Article: The semantic optimisation of collaborative filtering systems Journal: International Journal of Metadata, Semantics and Ontologies (IJMSO) 2015 Vol.10 No.2 pp.84 - 91 Abstract: The information filtering is the process for routing a continuous flow of relevant information towards groups of people, without having to explicitly express what they want. This process provides ranked lists of recommendation of items to be of use to a user. This system collects from users their ratings, evaluations or preferences for certain items. We distinguish different classes of recommendation approaches, in this work we focus on collaborative filtering. The development of such a system suffers from problems, such as the initial construction phase 'cold start', the scalability and data sparsity. In this paper, we combine the ontology 'item' of semantic web with the memory-based algorithm used in information filtering to reduce some drawbacks of collaborative filtering. We enrich the system by a new term which is 'representative' to fix the problem of funnel effect. We propose a new function which calculates the similarity between users in case of lack of items' evaluations and furthermore, we suggest a new algorithm to reduce the problem of sparse matrix 'item/users'. Inderscience Publishers - linking academia, business and industry through research

Title: The semantic optimisation of collaborative filtering systems

Authors: Youcef Dahmani; Lalia Benothmane; Sahraoui Kharroubi

Addresses: Laboratory Energy Engineering and Computer Engineering, University Ibn Khaldoun Tiaret, Tiaret, Algeria ' High National School of Computer Science, ESI, Algiers, Algeria ' Laboratory Energy Engineering and Computer Engineering, University Ibn Khaldoun Tiaret, Tiaret, Algeria

Abstract: The information filtering is the process for routing a continuous flow of relevant information towards groups of people, without having to explicitly express what they want. This process provides ranked lists of recommendation of items to be of use to a user. This system collects from users their ratings, evaluations or preferences for certain items. We distinguish different classes of recommendation approaches, in this work we focus on collaborative filtering. The development of such a system suffers from problems, such as the initial construction phase 'cold start', the scalability and data sparsity. In this paper, we combine the ontology 'item' of semantic web with the memory-based algorithm used in information filtering to reduce some drawbacks of collaborative filtering. We enrich the system by a new term which is 'representative' to fix the problem of funnel effect. We propose a new function which calculates the similarity between users in case of lack of items' evaluations and furthermore, we suggest a new algorithm to reduce the problem of sparse matrix 'item/users'.

Keywords: collaborative filtering; domain ontology; user evaluations; user preferences; ranked recommendations; representative; semantic optimisation; semantic web; memory-based algorithms; information filtering; funnel effect; data sparsity.

DOI: 10.1504/IJMSO.2015.070823

International Journal of Metadata, Semantics and Ontologies, 2015 Vol.10 No.2, pp.84 - 91

Received: 22 Jan 2014
Accepted: 19 Oct 2014
Published online: 28 Jul 2015 *

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Title: The semantic optimisation of collaborative filtering systems

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