What about Interpreting Features in Matrix Factorization-based Recommender Systems as Users?

Marharyta Aleksandrova 1 Armelle Brun 1 Anne Boyer 1
1 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : Matrix factorization (MF) is a powerful approach used in recommender systems. One main drawback of MF is the dif- ficulty to interpret the automatically formed features. Fol- lowing the intuition that the relation between users and items can be expressed through a reduced set of users, re- ferred to as representative users, we propose a simple mod- ification of a traditional MF algorithm, that forms a set of features corresponding to these representative users. On one state of the art dataset, we show that the proposed representative users-based non-negative matrix factorization (RU-NMF) discovers interpretable features, while slightly (in some cases insignificantly) decreasing the accuracy.
Type de document :
Communication dans un congrès
25th ACM Conference on Hypertext and Social Media - Workshop on Social Personalisation , Sep 2014, santiago du chili, Chile
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https://hal.inria.fr/hal-01108748
Contributeur : Armelle Brun <>
Soumis le : vendredi 23 janvier 2015 - 14:05:05
Dernière modification le : mardi 24 avril 2018 - 13:09:13

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  • HAL Id : hal-01108748, version 1

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Marharyta Aleksandrova, Armelle Brun, Anne Boyer. What about Interpreting Features in Matrix Factorization-based Recommender Systems as Users?. 25th ACM Conference on Hypertext and Social Media - Workshop on Social Personalisation , Sep 2014, santiago du chili, Chile. 〈hal-01108748〉

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