Improving recommender systems with an intention-based algorithm switching strategy

Fanjuan Shi 1 Chirine Ghedira 2
2 SOC - Service Oriented Computing
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Modern e-commerce websites are equipped with hybrid recommendation systems aiming at bringing novelty and diversity to consumers. However, mobilizing several recommendation algorithms simultaneously not only incurs unnecessary computation costs, but also jeopardizes consumers' shopping experience due to excessive information load. Hence, recommending less but better (more relevant) items is critical, especially when consumers depend more and more on mobile devices, whose screen is much smaller. In this paper, we present a switching hybrid strategy capable of selecting recommendation algorithms according to consumers' instantaneous intention. Compared with a benchmark system which simultaneously uses all algorithms, our system achieved higher performance in terms of item view and consumption while sending less items, though both systems are empowered by the same recommendation algorithms. Meanwhile, the interface of our system is more concise and user-friendly. The result indicates that the intention as an important context factor can be used to enhance the performance and consumer experience of e-commerce recommender systems.
Type de document :
Communication dans un congrès
Symposium on Applied Computing (SAC 2017), Apr 2017, Marrakech, Morocco. Proceedings of the Symposium on Applied Computing, <http://www.sigapp.org/sac/sac2017/>. <10.1145/3019612.3019761>
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01538439
Contributeur : Chirine Ghedira <>
Soumis le : mardi 13 juin 2017 - 15:48:32
Dernière modification le : mercredi 14 juin 2017 - 01:07:11

Identifiants

Collections

Citation

Fanjuan Shi, Chirine Ghedira. Improving recommender systems with an intention-based algorithm switching strategy. Symposium on Applied Computing (SAC 2017), Apr 2017, Marrakech, Morocco. Proceedings of the Symposium on Applied Computing, <http://www.sigapp.org/sac/sac2017/>. <10.1145/3019612.3019761>. <hal-01538439>

Partager

Métriques

Consultations de la notice

41