The integration of artificial intelligence (AI) into daily life, particularly through information
retrieval and recommender systems, has necessitated advanced user modeling and …

In conversational search, the user's real search intent for the current turn is dependent on
the previous conversation history. It is challenging to determine a good search query from …

Retrieval-Augmented Generation (RAG) offers a promising solution to address various
limitations of Large Language Models (LLMs), such as hallucination and difficulties in …

Session search involves a series of interactive queries and actions to fulfill user's complex
information need. Current strategies typically prioritize sequential modeling for deep …

Users' complex information needs usually require consecutive queries, which results in
sessions with a series of interactions. Exploiting such contextual interactions has been …

Modeling contextual information in a search session has drawn more and more attention
when understanding complex user intents. Recent methods are all data-driven, ie, they train …

Session search is a widely adopted technique in search engines that seeks to leverage the
complete interaction history of a search session to better understand the information needs …

In this work, the IRGAN model is revisited to tackle semi-supervised information retrieval (IR)
problems, considering the premature convergence of IRGAN caused by mismatching the …

Learning general-purpose user representations based on user behavioral logs is an
increasingly popular user modeling approach. It benefits from easily available, privacy …

In search sessions, a series of interactions in the context has been proven to be
advantageous in capturing users' search intents. Existing studies show that designing pre …