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Building a Real-Time Solr-Powered Recommendation Engine

DZone's Guide to

Building a Real-Time Solr-Powered Recommendation Engine

· Java Zone
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Microservices! They are everywhere, or at least, the term is. When should you use a microservice architecture? What factors should be considered when making that decision? Do the benefits outweigh the costs? Why is everyone so excited about them, anyway?  Brought to you in partnership with IBM.

 The video above shows us how to use Solr toperform collaborative filtering, categorical classification, and subsequent hirerarchical-based recommendations, and concept extraction/concept based recommendations.

Searching text is what Solr is known for, but did you know that many companies receive an equal or greater business impact through implementing a recommendation engine in addition to their text search capabilities? With a few tweaks, Solr (or Lucene) can also serve as a full featured recommendation engine. Machine learning libraries like Apache Mahout provide excellent behavior-based, off-line recommendation algorithms, but what if you want more control? This talk will demonstrate how to effectively utilize Solr to perform collaborative filtering (users who liked this also liked…), categorical classification and subsequent hierarchical-based recommendations, as well as related-concept extraction and concept based recommendations. Sound difficult? It’s not. Come learn step-by-step how to create a powerful real-time recommendation engine using Apache Solr and see some real-world examples of some of these strategies in action.

 

 

 

Discover how the Watson team is further developing SDKs in Java, Node.js, Python, iOS, and Android to access these services and make programming easy. Brought to you in partnership with IBM.

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Published at DZone with permission of Will Soprano. See the original article here.

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