Embedding-based retrieval methods construct vector indices to search for document
representations that are most similar to the query representations. They are widely used in …

Passage ranking involves two stages: passage retrieval and passage re-ranking, which are
important and challenging topics for both academics and industries in the area of …

To better support information retrieval tasks such as web search and open-domain question
answering, growing effort is made to develop retrieval-oriented language models, eg …

Large language models (LLMs) have demonstrated remarkable capabilities across various
research domains, including the field of Information Retrieval (IR). However, the responses …

Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval
stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their …

Passage retrieval is a fundamental task in many information systems, such as web search
and question answering, where both efficiency and effectiveness are critical concerns. In …

Graphs are widely used to represent the relations among entities. When one owns the
complete data, an entire graph can be easily built, therefore performing analysis on the …

Information Retrieval (IR), the process of finding information to satisfy user's information
needs, plays an essential role in modern people's lives. Recently, large language models …

Despite recommender systems play a key role in network content platforms, mining the
user's interests is still a significant challenge. Existing works predict the user interest by …

Modeling user interest accurately is crucial to recommendation systems. Existing works
capture user interest from historical behaviors. Due to the sparsity and noise in user …