Hengchang Hu | 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!

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

Dr. Hengchang Hu defends his Ph.D. Thesis!

Tue, 27 Aug 2024 00:00:00 +0000

We are delighted to announce that Dr. Hengchang Hu has successfully defended his PhD thesis titled, “Going Beyond ID-Based Recommender Systems by Exploiting External Knowledge,” under the supervision of A/P Min-Yen Kan.

Congratulations to Dr. Hengchang Hu! His thesis explores innovative approaches to recommender systems by leveraging external knowledge beyond traditional ID-based methods. We look forward to seeing his continued contributions in the field as he embarks on his next chapter with SAP Singapore in Recommender Systems.

Learn more about Dr. Hengchang Hu Homepage on his work.

Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems

Sat, 21 Oct 2023 00:00:00 +0000

CS6101 2310 - Large Language Models

Sun, 06 Aug 2023 00:00:00 +0000

CS6101 2020 - Conversational Recommendation Systems

Wed, 06 Jan 2021 00:00:00 +0000