Graph structures in recommendation systems
Graph structures have emerged as a powerful paradigm for modeling complex relationships in recommendation systems. By representing users, items, and their interactions as nodes and edges in a graph, we can capture rich, multi-dimensional information that traditional matrix-based approaches often miss.
User-item interaction graphs
The foundation of graph-based recommendation systems is the user-item interaction graph. In this structure, users and items are represented as nodes, while interactions (such as ratings, views, or purchases) form edges between them.
For a movie recommendation system, the graph might look like this:
- Nodes: Users (U1, U2, U3…) and movies (M1, M2, M3…)
- Edges: Ratings or views (for example, U1 -> M1 with a weight of 4 stars)
This simple structure already allows for a more nuanced analysis than a traditional user-item matrix. For example, we can easily identify the following...
