Ted Dunning, Committer for Apache Mahout, Drill & Zookeeper presents on:
1. How to build a production quality recommendation engine using Mahout and Solr or Elasticsearch
2. How to build a multi-modal recommendation from multiple behavioral inputs
3. How search engines can be used for more than just text
This talk will present a detailed tear-down and walk-through of a working soup-to-nuts recommendation engine that uses observations of multiple kinds of behavior to do combined recommendation and cross recommendation. The system uses Mahout to do off-line analysis and can use Solr or Elasticsearch to provide real-time recommendations. The talk will also include enough theory to provide useful working intuitions for those desiring to adapt this design.
The entire system including a data generator, off-line analysis scripts, Solr and Elasticsearch configurations and sample web pages will be made available on github for attendees to modify as they like.
Building recommendation engines by abusing a search engine has been well-known for some time to a small sub-culture in the recommendation community, but techniques for building multi-model recommendation engines are not at all well known.