Recommender Systems - Using Neo4j vs traditional NMF techniques


I see a bunch of articles about how Neo4j could be used to develop recommender systems.
How do recommender systems developed using Neo4j fare in comparison to those developed using NMF (Non-negative Matrix Factorization) techniques?
From my understanding, we can write targeted Cypher queries to get recommendations. But these would be hard-coded queries that work with predefined relations. How would hidden features be discovered and used for giving better recommendations?
I'd really appreciate some discussion regarding the two approaches.


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