Unifying multi-associations through hypergraph for bundle recommendation

Article Computer Science, Artificial Intelligence

Dynamic network embedding survey

Guotong Xue et al.

Summary: With the increasing evolution of real world networks, such as social networks and user-item networks, dynamic network embedding has gained significant research attention in recent years. This paper provides a survey on the data models, representation learning techniques, evaluation, and applications of dynamic network embedding, and identifies common patterns from current related works.

NEUROCOMPUTING (2022)

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Article Computer Science, Artificial Intelligence

Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing

Xiangyu Song et al.

Summary: The goal of Knowledge Tracing is to estimate students' mastery of a concept based on their learning history. With the rise of deep learning, Deep Knowledge Tracing has shown success in organizing students' learning plans.

KNOWLEDGE-BASED SYSTEMS (2022)

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Article Computer Science, Artificial Intelligence

On Both Cold-Start and Long-Tail Recommendation with Social Data

Jingjing Li et al.

Summary: The paper discusses the importance of hits in web systems and the critical role of recommender systems in discovering and displaying interesting items to users. The authors propose a novel approach that addresses both cold-start and long-tail recommendation, tackling the challenges of new users and surprising users.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

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Article Computer Science, Information Systems

JKT: A joint graph convolutional network based Deep Knowledge Tracing

Xiangyu Song et al.

Summary: Knowledge Tracing (KT) aims to track students' mastery of knowledge based on their historical learning interactions. To address the shortcomings of existing methods, a deep Knowledge Tracing framework called JKT is proposed, which utilizes a Joint graph convolutional network to model multi-dimensional relationships and improve interpretability.

INFORMATION SCIENCES (2021)

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Article Computer Science, Artificial Intelligence

Dual-view hypergraph neural networks for attributed graph learning

Longcan Wu et al.

Summary: The text introduces the importance of graph embedding in network data analysis and the proposed Dual-view HyperGraph Neural Network (DHGNN) model for attributed graph learning. The model unifies the expression form of different information sources, constructs dual hypergraphs, and utilizes the attention mechanism to achieve more effective graph embedding. Extensive experiments demonstrate that the performance of DHGNN surpasses state-of-the-art graph embedding methods.

KNOWLEDGE-BASED SYSTEMS (2021)

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Article Mathematics, Interdisciplinary Applications