Eugene Yan • 1/13/2020

Beating the Baseline Recommender with Graph & NLP in Pytorch

Read Original

This technical article details an approach to beat a baseline matrix factorization recommender system. It combines Natural Language Processing (word2vec) and graph algorithms (DeepWalk, random walks) to learn product embeddings from a graph of product relationships, implemented in PyTorch and NetworkX.

0 comments
#Machine Learning #Natural Language Processing #Pytorch
Beating the Baseline Recommender with Graph & NLP in Pytorch

Comments

No comments yet

Be the first to share your thoughts!

Browser Extension

Get instant access to AllDevBlogs from your browser

Top of the Week

1
React, useEffect, and Stale Closures: How to Avoid Them with Dependencies and useRef
Jivbcoop • 4 votes
2
Better react-hook-form Smart Form Components
Maarten Hus • 2 votes
3
AGI, ASI, A*I – Do we have all we need to get there?
John D. Cook • 1 votes
4
Quoting Thariq Shihipar
Simon Willison • 1 votes
5
Dew Drop – January 15, 2026 (#4583)
Alvin Ashcraft • 1 votes
6
Using Browser Apis In React Practical Guide
Jivbcoop • 1 votes