Memory-Based Recommender Systems : A Comparative Study Aaron John Mani Srinivas Ramani CSCI 572 PROJECT RECOMPARATOR.

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Memory-Based Recommender Systems : A Comparative Study Aaron John Mani Srinivas Ramani CSCI 572 PROJECT RECOMPARATOR

Problem definition This project is a comparative study of two movie recommendation systems based on collaborative filtering. User-User Rating vs Item-Item Rating Slope-One algorithm - Prediction engine. Pearson’s Correlation – Calculate similarity of users/items Also compare against Netflix/IMDB recommendations The aim of the experiment is to study the accuracy of the two algorithms when applied on the same dataset under similar conditions 2

S/W, Language used 3 S/W, LanguagePurpose NetFlixDataset JavaMain programming language for similarity ranking and prediction engine HTML/CSS/JavaScriptFrond End/ GUI PerlScraping/RegEx MySQLBack End Database Shell/RubyScripts for importing/exporting dataset

Plan of Action 4 SNo #TaskResponsibilityCheckPoint (Week Ending) 1.Scripts to import/export DatasetAJ25 th March 2.Similarity RankingSR1 st April 3.Prediction EngineAJ1 st April 4.UI DesignAJ25 th March 5.Results FormSR8 th April 6.Graphs/Metrics Data PlotAJ, SR15 th April 7.NetFlix ScrapingSR8 th April 8.Unit/Incremental Testing, QCAJ, SR22 nd April

Sample Screenshot [Recommendation Page] 5

Sample graphs showing the data you will collect and how it will be presented. Mean Absolute Error (MAE) – Sample error difference of approx.100 Users. This is a standard metric which is essentially used to measure how much deviation a particular algorithm will show against original ratings (blanked out for the test). 6

Sample graphs showing the data you will collect and how it will be presented. New User Problem – Conduct a survey among 10 human testers to gauge how relevant the top n predictions are compared to the selected movie and rate their accuracy on a scale of These users will be new user rows in the User-Item Matrix with a single rating. The mean of this test data will provide a human perspective on the Precision of machine-generated suggestions for new users introduced into the system. 7 Human UsersUser-UserItem-Item User 1810 User 264 User 377 User 485 User 554 User 677 User 714 User 843 User 968 User 10810

Sample graphs showing the data you will collect and how it will be presented. Average Precision Analysis – Create similar test conditions as before. Each human tester logs the relevancy of the top-n predictions of each algorithm to the selected movie. The average across each category of algorithms should provide some insight into the # of relevant predictions generated as compared to the total predictions generated. 8 Human UsersP User-User % P Item-Item % User User User 30.7 User User User 60.7 User User User User