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Recommender Systems

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Video: http://vimeo.com/16537278
An introduction to recommender systems.
Given at Talis (www.talis.com) Oct. 28 2010

Published in: Technology, Education

Transcript of "Recommender Systems"

  1. 1. alan.said@dai-lab.de @alansaid Alan Said Recommender Systems 1/30/2015 1Talis
  2. 2. Abstract • The amount of data in the digital universe is estimated to hit 1.2 Zettabytes (1 billion terabytes) during 2010. • These data quantities make discovering relevant information a difficult task. • Recommender Systems are an integral tool for assisting users in information discovery. • By combining wisdom of crowds, content, user profiles, etc. Recommender Systems find relevant data for us. “We are leaving the age of information and entering the age of recommendation” Chris Anderson, The Long Tail 1/30/2015 Talis 2
  3. 3. Outline • Introduction • Standard recommenders – Content-based – Collaborative filtering-based – Hybrid recommenders • Context-aware recommenders • Recommenders at Talis 1/30/2015 Talis 3
  4. 4. Introduction • IMDb, one of the first online recommender systems, turned 20 on October 17th 2010. • Ever since, recommender systems have, through relatively simple techniques, produced adequately good results • Is adequately good good enough? – How can recommender systems be improved? – What do we need to improve them? 1/30/2015 Talis 4
  5. 5. Recommender System Types Introduction • Semantic recommenders – explicit information – Content – Keywords – Genre – etc. • Social recommenders – implicit information (collaborative filtering) – Item-based user-user similarities, i.e. which users like similar things – Content-ignorant • Hybrid recommenders – Combinations of content- and CF-based • Context-aware recommenders – Aware of the current situation 1/30/2015 Talis 5
  6. 6. Content-based recommenders 1/30/2015 Talis 6
  7. 7. Social recommenders Most common recommender systems approach use Collaborative Filtering How does collaborative filtering work? • Calculates similarities between all users • Finds users similar to you • Fills in your ”gaps” based on similar users, usually by a k-nearest neighbor algorithm 1/30/2015 Talis 7 Recommend a book for user C
  8. 8. Social recommenders Most common recommender systems approach use Collaborative Filtering How does collaborative filtering work? • Calculates similarities between all users • Finds users similar to you • Fills in your ”gaps” based on similar users, usually by a k-nearest neighbor algorithm 1/30/2015 Talis 8 Recommend a book for user C
  9. 9. Social recommenders Most common recommender systems approach use Collaborative Filtering How does collaborative filtering work? • Calculates similarities between all users • Finds users similar to you • Fills in your ”gaps” based on similar users, usually by a k-nearest neighbor algorithm 1/30/2015 Talis 9 Recommend a book for user C
  10. 10. Hybrid models Hybrid recommender systems combine semantic recommenders with collaborative filtering ones. 1/30/2015 Talis 10 Recommend a book for user C
  11. 11. Hybrid models Hybrid recommender systems combine semantic recommenders with collaborative filtering ones. 1/30/2015 Talis 11 Recommend a book for user C
  12. 12. Context-awareness Is an item as relevant on a Sunday afternoon as on a Tuesday morning? 1/30/2015 Talis 12
  13. 13. What is context? Context-awareness in RecSys ”Any information that can be used to characterise the situation of entities”, Dey 2001 1. Item context • Seasonal (Christmas, Oscar’s) • Relation (movie sequel, director, actor) 2. User context • Surroundings (weather, location) • Company (alone, with friends) • Mood/emotions • any user related factor 1/30/2015 Talis 13
  14. 14. Why Context? Context-awareness in RecSys 1/30/2015 Talis 14 + • Filters relevant information • Ad hoc recommendations • Aware of changes - • What is context? • Where do we find it?
  15. 15. Applying Context-awareness Current state of the art research presents two types of context- awareness: • Context-aware collaborative filtering – Performs standard CF on virtual, contextual, items or users – Benefits: simple – Drawbacks: statically defined context 1/30/2015 Talis 15
  16. 16. Applying Context-awareness Current state of the art research presents two types of context- awareness: • Context-aware collaborative filtering – Performs standard CF on virtual, contextual, items or users – Benefits: simple – Drawbacks: statically defined context • Tensor factorization for context- awareness – Models the data as a tensor – Applies higiher-order factorization techniques (HoSVD, PARAFAC, HyPLSA, etc) to model context in a latent space – Benefits: no prior context identification necessary – Drawbacks: adds complexity 1/30/2015 Talis 16
  17. 17. My work 1/30/2015 Talis 17 Semantic recommenders Social recommenders Context-aware recommenders
  18. 18. Where does this fit at Talis? • Library data – Loan events – CF – Book meta data – semantic recommenders – Time of loan event – context-awareness 1/30/2015 Talis 18
  19. 19. Distributed higher order recommender system • Use matrix factorization techniques to make a tensor factorization approximation in MapReduce • By matricizing the tensor, standard matrix factorization approaches can be run in parallel • What is matrix factorization? – Decomposition of a matrix into its building blocks (SVD example) • A = UΣVT where A is the matrix, Σ is a diagonal matrix and U and V are unitary matrices. • By only taking the k first diagonal values in Σ and multiplying the resulting matrix back with U and V we obtain a k ranked approximation of the initial A matrix 1/30/2015 Talis 19 book user
  20. 20. Questions? 1/30/2015 Talis 20 Thank you!
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