提出快速更新图推荐系统的新方法，30倍提速且保持推荐质量。
Ultra Fast Warm Start Solution for Graph Recommendations
- 采用低秩近似技术加速图模型的推荐更新。
- 在大规模数据集上实现30倍速度提升，推荐质量不下降。
- 适合需要实时更新推荐结果的电商、内容平台。
本文提出一种高效快速的线性方法，用于在可扩展的图推荐系统UltraGCN中更新推荐结果。在大量新数据和用户偏好变化的背景下，该任务对维持推荐相关性至关重要。为此，我们将简单而有效的低秩近似方法适配到图模型中。所提方法实现了即时推荐，速度比传统方法快达30倍，同时提升了推荐质量，并在大规模目录数据集上表现出优异的可扩展性。
原文摘要 · Abstract (English)
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.
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