Abstract—The advent of collaborative information systems has scaled up the growth of the web into a huge repository of all kind of resources. The web user can share and annotate any identifiable thing, resource or item on the web. The social web has also empowered users by the tagging practice that enables a collaborative classification, folksonomy, of their shared resources. Still, the abundant web contents are mostly unorganized which make it hard for users to find and discover items of their interests. Thus, many major websites and companies' platforms use recommender systems in the user interface. Recommender systems assist users in their searching and exploring experience and provide them with relevant items matching their preferences. This article presents a folksonomy graphs based context-aware recommender system of resources. The generated graphs express the semantic relatedness between resources by effectively modelling the folksonomy relationship between user-resource-tag and integrating contextual information. The proposed approach incorporates spectral clustering to deal with the graph partitioning problem. The experimental evaluation shows relevant performances results of the Goodbooks-10k dataset for book recommendations. Future perspectives will integrate the graph theory and network analysis to improve the resources recommendation.
Index Terms—Recommender system, folksonomy, context, spectral clustering, semantic web.
The authors are with the Laboratory LISI, Department of Informatics, Faculty of Sciences Semlalia Marrakesh FSSM, Cadi Ayyad University, Morocco (e-mail: qassimi.sara@ced.uca.ma and qassimi.sara@gmail.com, abdelwahed@uca.ac.ma, meriem.hafidi@ced.uca.ma).
Cite: Sara Qassimi, El Hassan Abdelwahed, and Meriem Hafidi, "Folksonomy Graphs Based Context-Aware Recommender System Using Spectral Clustering," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 63-68, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).