An Efficient Group Recommendation Model with Multiattention-Based Neural Networks

Document Type

Article

Publication Date

11-1-2020

Abstract

Group recommendation research has recently received much attention in a recommender system community. Currently, several deep-learning-based methods are used in group recommendation to learn preferences of groups on items and predict the next ones in which groups may be interested. However, their recommendation effectiveness is disappointing. To address this challenge, this article proposes a novel model called a multiattention-based group recommendation model (MAGRM). It well utilizes multiattention-based deep neural network structures to achieve accurate group recommendation. We train its two closely related modules: vector representation for group features and preference learning for groups on items. The former is proposed to learn to accurately represent each group's deep semantic features. It integrates four aspects of subfeatures: group co-occurrence, group description, and external and internal social features. In particular, we employ multiattention networks to learn to capture internal social features for groups. The latter employs a neural attention mechanism to depict preference interactions between each group and its members and then combines group and item features to accurately learn group preferences on items. Through extensive experiments on two real-world databases, we show that MAGRM remarkably outperforms the state-of-The-Art methods in solving a group recommendation problem.

Identifier

85086013335 (Scopus)

Publication Title

IEEE Transactions on Neural Networks and Learning Systems

External Full Text Location

https://doi.org/10.1109/TNNLS.2019.2955567

e-ISSN

21622388

ISSN

2162237X

PubMed ID

31944999

First Page

4461

Last Page

4474

Issue

11

Volume

31

Grant

17ZR1445900

Fund Ref

Natural Science Foundation of Shanghai

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