Recommendation Engine with Neo4j, Piece of Cake
summary
A quick Neo4j tutorial that imports an Amazon reviews CSV to create nodes for users, products, reviews and categories, then adds indexes/constraints and runs Cypher queries to compute user–user similarity using intersection/Jaccard-style measures. It materializes all pairwise similarities (many ~0.06, some ~0.4) and generates product recommendations via nearest-neighbor lookups; the live demo runs on a limited sample (hundreds, capped at 100 for the demo) and produces usable recommendations.
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chapters (8)
- 0:00Introduction
- 6:13Relational vs NoSQL
- 13:06Distributed systems CAP
- 19:12Graph databases
- 24:58Data prep for Neo4j
- 31:15User similarity recommendations
- 34:54Similarity algorithms
- 36:43Conclusion
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