Ultra Fast Warm Start Solution for Graph Recommendations
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Abstract (arXiv)
In this work, we present a fast and effective Linear approach for updating recommendations in a scalable graph-based recommender system UltraGCN. Solving this task is extremely important to maintain the relevance of the recommendations under the conditions of a large amount of new data and changing user preferences. To address this issue, we adapt the simple yet effective low-rank approximation approach to the graph-based model. Our method delivers instantaneous recommendations that are up to 30 times faster than conventional methods, with gains in recommendation quality, and demonstrates high scalability even on the large catalogue datasets.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 43 references that are in the map. First posted on arXiv 1 Sep 2025.
Builds on
18 of 43 references.
References · 18
- 2017Attention Is All You Needcited by 59,932
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
- 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
- 2021A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directionscited by 144
- 2019Embarrassingly Shallow Autoencoders for Sparse Datacited by 93
- 2021UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendationcited by 87
- 2020Exploring Data Splitting Strategies for the Evaluation of Recommendation Modelscited by 48
6 more
- 2020How to Retrain Recommender System? A Sequential Meta-Learning Methodcited by 42
- 2024Linear-Time Graph Neural Networks for Scalable Recommendationscited by 31
- 2023GraphPro: Graph Pre-training and Prompt Learning for Recommendationcited by 17
- 2024Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendationscited by 15
- 2024Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogscited by 15
- 2022Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendationcited by 4
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Citing papers per year: 2026: 2.
2 papers.