How does Kumo handle the cold start problem in ML?

Several critical factors come into play when addressing cold start problem in machine learning, including new user and item dynamics (e.g., no users or limited products to observe behavior), sparse data in terms of users/items (e.g., no user interactions with a new product or marketplace), and data sparsity in the feature space (e.g., specific attributes or features exhibit limited coverage in the dataset), to name a few. These issues related to lack of sufficient historical data significantly impact the accuracy of predictions or recommendations.

Kumo mitigates the cold start problem by leveraging graph neural networks (GNNs) in its deep learning approach. By leveraging GNNs and graph-based methods like inductive representation learning, Kumo encodes entities and diverse relationships in a graph directly into embeddings through the aggregation of nearest neighbor information. This enables the models to make high-quality recommendations for new users and items for which little or no information exists.

By automatically creating graphs based on existing relationships and interactions in the relational data and generalizing user-item relationships (i.e., similar user demographics, past purchase preferences, budgetary constraints), Kumo can inherently and automatically learn new user preferences. The ability to make these generalized inferences addresses the cold-start problem and many data sensitivity problems created when a new customer joins the graph, minimizing noise and creating better customer experiences.

Learn More:

• https://kumo.ai/solutions/customer-acquisition
• https://kumo.ai/solutions/forecasting-and-anomaly-detection
• Inductive Representational Learning