4 citations · 7 across the 10 of their papers we have counts for
11 papers
An Explicit Feedback Contract for Iterative ML-Agent Harnesses
Kehui Yao, Chuanbo Hu, Jiao Chen +8
Iterative ML agents develop models by editing code, running experiments, reading a validation score, and choosing what to try next. In current harnesses this loop is served by an e…
No-Human in the Loop: Agentic Evaluation at Scale for Recommendation
Tao Zhang, Kehui Yao, Luyi Ma +7
Evaluating large language models (LLMs) as judges is increasingly critical for building scalable and trustworthy evaluation pipelines. We present ScalingEval, a large-scale benchma…
MetaSynth: Multi-Agent Metadata Generation from Implicit Feedback in Black-Box Systems
Shreeranjani Srirangamsridharan, Ali Abavisani, Reza Yousefi Maragheh +4
Meta titles and descriptions strongly shape engagement in search and recommendation platforms, yet optimizing them remains challenging. Search engine ranking models are black box e…
The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
Reza Yousefi Maragheh, Yashar Deldjoo
Large language models (LLMs) are evolving from passive text generators into agentic systems that can plan, maintain state, invoke tools, and coordinate with other agents. This pers…
CARTS: Collaborative Agents for Recommendation Textual Summarization
Jiao Chen, Kehui Yao, Reza Yousefi Maragheh +6
Current recommendation systems often require some form of textual data summarization, such as generating concise and coherent titles for product carousels or other grouped item dis…
CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design
Najmeh Forouzandehmehr, Reza Yousefi Maragheh, Sriram Kollipara +4
Automated content-aware layout generation -- the task of arranging visual elements such as text, logos, and underlays on a background canvas -- remains a fundamental yet under-expl…
ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Reza Yousefi Maragheh, Pratheek Vadla, Priyank Gupta +7
Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RA…
Seller-side Outcome Fairness in Online Marketplaces
Zikun Ye, Reza Yousefi Maragheh, Lalitesh Morishetti +5
This paper aims to investigate and achieve seller-side fairness within online marketplaces, where many sellers and their items are not sufficiently exposed to customers in an e-com…
LLM-TAKE: Theme Aware Keyword Extraction Using Large Language Models
Reza Yousefi Maragheh, Chenhao Fang, Charan Chand Irugu +8
Keyword extraction is one of the core tasks in natural language processing. Classic extraction models are notorious for having a short attention span which make it hard for them to…
GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation
Ramin Giahi, Reza Yousefi Maragheh, Nima Farrokhsiar +5
Similar item recommendation is a critical task in the e-Commerce industry, which helps customers explore similar and relevant alternatives based on their interested products. Despi…
A Customer Choice Model with HALO Effect
Reza Yousefi Maragheh, Alexandra Chronopoulou, James Mario Davis
In this paper, we propose an extension to the multinomial logit (MNL) model, the Halo MNL, that takes into account the interaction effects among products in an assortment. In parti…