Modelling High-Order Social Relations for Item Recommendation (Extended Abstract)

Liu, Yang; Chen, Liang; He, Xiangnan; Peng, Jiaying; Zheng, Zibin; Tang, Jie

doi:10.1109/icde55515.2023.00337

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GDSRec assumes that users' ratings are not representative of their true preferences, and re-weights user-user connections based on preference similarity, which in turn learns more beneficial social relationships [38]. HOSR explores the use of GCN in social networks to capture higher-order neighborhood features of users, using attention networks to distinguish the influence of neighbors of different orders [39]. DANSER developed a dual GAT to capture dynamic and static features from the user's social network and item relationships, respectively [40].…”

Section: Graph Neural Networkmentioning

confidence: 99%

SocialCU: Integrating Commonalities and Uniqueness of Users and Items for Social Recommendation

Li,

Gan

2024

Preprint

View full text Add to dashboard Cite

Social recommendation (SR) based on Graph Neural Networks (GNN) presents a promising avenue to significantly improve user experience by leveraging historical behavior and social data, which benefits from capturing user preferences through higher-order relationships. Although two socially connected users will prefer certain specific items, their preferences in other items are likely to be inconsistent. We argue that current GNN-based social recommendation methods only focus on the commonalities of user preferences, but ignore the uniqueness. In addition, GNN also suffers from the data sparsity problem commonly observed in recommender system. To address these limitations, we propose the Integrating Commonalities and Uniqueness of users and items method, namely SocialCU, which combines GNN and contrastive learning to gain commonalities and uniqueness for SR. To be specific, we firstly model the original data as the user-item interaction graph and user-user social graph and use GNN to obtain the commonalities of nodes (users or items). Then, we design the adaptive data augmentation to build dual contrastive learning to refine the uniqueness of nodes and mitigate data sparsity by extracting supervised signals. We have conducted extensive experiments on three real-world datasets to demonstrate the performance advantages of SocialCU over current state-of-the-art recommendation methods and the rationality of the model design.

show abstract

Section: Graph Neural Networkmentioning

confidence: 99%

SocialCU: Integrating Commonalities and Uniqueness of Users and Items for Social Recommendation

Li,

Gan

2024

Preprint

View full text Add to dashboard Cite

show abstract

Novel Recommendation-Based Approach for Multidisciplinary Development of Future Universities

Jiang

et al. 2022

Sustainability

View full text Add to dashboard Cite

Multidisciplinary sustainable development is an important and complex system for comprehensive universities. Typically, a comprehensive university’s objective is to create a free, open, and diversified ecosystem of disciplines. Given finite available resources, e.g., funding or investment, configuring the formation of disciplines is critical. Understanding the interrelationships among different disciplines is challenging. Rather than directly wading through massive high-dimensional interrelated data, we judiciously formulate the cumbersome configurations of disciplines as a discipline recommendation problem. In this paper, we propose a novel data-driven approach to the configuration of disciplines based on a recommendation to predict and recommend an appropriate configuration of disciplines. The proposed approach exhibits good performance against standard metrics on real-world public data sets. It can be implemented as an attractive engine for constructing disciplines for universities.

show abstract

Modelling High-Order Social Relations for Item Recommendation (Extended Abstract)

Cited by 2 publications

References 0 publications

SocialCU: Integrating Commonalities and Uniqueness of Users and Items for Social Recommendation

SocialCU: Integrating Commonalities and Uniqueness of Users and Items for Social Recommendation

Novel Recommendation-Based Approach for Multidisciplinary Development of Future Universities

Contact Info

Product

Resources

About