How to do personalized search using only Postgres

There are two phases:

DATA PREPARATION PHASE:

1️⃣ Generate movie embeddings for vector search and create a BM25 index for full-text search.

2️⃣ Generate user preference embeddings based on what the user has watched and what they have liked or disliked before.

SEARCH PHASE:

1️⃣ Retrieve the top-100 movies ranked by BM25.

2️⃣ Normalize BM25 scores to a range of 0–1.

3️⃣ Perform personalized search: compare movie embeddings with the user's preference embedding.

4️⃣ Combine signals: 50% for text match (relevance) and 50% for user match (personalization).

Guide Get Here

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