GroupRec: Group Recommendation by Numerical Characteristics of Groups in Telegram

Karimpour, Davod; Chahooki, Mohammad Ali Zare; Hashemi, Ali

doi:10.1109/iccke54056.2021.9721494

Cited by 5 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, we do not form groups based on their connections or similarity, and our experiments were conducted with real users rather than synthetic ones. [10] present a method for group recommendation in Telegram. Their method receives a set of users, analyses their groups, and recommends a list of ranked groups.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Diversification in Group Recommender Systems

Oliveira¹,

Durão²

2022

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

View full text Add to dashboard Cite

The formation of groups is an ordinary event in our routines. For example, people used to lunch, travel, or hang out in groups. Conversely, getting a consensus over an item may be difficult for some groups as the number of digital information increases. Group Recommender Systems (GRS) rise to assist in this task, as they filter which items may be more relevant to the group. Although there are consensus techniques to help in this matter, recommendations to groups can become monotonous, and this opens space for applying diversification techniques to improve recommendations. In this paper, we expose a model for recommendation to groups using diversification techniques and present the results of the online experiment where the proposal obtained an increase in precision at all levels compared with baseline.

show abstract

Section: Related Workmentioning

confidence: 99%

Evaluating Diversification in Group Recommender Systems

Oliveira¹,

Durão²

2022

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

View full text Add to dashboard Cite

show abstract

“…However, the requirement of side information degrades the performance of those methods when recommending groups to users with only interaction information. Some recent works [18,19] investigate recommending groups to users with only user-group interactions. They directly characterize the bipartite structure between users and groups, while item interaction information is ignored.…”

Section: Group Recommender Systemsmentioning

confidence: 99%

Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph

Yang¹,

Liu²,

Yang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Due to the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the ranking-based group identification (RGI) task, i.e., recommending groups to users. The major challenge in this task is how to effectively and efficiently leverage both the item interaction and group participation of users' online behaviors. Though recent developments of Graph Neural Networks (GNNs) succeed in simultaneously aggregating both social and user-item interaction, they however fail to comprehensively resolve this RGI task. In this paper, we propose a novel GNN-based framework named Contextualized Factorized Attention for Group identification (CFAG). We devise tripartite graph convolution layers to aggregate information from different types of neighborhoods among users, groups, and items. To cope with the data sparsity issue, we devise a novel propagation augmentation (PA) layer, which is based on our proposed factorized attention mechanism. PA layers efficiently learn the relatedness of non-neighbor nodes to improve the information propagation to users. Experimental results on three benchmark datasets verify the superiority of CFAG. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.

show abstract