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Collaborative Filtering and Recommender Systems By Navisro Analytics
 

Collaborative Filtering and Recommender Systems By Navisro Analytics

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Recommendation System Overview, Types of Recommender System, and OpenSource tools/libraries available.

Recommendation System Overview, Types of Recommender System, and OpenSource tools/libraries available.

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    Collaborative Filtering and Recommender Systems By Navisro Analytics Collaborative Filtering and Recommender Systems By Navisro Analytics Presentation Transcript

    • ACM Data Mining Hackathon 8/18/2012Recommender Systems Navisro Analytics @navisro info@navisro.com http://www.navisro.com
    • Capturing the Long Tail…
    • 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, starringClint 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)
    • Other/Model-based Approaches• Slope one recommender• Latent factor Models for Web Data – Matrix factorization using SVD, ALS, with Regularization – LDA, SVM, Bayesian Clustering
    • 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 Similarityweights/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 normalizationEvaluate Accuracy • Accuracy, Precision, Recall, F1, ROC
    • User-based CFReference: Recommenderlab vignette, http://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf
    • 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…
    • Just How Good is 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
    • Tools
    • Open Source ToolsSoftware Description Language URL Hadoop ML library that includes http://mahout.apache.org/Apache Mahout Collaborative Filtering JavaCofi Collaborative Filtering Library Java http://www.nongnu.org/cofi/ Components to createCrab recommender systems Python https://github.com/muricoca/crabeasyrec Recommender for web pages Java http://easyrec.org/ Collaborative Filtering algorithmsLensKit from GroupLens Research Java http://lenskit.grouplens.org/MyMediaLite Recommender system algorithms C#/Mono http://mloss.org/software/view/282/ Toolkit for Feature based MatrixSVDFeature Factorization C++ http://mloss.org/software/view/333/ Collaborative Filtering forVogoo PHP LIB personalized web sites PHP http://sourceforge.net/projects/vogoo/ http://cran.r- R library for developing and testing project.org/web/packages/recommenderrecommenderlab collaborative filtering systems R lab/index.html Python module integrating classic ML algorithms in scientific Python packagesScikit-learn (numpy, scipy, matplotlib) Python http://scikit-learn.org/stable/
    • recommenderlabReference: Recommenderlab vignette, http://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf
    • MahoutDataModel model = new FileDataModel(new File("data.txt"));// Construct the list of pre-computed correlationsCollection<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);
    • Peter Harrington’s Sample Py Code
    • 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.
    • 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
    • Questions?