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Collaborative Filtering and Recommender Systems By Navisro Analytics
The document discusses various approaches to recommender systems, including collaborative filtering techniques and model-based training methods, along with specific algorithms like SVD, LDA, and item-based filtering. It covers general steps for building a recommender, challenges faced such as input data sparsity and overfitting, and the evaluation of system accuracy through metrics like precision and recall. Additionally, it lists several open-source tools and resources for implementing collaborative filtering, alongside key readings related to the field.
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Collaborative Filtering and Recommender Systems By Navisro Analytics
- 1. ACM Data MiningHackathon 8/18/2012 Recommender Systems Navisro Analytics @navisro info@navisro.com http://www.navisro.com
- 2. Capturing the LongTail…
- 3. Recommender Approaches Model Based Training SVM, LDA, SVD for Collaborative implicit features Filtering – Item- Item similarity (You like Godfather so you will like Attribute-based Scarface - Netflix) recommendations (You like action movies, starring Clint Eastwood, you Social+Interest might like “Good, Graph Based (Your Bad and the Ugly” friends like Lady Netflix) Collaborative Gaga so you will Filtering – User- like Lady Gaga, User Similarity PYMK – Facebook, LinkedIn) (People like you who bought beer Item also bought Hierarchy diapers - Target) (You bought Printer you will also need ink - BestBuy)
- 4. Other/Model-based Approaches • Slope one recommender • Latent factor Models for Web Data – Matrix factorization using SVD, ALS, with Regularization – LDA, SVM, Bayesian Clustering
- 5. General Steps •Problem definition (user-based, item-based, ratings/binary…) Data Prep •Map-Reduce, cleansing, massaging data (input matrix) •Training Set, Validation Set Normalize • bias removal - Z-score, Mean-centering, Log • Pearson Correlation Coefficient Similarity • Cosine Similarity weights/Neighbors • K-nearest neighbor Train • Training model (only in model-based approaches) • Predict missing ratings Predict • top-N predictions for every user Denormalize • Reverse of normalization Evaluate Accuracy • Accuracy, Precision, Recall, F1, ROC
- 6. User-based CF Reference: Recommenderlabvignette, http://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf
- 7. Challenges • Dimensionality reduction(e.g. use PCA) • Input data sparsity (aka cold start problem) • Overfitting to training data set (use regularization) • Data wrangling, in general…
- 8. Just How Goodis your Recommender? • Evaluation of predicted ratings (Mean Average Error, Root Mean Sq Error) • Evaluation of top-N recommendations – Mean Absolute Error – Accuracy – Precision & Recall (F1 score) – ROC curve
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- 10. Open Source Tools Software Description Language URL Hadoop ML library that includes http://mahout.apache.org/ Apache Mahout Collaborative Filtering Java Cofi Collaborative Filtering Library Java http://www.nongnu.org/cofi/ Components to create Crab recommender systems Python https://github.com/muricoca/crab easyrec Recommender for web pages Java http://easyrec.org/ Collaborative Filtering algorithms LensKit from GroupLens Research Java http://lenskit.grouplens.org/ MyMediaLite Recommender system algorithms C#/Mono http://mloss.org/software/view/282/ Toolkit for Feature based Matrix SVDFeature Factorization C++ http://mloss.org/software/view/333/ Collaborative Filtering for Vogoo PHP LIB personalized web sites PHP http://sourceforge.net/projects/vogoo/ http://cran.r- R library for developing and testing project.org/web/packages/recommender recommenderlab collaborative filtering systems R lab/index.html Python module integrating classic ML algorithms in scientific Python packages Scikit-learn (numpy, scipy, matplotlib) Python http://scikit-learn.org/stable/
- 11. recommenderlab Reference: Recommenderlab vignette,http://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf
- 12. Mahout DataModel model =new FileDataModel(new File("data.txt")); // Construct the list of pre-computed correlations Collection<GenericItemSimilarity.ItemItemSimilarity> correlations = ...; ItemSimilarity itemSimilarity = new GenericItemSimilarity(correlations); Recommender recommender = new GenericItemBasedRecommender(model, itemSimilarity); Recommender cachingRecommender = new CachingRecommender(recommender); ... List<RecommendedItem> recommendations = cachingRecommender.recommend (1234, 10);
- 13. Peter Harrington’s SamplePy Code
- 14. 2. References &Reading • High Level Reading – Programming Collective Intelligence by Toby Segaran. The 2nd chapter gives a good introduction to collaborative filtering with Python examples (non-SVD). – Matrix Factorization Techniques for Recommender Systems Yehuda Koren; Robert Bell; Chris Volinsky, IEEE Computer, 2009, 8 • Singular Value Decomposition (SVD) Reading – The Singular Value Decomposition, by Jody Hourigan and Lynn McIndoo, Linear Algebra – Math 45. http://online.redwoods.edu/INSTRUCT/darnold/LAPROJ/Fall98/ JodLynn/report2.pdf w/ Matlab & image examples – Numerical Recipes, 3rd Edition, Press et. al.,2007, p65-75.
- 15. References & Reading(continued) • Collaborative Filtering Reading – See papers on research.yahoo.com/Yehuda_Koren – Collaborative Filtering for Implicit Feedback Datasets, Yifan Hu; Yehuda Koren; Chris Volinsky, IEEE International Conference on Data Mining (ICDM 2008), IEEE, 2008 – Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model, Yehuda Koren, ACM Int. Conference on Knowledge Discovery and Data Mining (KDD’08), 2008 – Collaborative Filtering with Temporal Dynamics, Yehuda Koren, KDD 2009, ACM, 2009 – James Thornton’s CF Blog http://original.jamesthornton.com/cf/ – Apache Mahout Recommender https://cwiki.apache.org/MAHOUT/recommender- documentation.html – Flexible Collaborative Filtering In Java With Mahout Taste - Philippe Adjiman – Books, Articles and Tutorials on Mahout/Cofi
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