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Scientific Recommender Systems - PG PUSHPIN
 

Scientific Recommender Systems - PG PUSHPIN

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A presentation about scientific recommender systems from the seminarphase of the project group PUSHPIN at the University of Paderborn

A presentation about scientific recommender systems from the seminarphase of the project group PUSHPIN at the University of Paderborn

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    Scientific Recommender Systems - PG PUSHPIN Scientific Recommender Systems - PG PUSHPIN Presentation Transcript

    • Scientific Recommender Systems Jan Petertonkoker January 12th, 2012 Scientific Recommender Systems 1
    • ContentsContents 1. Motivation (Examples) 2. Recommender Systems 3. Categories of Recommender Systems 3.1 Content-based Recommender: TF-IDF 3.2 Collaborative Recommender: Apache Mahout 3.3 Hybrid Recommender: SciPlore 4. Visualizations (Prototype) 5. Conclusion Scientific Recommender Systems 2
    • MotivationMotivation Example: Amazon Scientific Recommender Systems 3
    • MotivationMotivation Example: Twitter Scientific Recommender Systems 4
    • Recommender SystemsRecommender Systems u :C ×S →R C - set of all users S - set of all items R - totally ordered set, which describes the usefulness of the items to the respective user Scientific Recommender Systems 5
    • Categories of Recommender SystemsCategories of Recommender Systems content-based: items are recommended that are similar to items the user liked in the past collaborative: items are recommended that people liked that are similar to the user (similar taste/preferences) hybrid: a combination of content-based and collaborative recommendation approaches Scientific Recommender Systems 6
    • Categories of Recommender SystemsContent-based Recommender Systems utility u(c, s) of an item s is estimated with the help of the utilities u(c, si ) of all items si ∈ S that user c already rated that are similar to item s similarity between items is calculated according to their attributes user and item profiles common problems limited content analysis overspecialization new user problem Scientific Recommender Systems 7
    • Categories of Recommender SystemsContent-based Recommender: TF-IDF N - total number of documents in the system keyword ki appears in ni of the documents fi,j denotes the number of times a certain keyword ki appears in a document dj Scientific Recommender Systems 8
    • Categories of Recommender SystemsContent-based Recommender: TF-IDF N - total number of documents in the system keyword ki appears in ni of the documents fi,j denotes the number of times a certain keyword ki appears in a document dj Term Frequency fi,j TFi,j = maxz fz,j maximum in the denominator calculated over the frequencies of all keywords kz that appear in document dj Scientific Recommender Systems 8
    • Categories of Recommender SystemsContent-based Recommender: TF-IDF N - total number of documents in the system keyword ki appears in ni of the documents fi,j denotes the number of times a certain keyword ki appears in a document dj Term Frequency fi,j TFi,j = maxz fz,j maximum in the denominator calculated over the frequencies of all keywords kz that appear in document dj Inverse Document Frequency N for a keyword ki : IDFi = log ni Scientific Recommender Systems 8
    • Categories of Recommender SystemsContent-based Recommender: TF-IDF N - total number of documents in the system keyword ki appears in ni of the documents fi,j denotes the number of times a certain keyword ki appears in a document dj Term Frequency fi,j TFi,j = maxz fz,j maximum in the denominator calculated over the frequencies of all keywords kz that appear in document dj Inverse Document Frequency N for a keyword ki : IDFi = log ni TF-IDF wi,j = TFi,j × IDFi Scientific Recommender Systems 8
    • Categories of Recommender SystemsCollaborative Recommender Systems utility u(c, s) of an item s is estimated with the help of the utilities u(ci , s) assigned by users ci ∈ C that are similar to user c. common problems new user/item problem cold start sparsity scalability Scientific Recommender Systems 9
    • Categories of Recommender SystemsCollaborative Recommender: Apache Mahout (1) provides a ”toolbox” to create collaborative recommender systems input user (long), item (long), preference (double) 1, 111, 2.5 data model input from different file formats, database increase performance with specific data structures Scientific Recommender Systems 10
    • Categories of Recommender SystemsCollaborative Recommender: Apache Mahout (2) user-based recommender Scientific Recommender Systems 11
    • Categories of Recommender SystemsCollaborative Recommender: Apache Mahout (2) user-based recommender item-based recommender Scientific Recommender Systems 11
    • Categories of Recommender SystemsCollaborative Recommender: Apache Mahout (3) similarity measures pearson correlation (cosine similarity) euclidean distance spearman correlation log-likelihood ... slope-one recommender other experimental recommender implementations e.g. cluster-based Scientific Recommender Systems 12
    • Categories of Recommender SystemsHybrid Recommender Systems combination of content-based and collaborative methods seperate content-based and collaborative recommender systems; results get combined somehow collaborative recommender system with some added aspects of content-based methods content-based recommender system with some added aspects of collaborative methods a single recommender system which unifies content-based and collaborative methods from the beginning Scientific Recommender Systems 13
    • Categories of Recommender SystemsHybrid Recommender: SciPlore SciPlore Overview Scientific Recommender Systems 14
    • Visualizations (Prototype)Visualizations (Prototype) several recommenders based on given database visualizations for explaining recommendations Live Presentation Scientific Recommender Systems 15
    • ConclusionSummary utility function categories of recommender systems content-based collaborative hybrid implementation with Apache Mahout possible visualizations Scientific Recommender Systems 16
    • Conclusion Questions?Scientific Recommender Systems 17
    • ReferencesReferences Apache Mahout: Scalable machine learning and data mining. http://mahout.apache.org/ - accessed on 6th January 2012 SciPlore: Exploring Science. http://www.sciplore.org - accessed on 6th January 2012 G Adomavicius and A Tuzhilin. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734-749, 2005 B Gipp, J Beel and C Hentschel. Scienstein: A research paper recommender system, volume 301, pages 309-315. IEEE, 2009 Sean Owen, Robin Anil, Ted Dunning and Ellen Friedman. Mahout in Action, 2011 Scientific Recommender Systems 18