Explore best practices for building a recommender system using Apache Mahout and Solr in this 57-minute conference talk from PhillyETE2014. Discover effective data selection techniques, learn how to leverage Apache Mahout for co-occurrence discovery, and implement an innovative search technology approach. Gain insights into deploying recommenders with Apache Solr for rapid response in production environments. Examine the machine learning workflow, recommendation engines, and data processing techniques. Delve into topics such as history matrices, co-occurrence matrices, and indicator matrices. Learn about setting up Solr indexes, offline analysis, and real-time recommendations using MapR data platform. Explore the advantages of dithering and architectural considerations. Get an overview of Apache Mahout, its integration with Scala, Spark, and h2o, and learn about the project's roadmap and how to get involved in this open-source initiative.