Dawen Liang · PaperMetrix

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 …

From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System

2025 · arXiv (Cornell University)

Conversational recommender systems (CRS) typically require extensive domain-specific conversational datasets, yet high costs, privacy concerns, and data-collection challenges severely limit their availability. Although Large Language Models (LLMs) demonstrate strong zero-shot recommendation capabilities, practical applications often …

Modeling User Exposure in Recommendation

2016

Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feedback settings, all the items, including the ones that a user did not …

Factorization Meets the Item Embedding

2016

Matrix factorization (MF) models and their extensions are standard in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose a co-factorization model, …

Variational Autoencoders for Collaborative Filtering

2018

We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.We …

Large Language Models as Zero-Shot Conversational Recommenders

2023