Using a Social-Based Collaborative Filtering with Classification Techniques - Computational Intelligence and Its Applications Access content directly
Conference Papers Year : 2018

Using a Social-Based Collaborative Filtering with Classification Techniques

Abstract

In this paper, a social-based collaborative filtering model named SBCF is proposed to make personalized recommendations of friends in a social networking context. The social information is formalized and combined with the collaborative filtering algorithm. Furthermore, in order to optimize the performance of the recommendation process, two classification techniques are used: an unsupervised technique applied initially to all users using the Incremental K-means algorithm and a supervised technique applied to newly added users using the K-Nearest Neighbors algorithm (K-NN). Based on the proposed approach, a prototype of a recommender system is developed and a set of experiments has been conducted using the Yelp database.
Fichier principal
Vignette du fichier
467079_1_En_24_Chapter.pdf (157.16 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01913883 , version 1 (06-11-2018)

Licence

Attribution

Identifiers

Cite

Lamia Berkani. Using a Social-Based Collaborative Filtering with Classification Techniques. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. pp.267-278, ⟨10.1007/978-3-319-89743-1_24⟩. ⟨hal-01913883⟩
47 View
29 Download

Altmetric

Share

Gmail Facebook X LinkedIn More