Zhankui He

Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation

2025

Large Language Models (LLMs) are revolutionizing conversational recommender systems (CRS) by effectively indexing item content, understanding complex conversational contexts, and generating relevant item titles. However, the autoregressive nature of LLMs, which outputs item titles as …

Adversarial Personalized Ranking for Recommendation

2018

Item recommendation is a personalized ranking task. To this end, many recommender systems optimize models with pairwise ranking objectives, such as the Bayesian Personalized Ranking (BPR). Using matrix Factorization (MF) - the most widely used …

NAIS: Neural Attentive Item Similarity Model for Recommendation

2018 · IEEE Transactions on Knowledge and Data Engineering

Item-to-item collaborative filtering (aka.item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, …

Large Language Models as Zero-Shot Conversational Recommenders

2023

In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions. (1) Data: To gain insights into model behavior in "in-the-wild" conversational …