Information Retrieval (IR) systems are crucial tools for users to access information, widely
applied in scenarios like search engines, question answering, and recommendation …

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

Retrieval-augmented generation (RAG) effectively addresses issues of static knowledge and
hallucination in large language models. Existing studies mostly focus on question scenarios …

Traditional search engines usually provide identical search results for all users, overlooking
individual preferences. To counter this limitation, personalized search has been developed …

Ambiguity in queries is a common problem in information retrieval. There are currently two
solutions: search result personalization and diversification. The former aims to tailor results …

Given a user, a personalized search model relies on her historical behaviors, such as issued
queries and their clicked documents, to generate an interest profile and personalize search …

Large language models (LLMs) and retrieval-augmented generation (RAG) techniques have
revolutionized traditional information access, enabling AI agent to search and summarize …

Existing studies have proven that pre-trained ranking models outperform pre-trained
language models when it comes to ranking tasks. To pre-train such models, researchers …