Presented by Tyler Bjornestad and Rodney Weakly.  One app, all your favorite news feeds  Customizable  Client-server  Uses Bayesian algorithm to make.

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Presentation transcript:

Presented by Tyler Bjornestad and Rodney Weakly

 One app, all your favorite news feeds  Customizable  Client-server  Uses Bayesian algorithm to make recommendations  Unique

 Choose any RSS feeds  Custom categories  User specific recommendations

 Same customizations on all devices  Easy to develop a client for different platforms  Users don’t need to download and install patches for most updates

 Naïve Bayesian Algorithm  Classification Mechanism  Conditional Probability  Requires base dataset  More data yields better classification (it gets smarter)  Used in a variety of Applications  Spam filtering is just one very popular application  Smart feed is like a spam filter in reverse

 The Smart feed needs to be trained  We initially estimated the following results  60% accuracy level after 100 rankings  80% accuracy level after 1000 rankings  What does that mean?  If the user can rate the articles Smart Feed rated a 5 at  3 out of 5 or higher after 100 reviews  4 out of 5 or higher after 1000 reviews

 Initially used entire feed  Rated common words (and, the, of, etc.)  Gave artificially high results  Did not meet success criteria  Revised feed  Removed common words from analysis  Gave more accurate results  Met or exceeded expected results

*Results are subjective, based on human preferences

 Improvements  Compare users data with others  Cross platform support  Add additional machine learning systems  Bayesian networks  Neural networks  Genetic algorithms  Commercialization?