GRADI : Towards Group Recommendation Using Attentive Dual Top-Down and Bottom-Up Influences

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

2 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherIEEE
Pages631-636
ISBN (Electronic)978-1-7281-0858-2
Publication statusPublished - Dec 2019

Publication series

NameProceedings - IEEE International Conference on Big Data, Big Data

Conference

Title2019 IEEE International Conference on Big Data (Big Data 2019)
PlaceUnited States
CityLos Angeles
Period9 - 12 December 2019

Abstract

Most of current group recommenders only consider the bottom-up influences, i.e., the preference of a group is greatly affected by the members in the group. For example, children usually dominate the preference of a family while senior experts often lead the preference of a professional group. However, in reality there also exist the top-down influences, i.e., a group inherently affects its every member, because a group often has some distinct themes which limit the preferences of all the members in the group. For instance, the members in a group for sports may prefer hiking and rock climbing, whereas the members in a group for entertainment would like to watch movies and play games. In other words, the influences between a group and its members are dual. To this end, this paper proposes a new model for Group Recommendation using Attentive Dual Influences (GRADI) that simultaneously explores both the bottom-up and top-down influences between a group and its members. The preference of a member in a group is represented as the groupspecific member embedding by modeling the top-down influences from the group to the member. In addition, the preference of a group on a target item is represented as the item-specific group representation by considering the bottom-up influences from all the members to the group, where an attentive mechanism is developed to aggregate the preferences of all the members on a target item. Furthermore, GRADI investigates the interactions between groups and items with neural collaborative filtering. Results of extensive experiments conducted on two real-world datasets show that GRADI outperforms other state-of-the-art models.

Research Area(s)

  • attentive mechanism, group recommendation, neural collaborative filtering., Recommender systems, representation learning

Citation Format(s)

GRADI : Towards Group Recommendation Using Attentive Dual Top-Down and Bottom-Up Influences. / He, Zhixiang; Chow, Chi-Yin; Zhang, Jia-Dong; Li, Ning.

Proceedings - 2019 IEEE International Conference on Big Data. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. IEEE, 2019. p. 631-636 9005686 (Proceedings - IEEE International Conference on Big Data, Big Data).

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