Deep modeling of group preferences for group-based recommendation

92Citations
Citations of this article
81Readers
Mendeley users who have this article in their library.

Abstract

Nowadays, most recommender systems (RSs) mainly aim to suggest appropriate items for individuals. Due to the social nature of human beings, group activities have become an integral part of our daily life, thus motivating the study on group RS (GRS). However, most existing methods used by GRS make recommendations through aggregating individual ratings or individual predictive results rather than considering the collective features that govern user choices made within a group. As a result, such methods are heavily sensitive to data, hence they often fail to learn group preferences when the data are slightly inconsistent with predefined aggregation assumptions. To this end, we devise a novel GRS approach which accommodates both individual choices and group decisions in a joint model. More specifically, we propose a deep-architecture model built with collective deep belief networks and dual-wing restricted Boltzmann machines. With such a deep model, we can use high-level features, which are induced from lower-level features, to represent group preference so as to relieve the vulnerability of data. Finally, the experiments conducted on a real-world dataset prove the superiority of our deep model over other state-of-the-art methods.

Cite

CITATION STYLE

APA

Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., & Cao, W. (2014). Deep modeling of group preferences for group-based recommendation. In Proceedings of the National Conference on Artificial Intelligence (Vol. 3, pp. 1861–1867). AI Access Foundation. https://doi.org/10.1609/aaai.v28i1.9007

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free