Open-domain Question Answering (OpenQA) is an important task in Natural Language
Processing (NLP), which aims to answer a question in the form of natural language based …

The powerful large language models (LLMs) have played a pivotal role in advancing
recommender systems. Recently, in both academia and industry, there has been a surge of …

Conversational recommender systems (CRS) enable the traditional recommender systems
to explicitly acquire user preferences towards items and attributes through interactive …

This paper concerns user preference estimation in multi-round conversational recommender
systems (CRS), which interacts with users by asking questions about attributes and …

Large language models (LLMs) have significantly influenced recommender systems,
spurring interest across academia and industry in leveraging LLMs for recommendation …

Data-driven recommender systems have demonstrated great success in various Web
applications owing to the extraordinary ability of machine learning models to recognize …

Conversational recommendation system (CRS) is able to obtain fine-grained and dynamic
user preferences based on interactive dialogue. Previous CRS assumes that the user has a …

Conversational recommendation system (CRS) attracts increasing attention in various
application domains such as retail and travel. It offers an effective way to capture users' …

With the rapid progress of deep neural models and the explosion of available data
resources, dialogue systems that supports extensive topics and chit-chat conversations are …

As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …