Graph Convolutional Network (GCN) is a state-of-the-art method used for recommendation.
Throughout this paper we study the effects of modifying the methods used for layer combination in GCN.
An ablation study for GCF is conducted to understand why it outperformed LightGCN on their datasets.
We focus on LightGCN which is a simplified implementation of the Neural Graph Collaborative Filtering (NGCF).
LightGCN outperformed NGCF and other state-of-the-art methods.
We propose two new extensions for LightGCN called Aggressive Layer Combination (ALC) and Balanced Layer Combination (BLC) instead of LightGCN's version of weighted summation for layer combination.
This showed better results on most datasets compared to both GCF and LightGCN.
We also show that in certain cases only utilizing the embedding from a single layer showed to outperform ALC, BLC, GCF and LightGCN.