After the resounding success of the first article on recommender systems, Alvaro Santos is back with some further insight into creating a recommender system.
Coming soon: A follow-up Meetup in Madrid to go even further into this exciting topic. Stay tuned!
In the previous article of this series, we explained what a recommender system is, describing its main parts and providing some basic algorithms which are frequently used in these systems. We also explained how to code some functions to read JSON files and to map the data in MongoDB and ElasticSearch using Spark SQL and Spark connectors.
This second part will cover:
- Generating our Collaborative Filtering model.
- Pre-calculating product / user recommendations.
- Launching a small REST server to interact with the recommender.
- Querying the data store to retrieve content-based recommendations.
- Mixing the different types of recommendations to create a hybrid recommender.