467 articles – 705 references  [version française]

hal-00679233, version 1

Hybridising collaborative filtering and trust-aware recommender systems

Charif Haydar 1, Anne Boyer 1, Azim Roussanaly (Author to contact preferably) 1

8th International Conference on Web Information Systems and Technologies -WEBIST'2012 (2012)

Abstract: Recommender systems (RS) aim to predict items that users would appreciate, over a list of items. In evaluation of recommender systems, two issues can be defined: accuracy of prediction which implies the satisfaction of the user, coverage which implies the percentage of satisfied users. Collaborative filtering (CF) is the master approach in this domain, but still has some weaknesses especially about coverage. Trust-aware approach is today another promising approach in RS within social environments, whose prediction exceeds the quality of (CF). In this paper we propose several strategies to hybridize those both approaches in order to improve prediction quality, in the term of accuracy and coverage.

  • 1:  KIWI (LORIA)
  • INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
  • Domain : Computer Science/Artificial Intelligence
    Computer Science/Information Retrieval
    Computer Science/Web
  • Keywords : Recommender systems – Trust – Reputation – Users similarity
 
  • hal-00679233, version 1
  • http://hal.inria.fr/hal-00679233
  • oai:hal.inria.fr:hal-00679233
  • From: Azim Roussanaly <>
  • Submitted on: Thursday, 15 March 2012 10:52:56
  • Updated on: Friday, 16 March 2012 10:28:34