際際滷shows by User: pferrel / http://www.slideshare.net/images/logo.gif 際際滷shows by User: pferrel / Tue, 21 Oct 2014 18:30:20 GMT 際際滷Share feed for 際際滷shows by User: pferrel Discovery /slideshow/discovery-40568180/40568180 discovery-141021183020-conversion-gate02
Optimizing discovery and engagement]]>
Optimizing discovery and engagement]]> Tue, 21 Oct 2014 18:30:20 GMT /slideshow/discovery-40568180/40568180 pferrel@slideshare.net(pferrel) Discovery pferrel Optimizing discovery and engagement <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/discovery-141021183020-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Optimizing discovery and engagement
Discovery from Pat Ferrel
]]> 1628 1 https://cdn.slidesharecdn.com/ss_thumbnails/discovery-141021183020-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The Universal Recommender /pferrel/unified-recommender-39986309 unified-recommender-141007141435-conversion-gate02
How to create a cutting edge recommender that is fast, scalable, can use almost any applicable data, and is extremely flexible for use in many different contexts. Uses Spark, Mahout, and a search engine.]]>
How to create a cutting edge recommender that is fast, scalable, can use almost any applicable data, and is extremely flexible for use in many different contexts. Uses Spark, Mahout, and a search engine.]]> Tue, 07 Oct 2014 14:14:34 GMT /pferrel/unified-recommender-39986309 pferrel@slideshare.net(pferrel) The Universal Recommender pferrel How to create a cutting edge recommender that is fast, scalable, can use almost any applicable data, and is extremely flexible for use in many different contexts. Uses Spark, Mahout, and a search engine. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/unified-recommender-141007141435-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> How to create a cutting edge recommender that is fast, scalable, can use almost any applicable data, and is extremely flexible for use in many different contexts. Uses Spark, Mahout, and a search engine.
The Universal Recommender from Pat Ferrel
]]> 34057 10 https://cdn.slidesharecdn.com/ss_thumbnails/unified-recommender-141007141435-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-pferrel-48x48.jpg?cb=1597103384 We are creating framework with engines for different useful machine learning applications. We would love to help you build smarter apps with it. actionml.com https://cdn.slidesharecdn.com/ss_thumbnails/discovery-141021183020-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/discovery-40568180/40568180 Discovery https://cdn.slidesharecdn.com/ss_thumbnails/unified-recommender-141007141435-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds pferrel/unified-recommender-39986309 The Universal Recommender