1. COLLABORATIVE FILTERINGUSING KNN ALGORITHMeyda HATPOLU 11.06.2013

2. Recommender Systems Software tools and techniques providing suggestions for itemsto be of use to a user Recommender systems analyze patterns of user interest initems or products to provide personalized recommendationsof items that will suit a users tasteItem - What the system recommends to the user(CD, news, books, movies...)User preferences - ratings for productsUser actions - user browsing history 3. RS Techniques Collaborative-Filtering system recommends to the active user the items thatother users with similar tastes liked in the past Content-based system recommend items that are similar to the ones thatthe user liked in the past Hybrid-Collaborative Filtering Tagging: recommends items using tagsassigned by different users 4. Collaborative Filtering trying to predict the opinion the user will have on thedifferent items and be able to recommend the bestitems to each user based on the users previouslikings and the opinions of other like minded users. 5. Collaborative Filtering The task of a CF algorithm is to find item likeliness of twoforms :Prediction a numerical value, expressing the predictedlikeliness value about an item of the active userRecommendation a list of N items that the active user willlike the most 6. K Nearest Neighbour Algorithm A distance measure is needed to determine thecloseness of instances Classify an instance by finding its nearest neighborsand picking the most popular class among theneighbors 7. MegaMindToy Story Despicable MeLion King Kung FuPandaZeynep 4 5 3 2 4Funda 3 3 2 3 5Pnar 3 3 4 2 3Glten 4 4 5 4 5Yaz 4 5 ? 4 5Rating Prediction 8. Application MovieLens Database (1M) 3883 movies 6040 users 1000209 ratings Technologies ASP.Net 4.0 MS SQL Server 2008 9. RATING PREDICTION DATABASE DIAGRAMMoviesMovieIDTitleGenreRatingsIDUserIDMovieIDRatingTimestampUsersUserIDGenderAgeOccupationZipCodeAgeIdDescriptionOccupationIdDescriptionPredictionsIDUserIDMostSimilarUserIDDifferenceTimeElapsedMovieIDPredictedRatingActualRating 10. Error MeasurementMean Square Error (MSE)=0.975Mean Absolute Error(MAE)=0.679 11. DEMO 12. ProCon Cold-start Problem Storage: all trainingexamples are saved inmemory Time: to classify x, youneed to loop over alltraining examples (x,y) tocompute distance betweenx and x. Simple to implement anduse Comprehensible easy toexplain prediction Robust to noisy data byaveraging k-nearestneighborsKNN Algorithm 13. Conclusion Recommending and personalization are importantapproaches to combating information over-load. Machine Learning is an important part of systems forthese tasks. Collaborative Filtering has its own problems Better results would be achieved by use ofcontent, tags and more optimized similarityfunctions. 14. Thank you