Building a Real-Time Solr-Powered Recommendation Engine

Presented by Trey Grainger | CareerBuilder

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.

[tkinter]

Learnt that you can basically do item-item collaborative filtering using Solr. It also lets us do content-based recommendation (Non- ML technique) very fast. These are bound to return popular items. If your application needs any items from the tail end, Mahout's ALS is probably the best bet.

[drdonohue]

The video ends near half-way through. Where is the rest of this excellent talk?

[Specimen]

I hate when people interrupts when there is a Q&A at the end.