Exploiting Diversification in Gossip-Based Recommendation

Maximilien Servajean 1, 2 Esther Pacitti 2 Miguel Liroz-Gistau 2 Sihem Amer-Yahia 3 Amr El Abbadi 4
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
3 LIG Laboratoire d'Informatique de Grenoble - HADAS
LIG - Laboratoire d'Informatique de Grenoble
Abstract : In the context of Web 2.0, the users become massive producers of diverse data that can be stored in a large variety of systems. The fact that the users' data spaces are distributed in many different systems makes data sharing difficult. In this context of large scale distribution of users and data, a general solution to data sharing is offered by distributed search and recommendation. In particular, gossip-based approaches provide scalability, dynamicity, autonomy and decentralized control. Generally, in gossip-based search and recommendation, each user constructs a cluster of "relevant" users that will be employed in the processing of queries. However, considering only relevance introduces a sig-nificant amount of redundancy among users. As a result, when a query is submitted, as the user profiles in each user's cluster are quite similar, the probability of retrieving the same set of relevant items increases, and recall results are limited. In this paper, we propose a gossip-based search and recommendation approach that is based on a new clustering score, called usefulness, that combines relevance and diversity, and we present the corresponding new gossip-based clustering algorithm. We val-idate our proposal with an experimental evaluation using three datasets based on MovieLens, Flickr and LastFM. Compared with state of the art solutions, we obtain major gains with a three order of magnitude recall improvement when using the notion of usefulness regardless of the relevance score between two users used.
Type de document :
Communication dans un congrès
Globe'2014: 7th International Conference, Sep 2014, Munich, Germany. INRIA-SILICONVALLEY, LNCS (8648), pp.25-36, 2014, Data Management in Cloud, Grid and P2P Systems. 〈10.1007/978-3-319-10067-8_3〉
Liste complète des métadonnées

Littérature citée [15 références]  Voir  Masquer  Télécharger

https://hal-lirmm.ccsd.cnrs.fr/lirmm-01088730
Contributeur : Maximilien Servajean <>
Soumis le : vendredi 28 novembre 2014 - 15:02:20
Dernière modification le : samedi 27 janvier 2018 - 01:32:12
Document(s) archivé(s) le : vendredi 14 avril 2017 - 23:05:05

Fichier

Globe_2014.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Maximilien Servajean, Esther Pacitti, Miguel Liroz-Gistau, Sihem Amer-Yahia, Amr El Abbadi. Exploiting Diversification in Gossip-Based Recommendation. Globe'2014: 7th International Conference, Sep 2014, Munich, Germany. INRIA-SILICONVALLEY, LNCS (8648), pp.25-36, 2014, Data Management in Cloud, Grid and P2P Systems. 〈10.1007/978-3-319-10067-8_3〉. 〈lirmm-01088730〉

Partager

Métriques

Consultations de la notice

802

Téléchargements de fichiers

328