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Found 7 papers, 0 papers with code
LLM-driven Constrained Copy Generation through Iterative Refinement
To the best of our knowledge, this is the first study to address multiple challenging constraints simultaneously in copy generation.
Improving Sequential Recommender Systems with Online and In-store User Behavior
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 shopping.
Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations
The LLM models the user's interactions including behaviors and item features in natural languages.
Seller-side Outcome Fairness in Online Marketplaces
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-commerce platform.
LLM-TAKE: Theme Aware Keyword Extraction Using Large Language Models
We call our LLM-based framework Theme-Aware Keyword Extraction (LLM TAKE).
GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation
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.
Knowledge-aware Complementary Product Representation Learning
Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness.
