Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation
Not on the map. Why?
Abstract (arXiv)
The rapid growth of the internet has made personalized recommendation systems indispensable. Graph-based sequential recommendation systems, powered by Graph Neural Networks (GNNs), effectively capture complex user-item interactions but often face challenges such as noise and static representations. In this paper, we introduce the Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation (ALDA4Rec) method, a novel model that constructs an item-item graph, filters noise through community detection, and enriches user-item interactions. Graph Convolutional Networks (GCNs) are then employed to learn short-term representations, while averaging, GRUs, and attention mechanisms are utilized to model long-term embeddings. An MLP-based adaptive weighting strategy is further incorporated to dynamically optimize long-term user preferences. Experiments conducted on four real-world datasets demonstrate that ALDA4Rec outperforms state-of-the-art baselines, delivering notable improvements in both accuracy and robustness. The source code is available at https://github.com/zahraakhlaghi/ALDA4Rec.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 52 references that are in the map. First posted on arXiv 18 Apr 2025.
Builds on
26 of 52 references.
References · 26
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
- 2018Self-Attentive Sequential Recommendationcited by 1,169
- 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
- 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
- 2019Neural Graph Collaborative Filteringcited by 660
- 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 517
- 2018Session-based Recommendation with Graph Neural Networkscited by 312
- 2021A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directionscited by 144
- 2020Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approachcited by 119
- 2021UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendationcited by 87
- 2024Transformers need glasses! Information over-squashing in language taskscited by 87
14 more
- 2021Graph Structure Learning with Variational Information Bottleneckcited by 81
- 2019Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedbackcited by 79
- 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
- 2021Dynamic Graph Neural Networks for Sequential Recommendationcited by 57
- 2022Disentangling Long and Short-Term Interests for Recommendationcited by 22
- 2024Double Correction Framework for Denoising Recommendationcited by 19
- 2023Graph Collaborative Signals Denoising and Augmentation for Recommendationcited by 16
- 2024SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendationcited by 12
- 2023Content Augmented Graph Neural Networkscited by 7
- 2024Heterophily-Aware Fair Recommendation using Graph Convolutional Networkscited by 7
- 2023Fast unfolding of communities in large networks: 15 years latercited by 6
- 2024Best of Both Worlds: Advantages of Hybrid Graph Sequence Modelscited by 6
- 2021TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregationscited by 4
- 2025Disentangling Popularity and Quality: An Edge Classification Approach for Fair Recommendationcited by 4
Built on by
Citing papers per year: 2025: 1.
1 paper.