What strategies exist for mitigating the cold start problem?

The cold start problem occurs when a system lacks sufficient data to make accurate predictions or recommendations for new users or items. To address this, developers can use a mix of data-driven and rule-based strategies. The key is to balance default behaviors with incremental learning as data becomes available. Here are three practical approaches.

First, leverage hybrid models that combine collaborative filtering with content-based filtering. Collaborative filtering relies on user-item interactions, which new users or items lack. Content-based filtering uses attributes like user demographics, item descriptions, or categories to make initial predictions. For example, a music app could recommend songs based on a new user’s selected genres during sign-up or use a song’s metadata (e.g., genre, artist) to suggest it to users who like similar content. Hybrid models can also incorporate popularity signals (e.g., trending items) as fallbacks. This approach provides immediate, reasonable outputs while the system gathers personalized data.

Second, prompt users for explicit feedback early. Asking new users to rate a few items or select preferences during onboarding provides initial data to bootstrap recommendations. For instance, a streaming service might show a list of popular movies and ask users to rate five titles. This data can seed collaborative filtering models or adjust content-based recommendations. Similarly, for new items, encourage early adopters to engage by highlighting them in “recently added” sections or through targeted promotions. This generates initial interaction data faster.

Third, use transfer learning or pretrained embeddings. Pretrained models on related datasets can infer patterns for new users or items. For example, an e-commerce platform could train an embedding model on existing product data (e.g., descriptions, purchase history) and use it to represent new products based on their attributes. For users, embeddings from similar profiles (e.g., location, sign-up source) can provide initial recommendations. Additionally, progressive learning—starting with simple heuristics (e.g., most popular items) and transitioning to complex models as data accumulates—ensures the system remains functional at every stage. This avoids over-reliance on incomplete data early on.