JUCS - Journal of Universal Computer Science 16(16): 2179-2195, doi: 10.3217/jucs-016-16-2179
The 3A Personalized, Contextual and Relation-based Recommender System
expand article infoSandy El Helou‡, Christophe Salzmann‡, Denis Gillet‡
‡ École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

Corresponding author: Sandy Helou ( sandy.elhelou@epfl.ch )

© Sandy Helou, Christophe Salzmann, Denis Gillet.
This article is freely available under the J.UCS Open Content License.
Citation: Helou SE, Salzmann C, Gillet D (2010) The 3A Personalized, Contextual and Relation-based Recommender System. JUCS - Journal of Universal Computer Science 16(16): 2179-2195. https://doi.org/10.3217/jucs-016-16-2179
Open Access
Abstract
This paper discusses the 3A recommender system that targets CSCL (computer-supported collaborative learning) and CSCW (computer-supported collaborative work) environments. The proposed system models user interactions in a heterogeneous graph. Then, it applies a personalized, contextual, and multi-relational ranking algorithm to simultaneously rank actors, activity spaces, and assets. The results of an empirical evaluation carried out on an Epinions dataset indicate that the proposed recommendation approach exploiting the trust and authorship networks performs better than user-based collaborative filtering in terms of recall.
Keywords
Recommender systems, CSCW, CSCL, trust, algorithms, design, pagerank