论文标题
有效的基于图形的推荐系统，并加权平均消息
Efficient Graph based Recommender System with Weighted Averaging of Messages
论文作者
论文摘要
我们为推荐系统问题展示了一种新颖的解决方案，在该解决方案中，我们面临着永久的软件冷启动问题。我们的系统旨在向潜在卖家推荐要求的产品，以在亚马逊商店列出。这些产品总是只有很少的互动，从而产生了永久的软件冷启动情况。现代协作过滤方法使用内容属性解决冷启动，并利用温暖起始项目的现有隐式信号。这种方法在我们的用例中失败，因为我们的整个项目集总是面对冷启动问题。我们的产品图具有超过5亿个节点和超过50亿个边缘，可以使用现代图算法进行培训和推理，非常强化。为了克服这些挑战，我们提出了一个系统，该系统可以减少数据集大小，并采用改进的建模技术来减少存储和计算而不会损失性能。特别是，我们使用过滤技术降低了图形尺寸，然后使用层（WAML）算法的加权平均消息来利用此简化的产品图。 WAML通过将计算时间减少到1/7的LightGCN和1/26的图形注意力网络（GAT），简化了对先前方法的训练，并在LightGCN和GAT上增加了2.3倍的召回$@100 $。
We showcase a novel solution to a recommendation system problem where we face a perpetual soft item cold start issue. Our system aims to recommend demanded products to prospective sellers for listing in Amazon stores. These products always have only few interactions thereby giving rise to a perpetual soft item cold start situation. Modern collaborative filtering methods solve cold start using content attributes and exploit the existing implicit signals from warm start items. This approach fails in our use-case since our entire item set faces cold start issue always. Our Product Graph has over 500 Million nodes and over 5 Billion edges which makes training and inference using modern graph algorithms very compute intensive. To overcome these challenges we propose a system which reduces the dataset size and employs an improved modelling technique to reduce storage and compute without loss in performance. Particularly, we reduce our graph size using a filtering technique and then exploit this reduced product graph using Weighted Averaging of Messages over Layers (WAML) algorithm. WAML simplifies training on large graphs and improves over previous methods by reducing compute time to 1/7 of LightGCN and 1/26 of Graph Attention Network (GAT) and increasing recall$@100$ by 66% over LightGCN and 2.3x over GAT.
