[MapR Talk] Recommendations Secrets: How to build a multi-modal recommender

Document created by aalvarez Employee on Dec 1, 2015Last modified by aalvarez Employee on Dec 7, 2015
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Overview

In an effort to help grow organic communities interested in new technologies, MapR speakers around the world provide technical talks on numerous topics. Please browse our MapR Talks Directory  to learn how to search and request a talk in seconds.

 

Abstract

This talk will cover:

 

  1. How to build a production quality recommendation engine using Solr and Mahout
  2. How to build a multi-modal recommendation from multiple behavioral inputs
  3. 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.

You can request this talk here: Speaker Request

 

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