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This talk will cover:
- How to build a production quality recommendation engine using Solr and Mahout
- How to build a multi-modal recommendation from multiple behavioral inputs
- How search engines can be used for more than just text
To do this, we will do 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 is built using Mahout to do off-line analysis and Solr to provide real-time recommendations. The talk will also include enough theory to provide useful working intuitions for those desiring to adapt this design.
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.
KEYWORDS: Machine Learning, Recommendation, Apache Mahout, Use Case, Search
Location Availability & Request Link
North America. Please refer to theMapR Talks Directory for specific countries.
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