In this video, we're going to look at several different type of recommender systems in an iPython notebook. Popularity based, item-item collaborative, then user-item collaborative. Then we'll touch on the bleeding edge in deep learning at the end. Also I freestyle. Twice lol.
Code for this video:
https://github.com/llSourcell/recommender_live
More learning resources:
http://tech.hulu.com/blog/2016/08/01/cfnade.html
https://blogs.msdn.microsoft.com/carlnol/2012/06/23/co-occurrence-approach-to-an-item-based-recommender/
https://www.mapr.com/blog/inside-look-at-components-of-recommendation-engine
https://www.ics.uci.edu/~welling/teaching/CS77Bwinter12/presentations/course_Ricci/13-Item-to-Item-Matrix-CF.pdf
https://www.analyticsvidhya.com/blog/2016/06/quick-guide-build-recommendation-engine-python/
http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/
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