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2017-08-23 - Colloque/Article dans les actes avec comité de lecture - Anglais - 5 page(s)

Sommer Felix, Lecron Fabian , Fouss François, "Recommender Systems: The case of repeated interaction in Matrix Factorization" in IEEE/WIC/ACM International Conference on Web Intelligence, 843-847, Leipzig, Allemagne, 2017

  • Codes CREF : Technologies de l'information et de la communication (TIC) (DI4730)
  • Unités de recherche UMONS : Management de l'Innovation Technologique (F113)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech)

Abstract(s) :

(Anglais) This work presents a new matrix factorization recommender system approach, that takes repeated interaction into account. We analyze if and how users' repeated interaction behavior-such as repeat purchases can be integrated into a recommender system. We develop a method that takes advantage of this additional data dimension that is studied in many other fields to derive useful conclusions. Furthermore, we empirically test our method on real-life retailer data and on the Last.fm dataset. We compare our algorithm with popular matrix factorization approaches. Results indicate that our method manages to slightly outperform the existing methods.