592 citations · 1.2k across the 25 of their papers we have counts for
10 papers · 1 filter
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…