INFORMATION CHANGE THE WORLD

International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.9, No.2, Feb. 2017

Towards Semantics-Aware Recommender System: A LOD-Based Approach

Full Text (PDF, 540KB), PP.55-61


Views:70   Downloads:1

Author(s)

Asmaa Fridi, Sidi Mohamed Benslimane

Index Terms

Recommender system;Collaborative filtering;Content-based filtering;Linked Open Data;Clustering

Abstract

Recommender systems have contributed to the success of personalized websites as they can automatically and efficiently select items or services adapted to the user's interest from huge datasets. However, these systems suffer of issues related to small number of evaluations; cold start system and data sparsity. Several approaches have been explored to find solutions to related issues. The advent of the Linked Open Data (LOD) initiative has spawned a wide range of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper, we aim to demonstrate that adding semantic information from LOD enhance the effectiveness of traditional collaborative filtering. To evaluate the accuracy of the semantic approach, experiments on standard benchmark dataset was conducted. The obtained results indicate that the accuracy and quality of the recommendation are improved compared with existing approaches.

Cite This Paper

Asmaa Fridi, Sidi Mohamed Benslimane,"Towards Semantics-Aware Recommender System: A LOD-Based Approach", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.2, pp.55-61, 2017.DOI: 10.5815/ijmecs.2017.02.07

Reference

[1]N. Abderrahim, S.M. Benslimane, 2015. Towards Improving Recommender System: A Social Trust-aware Approach. International Journal of Modern Education and Computer Science (IJMECS), 7(2):8-15. 

[2]R. Burke, 2007. Hybrid web recommender systems. In P. Brusilovsky, A. Kobsa, and W. Nejdl, editors, The Adaptive Web: Methods and Strategies of Web Personalization, volume 4321 of Lecture Notes in Computer Science. Springer-Verlag, Berlin Heidelberg New York.

[3]Tommaso Di Noia, Vito Claudio Ostuni, 2015. Recommender Systems and Linked Open Data. Reasoning Web. Web Logic Rules. LNCS 9203, pp 88-113.

[4]Benjamin Heitmann and Conor Hayes, 2010. Using Linked Data to Build Open, Collaborative Recommender Systems, In AAAI Spring Symposium: Linked Data Meets Artificial Intelligence. 

[5]Rui Yang, Wei Hu, and Yuzhong Qu, 2012. Using Semantic Technology to Improve Recommender Systems Based on Slope One, CSWS.

[6]Han-Gyu Ko, Eunae Kim and In-Young Ko, 2014. Semantically based Recommendation by using Semantic Clusters of Users' Viewing History.

[7]Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B, 2010. Music Recommendations Using DBpedia. In ISWC'10. 

[8]Lasek, I., 2011. Dc proposal: Model for news filtering with named entities. In The Semantic Web – ISWC 2011, LNCS, vol. 7032, pp. 309–316. 

[9]Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, Davide Romito and Markus Zanker, 2012. Linked Open Data to support Content-based Recommender Systems. In the 8th International Conference on Semantic Systems, Graz, Austria.

[10]Fattane Zarrinkalam, Mohsen Kahani, 2012. A Multi-Criteria Hybrid Citation Recommendation System Based on Linked Data, In Computer and Knowledge Engineering (ICCKE).

[11]Vito Claudio Ostuni, Tommaso Di Noia, Eugenio Di Sciascio, Roberto Mirizzi, 2013. Top-N Recommendations from Implicit Feedback leveraging Linked Open Data, In the 7th ACM conference on Recommender Systems, RecSsys.

[12]Ladislav Peska and Peter Vojtas, 2013. Enhancing Recommender System with Linked Open Data.In the International Conference on Flexible Query Answering Systems, Granada, Spain.

[13]Rouzbeh Meymandpour and Joseph G. Davis, 2015. Enhancing Recommender Systems Using Linked Open Databased Semantic Analysis of Items. In the Proceedings of the 3rd Australasian Web Conference (AWC 2015), Sydney, Australia.

[14]M. F. Alhamid, M. Rawashdeh, H. Al Osman, M. Shamim Hossain, A. El Saddik, 2014. Towards context-sensitive collaborative media recommender system, International Journal of Multimedia Tools and Applications, 74(24):11399–11428.

[15]Auer, S., C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, Z. Ives (2007). DBpedia: A Nucleus for a Web of Open Data. The Semantic Web, Lecture Notes in Computer Science 4825, 722–735.

[16]Naziha Abderrahim, Sidi Mohamed Benslimane. Towards Improving Recommender System: A Social Trust-aware Approach. International Journal of Modern Education and Computer Science (IJMECS), Vol.7, No. 2, January 2015.

[17]MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, vol. 1, pp. 281–297. University of California Press, California (1967)

[18]Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Communications of the ACM, 18(11):613–620. 1975.