Multi-view Attention Fusion of Heterogeneous Hypergraph with Dynamic Behavioral Profiling for Personalized Learning Resource Recommendation
Authors: Tao Xie, Yan Li, Yongpan Sheng, Jian Liao
Organizations: Tao Xie, Yan Li, and Jian Liao are with the Faculty of Education, Southwest University, Chongqing, China 400715 · Yongpan Sheng is with the College of Computer and Information Science, Southwest University, Chongqing, China 400715
Hypergraph can capture complex and higher-order dependencies among learners and learning resources in personalized educational recommender systems. Many existing hypergraph-based recommendation approaches underexplored the dynamic behavioral processes inherent to learning and often oversimplified the complementary information embedded across multiple dimensions (i.e. views) within hypergraphs. These limitations compromise both the distinctiveness of learned representations and the model's generalization capabilities, especially under data-sparse conditions typical in educational settings. In this study, we propose a unified model comprising a dynamic behavioral profiling module and a multi-view attention fusion module based on heterogeneous hypergraph construction. The dynamic behavioral profiling module is designed to capture evolving behavioral processes and infer latent higher-order relations crucial for hypergraph completion; The multi-view fusion module cohesively integrates information from distinct relational views, enriching the overall data representation. The proposed model was systematically evaluated on five public benchmark datasets and one real-world, self-constructed dataset. Experimental results demonstrate that the model outperforms baseline methods across most datasets in key metrics; Furthermore, hypergraph completion based on dynamic behavioral profiling contributes significantly to performance gains, though its efficacy is modulated by dataset characteristics. Beyond offline experiments, we implemented a functional prototype system tailored for postgraduate student literature recommendation. A mixed-methods user study was conducted to assess its practical utility. Quantitative analysis revealed significantly higher perceived recommendation quality; Qualitative feedback highlighted enhanced user engagement and satisfaction with the prototype system.
Figures & tables
Figure 1: Overall framework
Dataset
#Interactions
#Vertices
#Hyperedges
Avg. Hyperedge Degree
Max. Hyperedge Degree
Min. Hyperedge Degree
Assistment2009
335,394
29,008
36,187
11.78
500
2
Assistment2017
390,331
4,399
89,642
16.51
883
2
CiteULike-t
123,896
25,586
64,735
38.04
2,490
2
MOOCCubeX
49,324
2,682
258,384
7.36
92
2
MOOPer
181,366
5,341
88,804
4.24
38
2
SelfBuild
936,640
5,399
13,165
80.65
591
2
Table 1: Statistics of the datasets.
Datasets
Metrics
Models
Ours
HGNN
HPN
HWNN
HeteHG
SAGNN
HJRL
ρ=0%
ρ=1%
ρ=5%
Assistment2009
P@10
0.3589
0.3424
0.3371
0.3607
0.3349
0.3915
0.4541
0.4514
0.4505
R@10
0.5326
0.4929
0.4707
0.5427
0.4566
0.6335
0.7935
0.7566
0.6230
NDCG@10
0.6640
0.6176
0.6357
0.6565
0.5902
0.7279
0.8722
0.8501
0.7801
MRR@10
0.5045
0.4405
0.4806
0.4846
0.4348
0.5727
0.7963
0.7736
0.6796
Assistment2017
P@10
0.8004
0.8014
0.7942
0.8018
0.8008
0.8005
0.8273
0.8313
0.8375
Table 2: Performance comparison
Figure 2: F1 scores versus K between the best baseline and ours
Figure 3: nDCG and MRR versus hypergraph complexity
Figure 4: Precision and nDCG versus K with different hypergraph completion ratio
Recommender systems are essential components of modern online platforms which presents personalized content in various domain. The traditional collaborative filtering methods depends on static user-item interaction graphs and a limited subset of similarity measures which fail to capture the changing nature of preferences of an individual. Recent graph neural network (GNN) based approaches focus on user-item bipartite graphs which do not use explicit user-user relational modelling and dynamic graph evolution during training. To address these limitations, this paper proposes a Dynamic Graph SimilarityAware Attention Graph Neural Network (DG-SA-GNN) framework that integrates dynamic user similarity graph construction with multi-similarity propagation and attention-based aggregation. The proposed architecture constructs four parallel user similarity graphs using Cosine, Jaccard, Discounted Pearson Correlation Coefficient (Discount PCC), and IPIJ similarity functions, each processed by a dedicated UserGNN module. A Graph Transformer fuses the four graph views, and a CrossAttention module refines user embeddings through interaction with item embeddings. Crucially, the graphs are reconstructed at scheduled epochs during training, enabling the model to adapt to the learned embedding space constituting the dynamic graph component. Mini-batch training with hard negative sampling improves scalability and convergence. Experiments on the MovieLens100K benchmark demonstrate that DG-SA-GNN achieves a Recall@20 of 0.162 and NDCG@20 of 0.065 which is better than the LightGCN baseline in recall. The results validate that dynamic multi-similarity graph construction coupled with attention-based fusion which produce recommendation performance
Aadarsh Senapati, Neha Kujur, Vivek Yelleti
Department of Computer Science and Engineering, SRM University Andhra Pradesh, Mangalagiri, Andhra Pradesh 522502, India
In recent years, the use of edge information provided by knowledge graphs together with the advantages of higher-order connectivity in graph neural networks for recommendation systems has become an important research direction. However, existing approaches are often limited by sparse labels, insufficient graph structure learning, and noisy entities in the knowledge graph, which reduce recommendation accuracy. To address these limitations, we propose a multi-view graph contrastive learning framework. The proposed method enhances user representations through multi-view knowledge graph distillation, enabling more accurate modeling of user preferences over entities and relations. The network aggregates neighborhood entity information to construct informative item representations. Furthermore, we design a multi-level self-supervised contrastive learning module that performs comparisons across three perspectives: Inter-Level, Intra-Level, and Interaction-Level. This design improves the model's ability to generalize across intra-class samples while increasing discrimination between inter-class samples, thereby enabling more effective multi-dimensional feature modeling. We conduct extensive experiments on three public datasets using both baseline and ablation settings. Experimental results demonstrate that the proposed framework consistently outperforms existing state-of-the-art methods. Ablation studies further verify the effectiveness of each module in the proposed model.
Zhifei Hu, Feng Xia
Durham University, Durham, United Kingdom · University of Sheffield, Sheffield, United Kingdom
Bundle recommendation ranks sets of related items rather than isolated items. Its central challenge is to connect user preferences, item interactions, and bundle composition without losing the signals needed to rank bundles. We propose Hypergraph-Enhanced Dual Convolutional Neural Network (HED), which constructs a complete hypergraph containing user--bundle, user--item, and bundle--item interactions together with intra-user and intra-bundle relations. HED couples complete-hypergraph propagation with a user--bundle branch, allowing item-aware higher-order context to inform ranking while preserving recommendation-specific signals. On NetEase, HED-128 improves over the strongest baseline by 5.04--6.97% across the six reported metrics; on Youshu, HED-64 improves by 1.87--4.56%. Ablation results support the contributions of both the user--bundle branch and intra-type relations, and sensitivity analyses identify stable operating ranges for the main hyperparameters. We further quantify the computational trade-off of the complete hypergraph, including its memory cost. The evidence supports HED on the two evaluated bundle-recommendation datasets while making its resource limitations explicit. Code and datasets will be made available upon publication.
Yang Li, Kangbo Liu, Yaoxin Wu +2
Northwestern Polytechnical University, China · Nanyang Technological University, Singapore