Recommending Groups to Users Using User-Group Engagement and Time-Dependent Matrix Factorization

Wang, Xin; Donaldson, Roger; Nell, Christopher; Gorniak, Peter; Ester, Martin; Bu, Jiajun

doi:10.1609/aaai.v30i1.10160

Cited by 26 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, semantic information from descriptions of groups [3] and visual information from photos shared by users [28] can be incorporated with a collaborative filtering framework to perform personalized group recommendations. User behaviors in different time periods [30,34], such as joining groups, can also be leveraged for recommending groups to users. However, the requirement of side information degrades the performance of those methods when recommending groups to users with only interaction information.…”

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

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

show abstract

“…There are multiple studies for core number computation under different settings including a linear-time in-memory algorithm (Batagelj and Zaversnik 2003), I/O efficient algorithms (Wen et al 2016;Cheng et al 2011), locally computing and estimating (Cui et al 2014) and core number maintenance on dynamic graphs (Aksu et al 2014;Zhang et al 2016). The engagement dynamic in social networks has attracted significant focus, e.g., (Wang et al 2016;Chwe 2000;Bhawalkar et al 2015;Malliaros and Vazirgiannis 2013;Wu et al 2013;Chitnis, Fomin, and Golovach 2013;. The k-core becomes more and more popular in social studies, because its degeneration property can be used to quantify engagement dynamics in real social networks (Malliaros and Vazirgiannis 2013).…”

Section: Efficiencymentioning

confidence: 99%

“…The user engagement on social network has attracted significant interests over recent years (Wang et al 2016;Wu et al 2013;Bhawalkar et al 2015). k-core is a simple and popular model based on degree constraint, which has been widely used to measure the network engagement (Malliaros and Vazirgiannis 2013;Chitnis, Fomin, and Golovach 2013;Abello and Queyroi 2013;Garcia, Mavrodiev, and Schweitzer 2013).…”

Section: Introductionmentioning

confidence: 99%

Finding Critical Users for Social Network Engagement: The Collapsed k-Core Problem

Zhang

et al. 2017

AAAI

View full text Add to dashboard Cite

In social networks, the leave of critical users may significantly break network engagement, i.e., lead a large number of other users to drop out. A popular model to measure social network engagement is k-core, the maximal induced subgraph in which every vertex has at least k neighbors. To identify critical users for social network engagement, we propose the collapsed k-core problem: given a graph G, a positive integer k and a budget b, we aim to find b vertices in G such that the deletion of the b vertices leads to the smallest k-core. We prove the problem is NP-hard. Then, an efficient algorithm is proposed, which significantly reduces the number of candidate vertices to speed up the computation. Our comprehensive experiments on 9 real-life social networks demonstrate the effectiveness and efficiency of our proposed method.

show abstract

Temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation

Meng

Zhang

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

Summary With the explosive growth of cloud services, how to design effective recommendation models has become more and more important. Temporal information has been proved to be an important factor affecting recommendation performance. Actually, both of user behaviors and QoS performance of services are time‐sensitive, especially in dynamic cloud environment. However, most existing collaborative recommendation methods seldom consider temporal influence to QoS performance. Furthermore, with the ever‐increasing number of security threats in clouds, privacy preservation becomes an important problem to be addressed in recommender systems. Based on these observations, in this paper, we propose a temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation, where tensor factorization‐based CF model is integrated into neighborhood‐based CF model to achieve improved recommendation performance. Specifically, temporal influence is considered into recommendation model by distinguishing short‐term QoS metrics from long‐term QoS metrics. A privacy‐aware time aggregation mechanism is adopted to preserve the sensitive time information of users. Moreover, to deal with the sparsity problem, a sparsity‐oriented data QoS rating prediction mechanism based on tensor factorization technique is applied to predict the missing or unrated QoS rating data. Finally, predictions are made based on both stable and temporal nearest neighbors. Experiments are conducted to demonstrate the prediction performance of our proposal.

show abstract

Recommending Groups to Users Using User-Group Engagement and Time-Dependent Matrix Factorization

Cited by 26 publications

References 25 publications

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

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

Finding Critical Users for Social Network Engagement: The Collapsed k-Core Problem

Temporal‐aware and sparsity‐tolerant hybrid collaborative recommendation method with privacy preservation

Contact Info

Product

Resources

About