Chuang Li | Web IR NLP Group @ NUS

Improving Conversational Recommendation with Contextual Adaptation of External Recommenders and LLM-Based Reranking

Mon, 30 Mar 2026 00:00:00 +0000

WING 2026 Spring Outing at Clarke Quay

Fri, 23 Jan 2026 00:00:00 +0000

WING members were excited to have a spring outing at Clarke Quay on January 23, 2026. For this outing, we scheduled it at the beginning of the semester to help new members better cohere with the group.

We enjoyed both the beautiful river view and the pizza! We were happy to see the return of our alumni, especially Hengchang, Victor, and Taha, as well as our collaborator, Yunshan. We especially thank Hengchang (a WING PhD alumnus) for his generous donation of the pizza (and other items on the bill as well)!

Below is a gallery of the outing, photo credit to Yajing, Glenda, and Yisong!

Dr. Victor Li Chuang defends his Ph.D. Thesis!

Thu, 13 Mar 2025 00:00:00 +0000

We are delighted to announce that Dr. Victor Li has successfully defended his PhD thesis titled, “Towards Holistic and Practical Conversational Recommender Systems,” under the co-supervision of A/P Min-Yen Kan and Prof. Haizhou Li, on Thursday, 13 March 2025! He joins the select group of WING.NUS’ doctoral alumni! Congratulations!

Learn more about Dr. Victor Li Chuang at his Homepage. He is currently on the job market!

ChatCRS: Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems

Wed, 01 Jan 2025 00:00:00 +0000

This project is inspired by the idea of applying LLMs for zero-shot conversational recommendation. Advanced LLMs (e.g., ChatGPT) are limited in domain-specific CRS tasks for 1. generating grounded responses with recommendation- oriented knowledge, or 2. proactively leading the conversations through different dialogue goals. In this work, we first analyze those limitations through a comprehensive evaluation, showing the necessity of external knowledge and goal guidance which contribute sig- nificantly to the recommendation accuracy and language quality. In light of this finding, we propose a novel ChatCRS framework to decompose the complex CRS task into several sub-tasks through the implementation of 1. a knowledge retrieval agent using a tool-augmented approach to reason over external knowledge bases and 2. a goal-planning agent for dialogue goal prediction. Incorporating those external inputs, LLMs proactively plan interactions and generate outputs with rich information. Experiments on two multi-goal CRS datasets reveal that ChatCRS sets new state-of-the-art benchmarks, improving language quality of informativeness by 17% and proactivity by 27%, with tenfold recommendation accuracy enhancement

WING Members Enjoy the Group Outing at Yajing's Condo for a BBQ

Fri, 26 Jul 2024 00:00:00 +0000

A gallery of additional photos of the outing is included below.

UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking

Sat, 01 Jun 2024 00:00:00 +0000