Tomasz

Build a retail graph, search product descriptions, personalize results through purchase paths, and use graph embeddings to supply recommendations for a seasonal email.

• Prepare the notebook and graph database
  0:26 ↗
• Find an item, personalize it, then suggest what goes with it
  5:59 ↗
• Build the customer–article–product graph
  11:56 ↗
• Generate embeddings before creating the index
  26:57 ↗
• Wrap retrieval without coupling it to one storage layout
  35:47 ↗
• Derive a peer group from shared purchases
  45:18 ↗
• Rerank semantic matches without losing cold-start results
  57:39 ↗
• Turn co-purchase structure into reusable recommendation links
  1:09:33 ↗
• Refresh embeddings and choose their size
  1:20:07 ↗
• Use graph queries for questions similarity cannot answer
  1:23:44 ↗
• Feed two retrieved product lists into one email prompt
  1:29:30 ↗
• Change the customer, then change the season
  1:37:04 ↗