The semantic optimisation of collaborative filtering systems. (2015) - Dahmani, Youcef; Benothmane, Lalia; Kharroubi, Sahraoui
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'.