Deep modeling of group preferences for group-based recommendation

Liang HU, Jian CAO, Guandong XU, Longbing CAO, Zhiping GU, Wei CAO

Research output: Chapter in Book/Report/Conference proceedingChapters

88 Citations (Scopus)


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. Copyright © 2014 Association for the Advancement of Artificial Intelligence ( All rights reserved.

Original languageEnglish
Title of host publicationProceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence
Place of PublicationUSA
PublisherAAAI press
ISBN (Electronic)9781577356790
Publication statusPublished - 2014


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 Twenty-Eighth AAAI Conference on Artificial Intelligence (pp. 1861-1867). AAAI press.


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