Trust-Aware Clustering Collaborative Filtering: Identification of Relevant Items - Artificial Intelligence Applications and Innovations - Part I (AIAI 2012) Access content directly
Conference Papers Year : 2012

Trust-Aware Clustering Collaborative Filtering: Identification of Relevant Items

Cosimo Birtolo
  • Function : Author
  • PersonId : 1008025
Davide Ronca
  • Function : Author
  • PersonId : 1008026
Gianluca Aurilio
  • Function : Author
  • PersonId : 1008027

Abstract

Identifying a customer profile of interest is a challenging task for sellers. Preferences and profile features can range during the time in accordance with current trends. In this paper we investigate the application of different model-based Collaborative Filtering (CF) techniques and in particular propose a trusted approach to user-based clustering CF. We propose a Trust-aware Clustering Collaborative Filtering and we compare several approaches by means of Epinions, which contains explicit trust statements, and MovieLens dataset, where we have implicitly defined a trust information. Experimental results show an increased value of coverage of the recommendations provided by our approach without affecting recommendation quality. To conclude, we introduce a tool, based on recommender systems, able to assist merchants in delivering special offers or in discovering potential interests of their customers. This tool allows each merchant to identify the products to suggest to the target customer in order to best fit his profile of interests.
Fichier principal
Vignette du fichier
978-3-642-33409-2_39_Chapter.pdf (1.2 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01521407 , version 1 (11-05-2017)

Licence

Attribution

Identifiers

Cite

Cosimo Birtolo, Davide Ronca, Gianluca Aurilio. Trust-Aware Clustering Collaborative Filtering: Identification of Relevant Items. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.374-384, ⟨10.1007/978-3-642-33409-2_39⟩. ⟨hal-01521407⟩
96 View
82 Download

Altmetric

Share

Gmail Facebook X LinkedIn More