the query from a large item pool. The core …

Dense retrieval (DR) has extended the employment of pre-trained language models, like
BERT, for text ranking. However, recent studies have raised the robustness issue of DR …

The advent of personalized news recommendation has given rise to increasingly complex
recommender architectures. Most neural news recommenders rely on user click behavior …

With the rapid development of deep learning, training Big Models (BMs) for multiple
downstream tasks becomes a popular paradigm. Researchers have achieved various …

Product quantization (PQ) has been usually applied to dense retrieval (DR) of documents
thanks to its competitive time, memory efficiency and compatibility with other approximate …