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Collaborative Filtering using KNN
Collaborative filtering is a technique used by recommender systems to predict items users may like based on opinions of similar users. K-nearest neighbors (KNN) is a collaborative filtering algorithm that finds the k most similar users and bases predictions on the ratings of those neighbors. The document describes KNN collaborative filtering, including finding neighbor similarity, making predictions, and evaluating error rates on a movie recommendation system using the MovieLens dataset.
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Collaborative Filtering using KNN
- 1. COLLABORATIVE FILTERING USING KNNALGORITHM Şeyda HATİPOĞLU 11.06.2013
- 2. Recommender Systems • Softwaretools and techniques providing suggestions for items to be of use to a user • Recommender systems analyze patterns of user interest in items or products to provide personalized recommendations of items that will suit a user’s taste Item - What the system recommends to the user (CD, news, books, movies...) User preferences - ratings for products User actions - user browsing history
- 3. RS Techniques • Collaborative-Filteringsystem – recommends to the active user the items that other users with similar tastes liked in the past • Content-based system – recommend items that are similar to the ones that the user liked in the past • Hybrid-Collaborative Filtering • Tagging: recommends items using tags assigned by different users
- 4. Collaborative Filtering • tryingto predict the opinion the user will have on the different items and be able to recommend the “best” items to each user based on the user’s previous likings and the opinions of other like minded users.
- 5. Collaborative Filtering • Thetask of a CF algorithm is to find item likeliness of two forms : Prediction – a numerical value, expressing the predicted likeliness value about an item of the active user Recommendation – a list of N items that the active user will like the most
- 6. K Nearest NeighbourAlgorithm • A distance measure is needed to determine the “closeness” of instances • Classify an instance by finding its nearest neighbors and picking the most popular class among the neighbors
- 7. Mega Mind Toy Story Despicabl eMe Lion King Kung Fu Panda Zeynep 4 5 3 2 4 Funda 3 3 2 3 5 Pınar 3 3 4 2 3 Gülten 4 4 5 4 5 Yağız 4 5 ? 4 5 Rating Prediction
- 8. Application • MovieLens Database(1M) 3883 movies 6040 users 1000209 ratings • Technologies ASP.Net 4.0 MS SQL Server 2008
- 9. RATING PREDICTION DATABASEDIAGRAM Movies MovieID Title Genre Ratings ID UserID MovieID Rating Timestamp Users UserID Gender Age Occupation ZipCode Age Id Description Occupation Id Description Predictions ID UserID MostSimilarUserID Difference TimeElapsed MovieID PredictedRating ActualRating
- 10. Error Measurement Mean SquareError (MSE)=0.975 Mean Absolute Error(MAE)=0.679
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- 12. Pro Con • Cold-start Problem •Storage: all training examples are saved in memory • Time: to classify x, you need to loop over all training examples (x’,y’) to compute distance between x and x’. Simple to implement and use Comprehensible – easy to explain prediction Robust to noisy data by averaging k-nearest neighbors KNN Algorithm
- 13. Conclusion Recommending andpersonalization are important approaches to combating information over-load. Machine Learning is an important part of systems for these tasks. Collaborative Filtering has its own problems Better results would be achieved by use of content, tags and more optimized similarity functions.
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