activity
20232025
most citedKnowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs

8 citations · 8 across the 6 of their papers we have counts for

collaborators
Showing cs.IRShow all

5 papers · 1 filter

cs.IR2025

Grocery to General Merchandise: A Cross-Pollination Recommender using LLMs and Real-Time Cart Context

Akshay Kekuda, Murali Mohana Krishna Dandu, Rimita Lahiri +4

Modern e-commerce platforms strive to enhance customer experience by providing timely and contextually relevant recommendations. However, recommending general merchandise to custom…

cs.IR2025

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…

cs.IR2024

Character-based Outfit Generation with Vision-augmented Style Extraction via LLMs

Najmeh Forouzandehmehr, Yijie Cao, Nikhil Thakurdesai +6

The outfit generation problem involves recommending a complete outfit to a user based on their interests. Existing approaches focus on recommending items based on anchor items or s…

cs.IR2023

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…

cs.IR20238 cited

Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs

Jiao Chen, Luyi Ma, Xiaohan Li +7

Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system performance by providing structured information about entities and their relationships, such as complement…