[en] Collaborative filtering (CF) is a widely applied method to perform recommendation tasks in a wide range of domains and applications. Dictionary learning (DL) models, which are highly important in CF-based recommender systems (RSs), are well represented by rating matrices. However, these methods alone do not resolve the cold start and data sparsity issues in RSs. We observed a significant improvement in rating results by adding trust information on the social network. For that purpose, we proposed a new dictionary learning technique based on trust information, called TrustDL, where the social network data were employed in the process of recommendation based on structural details on the trusted network. TrustDL sought to integrate the sources of information, including trust statements and ratings, into the recommendation model to mitigate both problems of cold start and data sparsity. It conducted dictionary learning and trust embedding simultaneously to predict unknown rating values. In this paper, the dictionary learning technique was integrated into rating learning, along with the trust consistency regularization term designed to offer a more accurate understanding of the feature representation. Moreover, partially identical trust embedding was developed, where users with similar rating sets could cluster together, and those with similar rating sets could be represented collaboratively. The proposed strategy appears significantly beneficial based on experiments conducted on four frequently used datasets: Epinions, Ciao, FilmTrust, and Flixster.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
KHALEDIAN, Navid ✱; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CritiX ; Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
Nazari, Amin ; Department of Computer Engineering, Bu-Ali Sina University, Hamedan, Iran
Khamforoosh, Keyhan ; Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
Abualigah, Laith ; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan
Javaheri, Danial ; Department of Computer Engineering, Chosun University, Gwangju, South Korea
✱ Ces auteurs ont contribué de façon équivalente à la publication.
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks
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