Julian McAuley

Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems

2016 · arXiv (Cornell University)

Product review websites provide an incredible lens into the wide variety of opinions and experiences of different people, and play a critical role in helping users discover products that match their personal needs and preferences. …

Self-Attentive Sequential Recommendation

2018 · arXiv (Cornell University)

Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the `context' of users' activities on the basis of actions they have performed recently. To capture such patterns, two approaches …

Controlling Bias Exposure for Fair Interpretable Predictions

2022 · arXiv (Cornell University)

Recent work on reducing bias in NLP models usually focuses on protecting or isolating information related to a sensitive attribute (like gender or race). However, when sensitive information is semantically entangled with the task information …

InforMask: Unsupervised Informative Masking for Language Model Pretraining

2022 · arXiv (Cornell University)

Masked language modeling is widely used for pretraining large language models for natural language understanding (NLU). However, random masking is suboptimal, allocating an equal masking rate for all tokens. In this paper, we propose InforMask, …

Automatic Pair Construction for Contrastive Post-training

2024

Canwen Xu, Corby Rosset, Ethan Chau, Luciano Corro, Shweti Mahajan, Julian McAuley, Jennifer Neville, Ahmed Awadallah, Nikhil Rao. Findings of the Association for Computational Linguistics: NAACL 2024. 2024.

Self-Updatable Large Language Models by Integrating Context into Model Parameters

2024 · arXiv (Cornell University)

Despite significant advancements in large language models (LLMs), the rapid and frequent integration of small-scale experiences, such as interactions with surrounding objects, remains a substantial challenge. Two critical factors in assimilating these experiences are (1) …

Transferable Sequential Recommendation via Vector Quantized Meta Learning

2024 · arXiv (Cornell University)

While sequential recommendation achieves significant progress on capturing user-item transition patterns, transferring such large-scale recommender systems remains challenging due to the disjoint user and item groups across domains. In this paper, we propose a vector …

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 …

GENNEXT: The Next Generation of IR and Recommender Systems with Language Agents, Generative Models, and Conversational AI

2025

We present GENNEXT, a workshop dedicated to exploring the integration of language agents, generative models, and conversational AI within information retrieval (IR) and recommender systems (RS). Building on the success of our recent RecSys'24 workshop, …

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 …

CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing

2026

Rui Wang, Junda Wu, Yu Xia, Tong Yu, Ruiyi Zhang, Ryan A. Rossi, Subrata Mitra, Lina Yao, Julian McAuley. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). …

Inferring Networks of Substitutable and Complementary Products

2015

To design a useful recommender system, it is important to understand how products relate to each other. For example, while a user is browsing mobile phones, it might make sense to recommend other phones, but …

Addressing Complex and Subjective Product-Related Queries with Customer Reviews

2016

Online reviews are often our first port of call when considering products and purchases online. When evaluating a potential purchase, we may have a specific query in mind, e.g. `will this baby seat fit in …

Vista

2016

Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome `standard' recommender systems challenges, such as dealing with …

Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation

2016 · arXiv (Cornell University)

Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, or place to visit, it is essential to …

Translation-based Recommendation

2017

Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems. One classical setting is predicting users' personalized sequential behavior (or 'next-item' recommendation), …

Item recommendation on monotonic behavior chains

2018

'Explicit' and 'implicit' feedback in recommender systems have been studied for many years, as two relatively isolated areas. However many real-world systems involve a spectrum of both implicit and explicit signals, ranging from clicks and …

Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation

2019

Liliang Ren, Jianmo Ni, Julian McAuley. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects

2019

Jianmo Ni, Jiacheng Li, Julian McAuley. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Time Interval Aware Self-Attention for Sequential Recommendation

2020

Sequential recommender systems seek to exploit the order of users' interactions, in order to predict their next action based on the context of what they have done recently. Traditionally, Markov Chains(MCs), and more recently Recurrent …

Intent Contrastive Learning for Sequential Recommendation

2022 · Proceedings of the ACM Web Conference 2022

Users’ interactions with items are driven by various intents (e.g., preparing for holiday gifts, shopping for fishing equipment, etc.). However, users’ underlying intents are often unobserved/latent, making it challenging to leverage such latent intents for …

Ups and Downs

2016

Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the …

Text Is All You Need: Learning Language Representations for Sequential Recommendation

2023

Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these approaches still …

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 …