GAME : Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

3 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationSIGIR '20
Subtitle of host publicationProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherACM
Pages649-658
ISBN (Print)9781450380164
Publication statusPublished - Jul 2020

Publication series

NameSIGIR - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Title43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20)
LocationVirtual
PlaceChina
CityXi'an
Period25 - 30 July 2020

Abstract

Group recommendation aims to suggest preferred items to a group of users rather than to an individual user. Most existing methods on group recommendation directly learn the inherent interests of groups and users or inherent features of items, i.e., independently modeling the inherent embeddings of groups, users or items. However, the independent view severely suffers from the cold-start problem when making recommendations for occasional groups that are temporally formed by a set of users and have few interactions on items. Actually, the groups, users and items are interdependent because they interact with one another. The interdependencies constitute an interaction graph that provides multiple views to model the embeddings of groups, users and items from their interacting counterparts to improve recommendation for occasional groups. To this end, we propose a model, named GAME to learn the Graphical and Attentive Multi-view Embeddings (i.e., representations) for the groups, users and items from the independent view and counterpart views based on the interaction graph. In the counterpart views, the embedding of a group, user or item is aggregated from the interacting counterparts based on an attention mechanism that derives the adaptive weight for each counterpart. For instance, a user's embedding may be aggregated from her interacting items or groups. Further, GAME applies neural collaborative filtering to investigate the interactions between the multi-view embeddings of groups (or users) and items for group recommendation. Finally, we conduct extensive experiments on two real datasets. The experimental results show that GAME outperforms other state-of-the-art models, especially on both cold-start groups (i.e., occasional groups) and cold-start items.

Research Area(s)

  • Occasional group, group recommendation, interaction graph, multiview embeddings, attention mechanism

Citation Format(s)

GAME : Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation. / He, Zhixiang; Chow, Chi-Yin; Zhang, Jia-Dong.

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2020. p. 649-658 (SIGIR - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review