Publications (17)
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…
Improving Sequential Recommender Systems with Online and In-store User Behavior
Luyi Ma, Aashika Padmanabhan, Anjana Ganesh +9
Online e-commerce platforms have been extending in-store shopping, which allows users to keep the canonical online browsing and checkout experience while exploring in-store shoppin…
20 Second Parity Lifetime in an InAs--Pb Tetron Device
Morteza Aghaee, Zulfi Alam, Mariusz Andrzejczuk +162
A central promise of topological quantum computing is that increasing the excitation gap improves device performance significantly. Here, we experimentally validate this principle…
Latent Customer Segmentation and Value-Based Recommendation Leveraging a Two-Stage Model with Missing Labels
Keerthi Gopalakrishnan, Tianning Dong, Chia-Yen Ho +5
The success of businesses depends on their ability to convert consumers into loyal customers. A customer's value proposition is a primary determinant in this process, requiring a b…
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…
Knowledge-aware Complementary Product Representation Learning
Da Xu, Chuanwei Ruan, Jason Cho +3
Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, w…
To See or To Read: User Behavior Reasoning in Multimodal LLMs
Tianning Dong, Luyi Ma, Varun Vasudevan +3
Multimodal Large Language Models (MLLMs) are reshaping how modern agentic systems reason over sequential user-behavior data. However, whether textual or image representations of us…
Distinct Lifetimes for and Loop Measurements in a Majorana Tetron Device
Morteza Aghaee, Zulfi Alam, Rikke Andersen +164
We present a hardware realization and measurements of a tetron qubit device in a superconductor-semiconductor heterostructure. The device architecture contains two parallel superco…
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…
Look at Me When I Talk to You: A Video Dataset to Enable Voice Assistants to Recognize Errors
Andrea Cuadra, Hansol Lee, Jason Cho +1
People interacting with voice assistants are often frustrated by voice assistants' frequent errors and inability to respond to backchannel cues. We introduce an open-source video d…
Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations
Luyi Ma, Xiaohan Li, Zezhong Fan +7
Integrating diverse data modalities is crucial for enhancing the performance of personalized recommendation systems. Traditional models, which often rely on singular data sources,…
LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks
Luyi Ma, Wanjia Sherry Zhang, Zezhong Fan +10
On online advertising platforms, newly introduced promotional ads face the cold-start problem, as they lack sufficient user feedback for model training. In this work, we propose LL…
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…
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…
GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization
Luyi Ma, Wanjia Zhang, Kai Zhao +15
Generative models have recently demonstrated strong potential in multi-behavior recommendation systems, leveraging the expressive power of transformers and tokenization to generate…
LLM-driven Constrained Copy Generation through Iterative Refinement
Varun Vasudevan, Faezeh Akhavizadegan, Abhinav Prakash +5
Crafting a marketing message (copy), or copywriting is a challenging generation task, as the copy must adhere to various constraints. Copy creation is inherently iterative for huma…
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…