Building a Strong Baseline Recommender in PyTorch, on a Laptop
Eugene Yan
An article describes building product recommendation systems using PyTorch on a standard laptop, covering data preparation from Amazon product reviews and implementing matrix factorization without loading entire datasets into memory. The electronics dataset contained 418,749 unique products with 4,005,262 product pairs, and training took 45 minutes for 5 epochs achieving a ROC-AUC score of 0.8083. The approach enables resource-efficient training on sparse recommendation data by processing product pairs iteratively rather than storing full matrices in memory.
Why it matters
Building a baseline recsys based on data scraped off Amazon. Warning - Lots of charts! (Part 1 of 2).
