Graph Neural Networks (GNNs) play a key role in recommender systems by modeling complex relationships between users, items, and other entities as a graph. Traditional recommendation methods often treat user-item interactions as a matrix, which struggles with sparse data and ignores higher-order connections (e.g., users connected through shared preferences or items linked via attributes). GNNs address this by explicitly representing interactions as nodes and edges in a graph, then propagating information across the graph to learn richer representations. For example, a user node can aggregate information from items they’ve interacted with, while an item node aggregates data from users who clicked or purchased it. This approach captures indirect relationships, such as identifying users with similar tastes even if they haven’t interacted with the same items directly.
A common use case is social recommendation, where GNNs combine user social networks with interaction data. If User A follows User B, a GNN can propagate User B’s preferences to User A’s embeddings, improving recommendations. Another example is session-based recommendation, where a sequence of user actions (e.g., clicks in a browsing session) is modeled as a dynamic graph. GNNs update item embeddings by analyzing how items are connected temporally or contextually within the session. Techniques like graph attention networks (GATs) further refine this by weighting connections—for instance, emphasizing recent interactions over older ones. Platforms like Pinterest and Alibaba have deployed GNN-based systems (e.g., PinSage) to handle large-scale graphs with billions of nodes, demonstrating their practical scalability.
However, GNNs also introduce challenges. Training requires efficient handling of sparse, large-scale graphs, often solved via subgraph sampling or neighborhood aggregation strategies. Real-time inference can be tricky due to dynamic updates (e.g., new user interactions), prompting solutions like incremental graph updates or lightweight retraining. Despite these hurdles, GNNs excel in scenarios where relationships matter—such as recommending items in a network of collaborators in a research paper dataset or suggesting products in an e-commerce ecosystem with overlapping categories. By leveraging graph structure, GNNs enable more accurate and explainable recommendations compared to traditional methods, especially when data is interconnected but sparse.