Artwork

Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!

Seller Inventory Recommendations Enhanced by Expert Knowledge Graph with Large Language Model

19:10
 
Share
 

Manage episode 430727964 series 3474148
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/seller-inventory-recommendations-enhanced-by-expert-knowledge-graph-with-large-language-model.
This paper proposes an item recommender system for sellers that could potentially address market inefficiencies.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llm, #expert-knowledge-graph, #b2b, #ai, #inventory-recommendations, #seller-inventory, #c2c-sellers, #b2c-sellers, and more.
This story was written by: @mranthony. Learn more about this writer by checking @mranthony's about page, and for more stories, please visit hackernoon.com.
This paper proposes an item recommender system for sellers that could potentially address market inefficiencies stemming from the insufficiency of market information available to sellers. The system is designed to suggest specific items for listing that could expand a seller’s inventory, taking into account their preferences, buyer demand, and economic projections. The proposed solution integrates various methodologies to deliver optimal item recommendations.

  continue reading

316 episodes

Artwork
iconShare
 
Manage episode 430727964 series 3474148
Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/seller-inventory-recommendations-enhanced-by-expert-knowledge-graph-with-large-language-model.
This paper proposes an item recommender system for sellers that could potentially address market inefficiencies.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llm, #expert-knowledge-graph, #b2b, #ai, #inventory-recommendations, #seller-inventory, #c2c-sellers, #b2c-sellers, and more.
This story was written by: @mranthony. Learn more about this writer by checking @mranthony's about page, and for more stories, please visit hackernoon.com.
This paper proposes an item recommender system for sellers that could potentially address market inefficiencies stemming from the insufficiency of market information available to sellers. The system is designed to suggest specific items for listing that could expand a seller’s inventory, taking into account their preferences, buyer demand, and economic projections. The proposed solution integrates various methodologies to deliver optimal item recommendations.

  continue reading

316 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Quick Reference Guide

Copyright 2025 | Privacy Policy | Terms of Service | | Copyright
Listen to this show while you explore
Play