Recommender Engines : A Peak into Predictive Analytics

Proposed talk on Predictive Analytics and Recommender Engines

Transcript

1. Recommender Engines A Peak into Predictive Analytics
2. Predictive Analytics http://giphy.com/gifs/season-6-the-simpsons-6x19-3orieSdZDhn7I6gViw
3. Predictive Analytics • Analysis of current and historical facts/data to make predictions about the future • Traditionally a field of statistics/statistical computing. • Now encompasses machine learning and data mining. Current Data Historical Data Predict Future Machine Learning / Statistics
4. Analytical Maturity
5. Analytical Maturity
6. Recommender Engines • Class of Information Filtering systems • Model user preferences • Analyse input data to predict output similar to user preferences.
7. Types of RE • Collaborative Filters • Content Based Filters • Hybrid Recommender Engines http://i.imgur.com/xlXjtOL.jpg
8. RE: Collaborative Filters • Also termed as User Based CF • Users with similar behaviours and/or attributes have similar preferences
9. RE : Content Based • Also termed as Item Based CD+F • Item attributes along with user personas are utilized to build preference models
10. RE : Hybrid • Best of both worlds • Can be modelled using User Based CF and Item Based CF in different configurations. • Less prone to issues of sparsity and cold start.
11. Quick and Dirty RE • Matrix Factorization based Recommender Engine
12. Quick and Dirty RE • Code and Results
13. Applications • Jobs you may be interested in • Who to follow • Other movies you might enjoy
14. Issues • Cold Start Problem • Sparsity Problem • Filter Bubble http://ebiquity.umbc.edu/blogger/2015/06/08/hot-stuff-at-coldstart/
15. References • R Machine Learning by Example (link) • Gartner Analytics Maturity Model (link)
16. THANK YOU Raghav Bali (@rghv_bali) http://xkcd.org/892/