Progress in Artificial Intelligence

, Volume 7, Issue 1, pp 15–29 | Cite as

An adapted incremental graded multi-label classification model for recommendation systems

  • Khalil Laghmari
  • Christophe Marsala
  • Mohammed Ramdani
Regular Paper


Graded multi-label classification (GMLC) is the task of assigning to each data a set of relevant labels with corresponding membership grades. This paper is interested in GMLC for large and evolving datasets where data are collected from a possibly infinite stream. Many commercial and non-commercial websites acquire such data by giving users the opportunity to rank items any time using an ordinal scale like one-to-five star rating. Typically these collected data are sparse because users rank only a small subset of items. Websites rely on recommender systems to dynamically adapt the recommended item set for each user. Hence, the applied recommender system should remain scalable and efficient when dealing with sparse data. State-of-the-art methods related to GMLC were tested only in batch mode. Their performance in an incremental mode is not investigated, especially in presence of sparse data and concept drifts. This paper presents our proposed incremental GMLC method which answers the above challenges and can be applied to build a recommender system. This method is tested on the well-known MovieLens and Jester datasets, and it is able to adapt to concept drifts and maintain the Hamming loss at a low level.


Graded multi-label classification Recommender system Sparse data Incremental mode Concept drift 



This work has been partially funded by the Ministère de l’Enseignement Supérieur, de la Recherche Scientifique et de la Formation des Cadres (MESRSFC) of Morocco and the French Institute of the French Embassy in Morocco.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Khalil Laghmari
    • 1
    • 2
  • Christophe Marsala
    • 2
  • Mohammed Ramdani
    • 1
  1. 1.Laboratoire Informatique de MohammediaFSTM Hassan II University of CasablancaMohammediaMorocco
  2. 2.Sorbonne Universités, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606ParisFrance

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