592 citations · 1.2k across the 25 of their papers we have counts for
28 papers
Retrieval Augmented Conversational Recommendation with Reinforcement Learning
Zhenrui Yue, Honglei Zhuang, Zhen Qin +4
Large language models (LLMs) exhibit enhanced capabilities in language understanding and generation. By utilizing their embedded knowledge, LLMs are increasingly used as conversati…
LaViC: Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation
Hyunsik Jeon, Satoshi Koide, Yu Wang +2
Conversational recommender systems engage users in dialogues to refine their needs and provide more personalized suggestions. Although textual information suffices for many domains…
ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation
Yupeng Hou, Jianmo Ni, Zhankui He +5
Generative recommendation (GR) is an emerging paradigm where user actions are tokenized into discrete token patterns and autoregressively generated as predictions. However, existin…
Recommendation with Generative Models
Yashar Deldjoo, Zhankui He, Julian McAuley +8
Generative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions. In recent years, these models ha…
Multi-modal Generative Models in Recommendation System
Arnau Ramisa, Rene Vidal, Yashar Deldjoo +8
Many recommendation systems limit user inputs to text strings or behavior signals such as clicks and purchases, and system outputs to a list of products sorted by relevance. With t…
Large Language Model Driven Recommendation
Anton Korikov, Scott Sanner, Yashar Deldjoo +8
While previous chapters focused on recommendation systems (RSs) based on standardized, non-verbal user feedback such as purchases, views, and clicks -- the advent of LLMs has unloc…
Your Causal Self-Attentive Recommender Hosts a Lonely Neighborhood
Yueqi Wang, Zhankui He, Zhenrui Yue +2
In the context of sequential recommendation, a pivotal issue pertains to the comparative analysis between bi-directional/auto-encoding (AE) and uni-directional/auto-regressive (AR)…
Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation
Zhankui He, Zhouhang Xie, Harald Steck +4
Large language models (LLMs) are revolutionizing conversational recommender systems by adeptly indexing item content, understanding complex conversational contexts, and generating…
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Yashar Deldjoo, Zhankui He, Julian McAuley +7
Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample…
Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation
Se-eun Yoon, Zhankui He, Jessica Maria Echterhoff +1
Synthetic users are cost-effective proxies for real users in the evaluation of conversational recommender systems. Large language models show promise in simulating human-like behav…
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation
Junda Wu, Cheng-Chun Chang, Tong Yu +4
The long-tail recommendation is a challenging task for traditional recommender systems, due to data sparsity and data imbalance issues. The recent development of large language mod…
Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders
Yupeng Hou, Jiacheng Li, Xiangjun Fu +4
Feature engineering has long been central to recommender systems, yet effectively leveraging textual item features remains challenging. Recent advances in large language models (LL…
RecWizard: A Toolkit for Conversational Recommendation with Modular, Portable Models and Interactive User Interface
Zeyuan Zhang, Tanmay Laud, Zihang He +5
We present a new Python toolkit called RecWizard for Conversational Recommender Systems (CRS). RecWizard offers support for development of models and interactive user interface, dr…
Deciphering Compatibility Relationships with Textual Descriptions via Extraction and Explanation
Yu Wang, Zexue He, Zhankui He +2
Understanding and accurately explaining compatibility relationships between fashion items is a challenging problem in the burgeoning domain of AI-driven outfit recommendations. Pre…
Linear Recurrent Units for Sequential Recommendation
Zhenrui Yue, Yueqi Wang, Zhankui He +3
State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models. Yet such models are computationally expensive and often too slow for real-time…
Automatic Feature Fairness in Recommendation via Adversaries
Hengchang Hu, Yiming Cao, Zhankui He +2
Fairness is a widely discussed topic in recommender systems, but its practical implementation faces challenges in defining sensitive features while maintaining recommendation accur…
Large Language Models as Zero-Shot Conversational Recommenders
Zhankui He, Zhouhang Xie, Rahul Jha +6
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contribution…
Generative Flow Network for Listwise Recommendation
Shuchang Liu, Qingpeng Cai, Zhankui He +5
Personalized recommender systems fulfill the daily demands of customers and boost online businesses. The goal is to learn a policy that can generate a list of items that matches th…
Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders
Yupeng Hou, Zhankui He, Julian McAuley +1
Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models~(PLM) to e…
UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation
Jiacheng Li, Zhankui He, Jingbo Shang +1
Personalized natural language generation for explainable recommendations plays a key role in justifying why a recommendation might match a user's interests. Existing models usually…
Bundle MCR: Towards Conversational Bundle Recommendation
Zhankui He, Handong Zhao, Tong Yu +3
Bundle recommender systems recommend sets of items (e.g., pants, shirt, and shoes) to users, but they often suffer from two issues: significant interaction sparsity and a large out…
Personalized Showcases: Generating Multi-Modal Explanations for Recommendations
An Yan, Zhankui He, Jiacheng Li +2
Existing explanation models generate only text for recommendations but still struggle to produce diverse contents. In this paper, to further enrich explanations, we propose a new t…
Leashing the Inner Demons: Self-Detoxification for Language Models
Canwen Xu, Zexue He, Zhankui He +1
Language models (LMs) can reproduce (or amplify) toxic language seen during training, which poses a risk to their practical application. In this paper, we conduct extensive experim…
Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction
Zhenrui Yue, Zhankui He, Huimin Zeng +1
We investigate whether model extraction can be used to "steal" the weights of sequential recommender systems, and the potential threats posed to victims of such attacks. This type…