During the Lucene/Solr Revolution session, “System Teardown – Solr as a Practical Recommendation Engine,” Michael Hausenblas, Chief Data Engineer at MapR Technologies, 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 is built using Mahout to do off-line analysis and Solr to provide real-time recommendations. The presentation 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 configurations, and sample web pages will be made available on GitHub for attendees to modify as they like.
This intermediate level session will take place from 3:40-4:25 on Wednesday, November 6. Click here for more details.
About the Speaker:
Michael Hausenblas (@mhausenblas) works at MapR EMEA in the role of Chief Data Engineer, where he helps people to tap the potential of big data. His background is in large-scale data integration research and development, advocacy, and standardisation. He has experience with NoSQL databases and the Hadoop ecosystem. Michael contributes to Apache Drill, a distributed system for interactive analysis of large-scale datasets.
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Lucene/Solr Revolution is presented by Lucidworks, the commercial entity for Apache Lucene/Solr open source search — the future of search technology.