This document presents a framework for training hybrid recommender systems to address the cold-start problem. It describes combining collaborative filtering and metadata-based approaches through result merging, cross-training, and sampling. The collaborative filtering model uses SVD++ to factorize latent user and item features, while the metadata-based model incorporates user, item, and demographic biases based on attributes. The framework is evaluated on the MovieLens dataset to improve recommendations for new users and items.

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. A Framework for Training Hybrid Recommender Systems
MERCATEO
•Online procurement
•Business to Business
•One of the largest in Europe
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3. A Framework for Training Hybrid Recommender Systems
MERCATEO UNITE
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•Marketplace, focus on networking
•Cooperation with SAP Ariba
•Currently beta-phase
•Not enough data (yet) -> Testing on MovieLens

4. A Framework for Training Hybrid Recommender Systems
APPLICATION OF RECOMMENDER SYSTEM
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•Suggest supplier (items) to customers (users)
•Available data:
• Explicit feedback: Trading frequency
• Metadata about customers/suppliers
•Problem: Cold-Start

5. A Framework for Training Hybrid Recommender Systems
METADATA-BASED APPROACH
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•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. A Framework for Training Hybrid Recommender Systems
ALGORITHM PERFORMANCE - COLD-START
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7. A Framework for Training Hybrid Recommender Systems
HYBRIDIZATION STRATEGIES
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•Hybridization of two different algorithms (CF + MD)
•Performance depends on area of matrix
•Goal: Improve both, get best results

8. A Framework for Training Hybrid Recommender Systems
RESULT MERGING
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CF
MD
TRAINING
DATA
RESULTS

9. A Framework for Training Hybrid Recommender Systems
CROSS-TRAINING
A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 9
CF
MD
TRAINING
DATA
RESULTS

10. A Framework for Training Hybrid Recommender Systems
CROSS-TRAINING
A FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 10

11. A Framework for Training Hybrid Recommender Systems
ALGORITHM PERFORMANCE - COLD-START
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12. A Framework for Training Hybrid Recommender Systems
CF SAMPLING
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13. A Framework for Training Hybrid Recommender Systems
MD SAMPLING
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14. A Framework for Training Hybrid Recommender Systems
CROSS TRAINING
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15. A Framework for Training Hybrid Recommender Systems
CF MODEL
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•Latent Feature Matrix Factorization
•SVD++ by Koren
•L2 Regularization on biases and latent feature weights

16. A Framework for Training Hybrid Recommender Systems
MD MODEL
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•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. A Framework for Training Hybrid Recommender Systems
MD MODEL
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•Offset: Demographic group -> individual movie
•Example: Programmers like “The Matrix”

18. A Framework for Training Hybrid Recommender Systems
MD MODEL
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•Offset: Individual user -> Movie attribute
•Example: I like Sci-Fi

19. A Framework for Training Hybrid Recommender Systems
MD MODEL
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•Offset: Demographic group -> Movie attribute
•Example: Men like Western movies more than Women

20. A Framework for Training Hybrid Recommender Systems
MD MODEL
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•Inclusion of user and item bias
•Attribute bias covers absolute offset of user and item features

21. A Framework for Training Hybrid Recommender Systems
EVALUATION
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22. A Framework for Training Hybrid Recommender Systems
EVALUATION
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23. A Framework for Training Hybrid Recommender Systems
EVALUATION
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24. A Framework for Training Hybrid Recommender Systems
RESULTS
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25. A Framework for Training Hybrid Recommender SystemsA FRAMEWORK FOR TRAINING HYBRID RECOMMENDER SYSTEMS 25
THANK YOU!
CONTACT
Simon Bremer - simon.bremer@tum.de