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A Framework for Training Hybrid Recommender Systems

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Recommender Systems (RS) are widely used to provide users with
personalized suggestions taken from an extended variety of items.
One of the major challenges of RS is the accuracy in cold-start
situations where little feedback is available for a user or an item.
Exploiting available user and item metadata helps to cope with this
problem. We propose a hybrid training framework consisting of two
predictors, a collaborative filtering instance and a metadata-based
instance relying on content and demographic data. Our framework
supports a wide range of algorithms to be used as predictors. The
cross-training mechanism we design minimizes the weaknesses of
one instance by updating its training with predicted data from the
other instance. A sophisticated sampling function selects ratings to
be predicted for cross-training
We evaluate our framework conducting multiple experiments
on the MovieLens 100K dataset, simulating different scenarios in-
cluding user and item cold-start. Our framework outperforms state-
of-the-art algorithms and is able to provide accurate predictions
across all tested scenarios.

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A Framework for Training Hybrid Recommender Systems

  1. 1. A Framework for Training Hybrid Recommender SystemsA FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 1 A Framework for Training Hybrid Recommender Systems Simon Bremer, Alan Schelten, Enrico Lohmann, Martin Kleinsteuber
  2. 2. A Framework for Training Hybrid Recommender Systems MERCATEO •Online procurement •Business to Business •One of the largest in Europe A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 2
  3. 3. A Framework for Training Hybrid Recommender Systems MERCATEO UNITE A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 3 •Marketplace, focus on networking •Cooperation with SAP Ariba •Currently beta-phase •Not enough data (yet) -> Testing on MovieLens
  4. 4. A Framework for Training Hybrid Recommender Systems APPLICATION OF RECOMMENDER SYSTEM A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 4 •Suggest supplier (items) to customers (users) •Available data: • Explicit feedback: Trading frequency • Metadata about customers/suppliers •Problem: Cold-Start
  5. 5. A Framework for Training Hybrid Recommender Systems METADATA-BASED APPROACH A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 5 •Combination of Content-based and Demographic Filtering (MD) •Use of all available metadata •“Perfect” to cope with cold-start •Downside: Collaborative Filtering better for active users/items
  6. 6. A Framework for Training Hybrid Recommender Systems ALGORITHM PERFORMANCE - COLD-START A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 6
  7. 7. A Framework for Training Hybrid Recommender Systems HYBRIDIZATION STRATEGIES A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 7 •Hybridization of two different algorithms (CF + MD) •Performance depends on area of matrix •Goal: Improve both, get best results
  8. 8. A Framework for Training Hybrid Recommender Systems RESULT MERGING A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 8 CF MD TRAINING DATA RESULTS
  9. 9. A Framework for Training Hybrid Recommender Systems CROSS-TRAINING A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 9 CF MD TRAINING DATA RESULTS
  10. 10. A Framework for Training Hybrid Recommender Systems CROSS-TRAINING A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 10
  11. 11. A Framework for Training Hybrid Recommender Systems ALGORITHM PERFORMANCE - COLD-START A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 11
  12. 12. A Framework for Training Hybrid Recommender Systems CF SAMPLING A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 12
  13. 13. A Framework for Training Hybrid Recommender Systems MD SAMPLING A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 13
  14. 14. A Framework for Training Hybrid Recommender Systems CROSS TRAINING A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 14
  15. 15. A Framework for Training Hybrid Recommender Systems CF MODEL A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 15 •Latent Feature Matrix Factorization •SVD++ by Koren •L2 Regularization on biases and latent feature weights
  16. 16. A Framework for Training Hybrid Recommender Systems MD MODEL A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 16 •Combination of different biases inspired by possible causal links •Metadata of the MovieLens dataset: Age Sex Occupation Zip Code 49 M Engineer 21044 40 F Librarian 30030 32 M Writer 55369 41 M Programmer 94043 7 M Student 55436 24 M Artist 10003 Title Genres From Dusk Till Dawn (1996) Action|Comedy|Crime|Horror|Thriller Toy Story (1995) Animation|Children's|Comedy Misérables, Les (1995) Drama|Musical Ghostbusters (1984) Comedy|Horror Blade Runner (1982) Film-Noir|Sci-Fi Casablanca (1942) Drama|Romance|War
  17. 17. A Framework for Training Hybrid Recommender Systems MD MODEL A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 17 •Offset: Demographic group -> individual movie •Example: Programmers like “The Matrix”
  18. 18. A Framework for Training Hybrid Recommender Systems MD MODEL A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 18 •Offset: Individual user -> Movie attribute •Example: I like Sci-Fi
  19. 19. A Framework for Training Hybrid Recommender Systems MD MODEL A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 19 •Offset: Demographic group -> Movie attribute •Example: Men like Western movies more than Women
  20. 20. A Framework for Training Hybrid Recommender Systems MD MODEL A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 20 •Inclusion of user and item bias •Attribute bias covers absolute offset of user and item features
  21. 21. A Framework for Training Hybrid Recommender Systems EVALUATION A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 21
  22. 22. A Framework for Training Hybrid Recommender Systems EVALUATION A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 22
  23. 23. A Framework for Training Hybrid Recommender Systems EVALUATION A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 23
  24. 24. A Framework for Training Hybrid Recommender Systems RESULTS A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 24
  25. 25. A Framework for Training Hybrid Recommender SystemsA FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 25 THANK YOU! CONTACT Simon Bremer - simon.bremer@tum.de

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