  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.
