Neural Graph Collaborative Filtering

Key ideas

The collaborative signal is typically not encoded in regular embeddings for users or items.
Proposal to capture bipartite graph into the embedding process.

Introduction

2 key components in learnable CF models.
- Embeddings which transform user/items into vectorised representations
- Interaction modeling which reconstructs historical interactions based on the embeddings.
Matrix Factorisation for example directly embeds user/item ID as a vector and models interactions via inner product.
Neural CF replace this inner product with non-linear NN.
Translation CF replaces inner product with distance metrics like Euclidean
Instead of expanding the interaction graph as a tree, which is complex and expensive:
- Devise an embedding propagation layer which aggregates embeddings of interacted items or users.
- Stack multiple embedding propagation layers

Methodology

Embedding layer: builds a parameter matrix as an embedding lookup table. This is the intiial state of user and item embeddings to be optimised.
Embedding Propagation Layesr