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Introduction to Recommendation Systems

Introduction to Recommendation Systems

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Jaidev Deshpande

November 28, 2015
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  1. The Long Tail Phenomenon Introduction to Recommender Systems Jaidev Deshpande

    Cube26 Software Pvt Ltd November 28, 2015 Jaidev Deshpande Introduction to Recommender Systems
  2. The Long Tail Phenomenon Examples Jaidev Deshpande Introduction to Recommender

    Systems
  3. The Long Tail Phenomenon Examples Jaidev Deshpande Introduction to Recommender

    Systems
  4. The Long Tail Phenomenon Credits: Chris Anderson (http://thelongtail.com) Brick-and-mortar vs

    online marketplaces: hundreds of thousands vs tens of millions of choices Tailoring to individual responses Discovery of interests Jaidev Deshpande Introduction to Recommender Systems
  5. The Long Tail Phenomenon Introduction Approaches to Recommendation Systems: Content-based:

    Use properties of items being recommended. Collaborative Filtering: Users ”collaborate” to get the best recommendations Jaidev Deshpande Introduction to Recommender Systems
  6. The Long Tail Phenomenon The Model The Utility Matrix Airframe

    To Kill A Mockingbird Seabiscuit Beloved users 9 0 0 0 6 14 0 0 0 0 16 9 0 0 0 17 0 0 0 0 26 0 10 9 0 Jaidev Deshpande Introduction to Recommender Systems
  7. The Long Tail Phenomenon Populating the Utility Matrix Explicit: Asking

    users to rate items: Ola, Uber :) Flipkart, Amazon :( Inferred: Quantify user behaviour. Jaidev Deshpande Introduction to Recommender Systems
  8. The Long Tail Phenomenon Content-based Recommendations Collect attributes of books?

    Perform text processing on the content Generally: Mining for metadata (Google Search APIs, etc) Look for web APIs of other search services Data-specific feature extraction (Signal processing, text processing) Look for scikit-learn, pandas, nltk, etc. Jaidev Deshpande Introduction to Recommender Systems
  9. The Long Tail Phenomenon Collaborative Filtering Concept of similarity Similarity

    measures: sklearn.metrics.pairwise Clustering / grouping similar profiles Calculate ”projected” ratings. Jaidev Deshpande Introduction to Recommender Systems
  10. The Long Tail Phenomenon Practical Considerations S = f (U)

    (1) where f is the similarity function and U ∈ Zmxn S ∈ R+ mxm U is sparse, and S is massive S is symmetrical, sparsity ≤ 50% Nature of the data affects f Distributed execution: Workers need access to S Need to maintain designated copies of U Look for scipy.[sparse, spatial, dist], sklearn.metrics Jaidev Deshpande Introduction to Recommender Systems
  11. The Long Tail Phenomenon Popular Implementations Graphlab: graph based, not

    free Crab: NumPy based, FOSS Spark MLlib: Collaborative filtering Jaidev Deshpande Introduction to Recommender Systems