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The Cold Start User Problem

Imagine walking into a store for the first time. A helpful assistant might ask what you're looking for or observe your general direction to offer relevant suggestions. Now imagine walking into a digital equivalent – an e-commerce site, streaming service, or content platform – and being met with a wall of generic, popular items that have little bearing on your actual interests. This is the "cold start user" problem: how do you provide relevant and engaging experiences for users you know nothing, or very little, about?

Solving this is critical. The initial experience heavily influences whether a new visitor stays, engages, converts, or bounces. Yet, traditional personalization systems, heavily reliant on past user interaction history, often stumble here. Showing purely random or globally popular items is a missed opportunity to demonstrate value immediately. Building a system that intelligently handles cold starts, using whatever limited signals are available, has traditionally been a complex engineering feat.

The Shaped Approach: Intelligent Cold Start Handling Built-In​

Shaped is designed to handle the cold start user problem gracefully and intelligently, leveraging multiple strategies within its unified platform, often requiring significantly less custom engineering.

How Shaped Streamlines Cold Start Personalization:

  1. Intelligent Baselines: When absolutely no user information is available, Shaped's rank endpoint defaults to sophisticated popularity and trending models automatically tuned based on your data, providing a much stronger baseline than simple global popularity.
  2. Leveraging Real-time Context via Connectors: Shaped integrates seamlessly with real-time data sources (Segment, Amplitude, Kafka, etc.) via Connectors. Information gathered during onboarding or initial session context (location, device) can be ingested rapidly (sub-30 seconds) and used by the model.
  3. Direct Context Injection via API (user_features): For immediate context not yet flowing through connectors, or for purely anonymous sessions, you can directly pass user attributes (stated interests, location, device type, segment membership) into the rank API call using the user_features parameter.
  4. Direct In-Session Behavior Injection via API (interactions): Crucially, you can also provide a list of recent interactions (e.g., items clicked or viewed in the current session) directly within the rank API call itself using the interactions parameter. This allows Shaped to react instantly to what even an anonymous user is doing right now.
  5. Sophisticated Model Learning: Shaped's models learn complex relationships during training:
    • Attribute-Item Relationships: Correlations between user/context features and relevant items.
    • User Similarity: Relationships between users based on shared attributes and behavior.
    • Sequence Understanding: How sequences of interactions predict future interests. When user_features or interactions are provided for a cold-start user, Shaped uses these learned patterns and the immediate context to infer preferences.
  6. Unified Ranking Logic: The same rank endpoint handles known users (user_id), cold-start users providing context (user_features), users interacting in-session (interactions), or completely anonymous users (baselines), simplifying your application logic.

Handling Cold Start Users with Shaped​

Let's illustrate providing recommendations for a new or anonymous user who has just interacted with a couple of items during their current session.

Goal: Show relevant items to a user (potentially anonymous) based on items they just clicked ('ITEM_ABC', 'ITEM_XYZ') in this session, possibly combined with known context like location.

1. Ensure Data is Connected:

2. Define Your Shaped Model (YAML):

cold_start_model.yaml
model:
  name: discovery_engine_v1
connectors:
  - type: Dataset
    name: user_metadata
    id: users
  - type: Dataset
    name: item_metadata
    id: items
  - type: Dataset
    name: user_interactions
    id: interactions
fetch:
  users: |
    SELECT 
      user_id, 
      country, 
      device_type
    FROM users
  items: |
    SELECT
      item_id,
      title,
      description,
      category,
      tags
      brand,
      image_url,
      product_url,
      publish_date
    FROM items
  events: |
    SELECT 
      user_id, 
      item_id, 
      timestamp AS created_at, 
      event_type 
    FROM interactions

3. Create the Model & Monitor Training: (Same as before)

shaped create-model --file cold_start_model.yaml

shaped view-model --model-name discovery_engine_v1 # Wait for ACTIVE

4. Fetch Personalized Ranking Using In-Session Interactions (Application Logic): When the user navigates to a page where you want to show recommendations reflecting their immediate past actions in this session:

  • Step A (Your Frontend/Backend): Track the items the user has interacted with in this session. Gather any other available context. 
const sessionInteractions = [  
    { itemId: "ITEM_ABC" }, // Basic interaction  
    { itemId: "ITEM_XYZ", label: 1.0 } // Interaction with optional label  
];  

// Maybe we also know their general location  
const userContextFeatures = {  
    country: "CA"  
};
  • Step B (Your Backend): Call Shaped's rank API. Provide the interactions list. You can also provide user_features simultaneously if available. Omit user_id if the user is anonymous or their ID isn't relevant/available yet. 
const { Shaped } = require('@shaped/shaped');  

const shapedClient = new Shaped();  
const modelName = 'discovery_engine_v1';  
const itemsLimit = 10;  
const response = await shapedClient.rank({  
    modelName: modelName,  
    interactions: sessionInteractions, // Pass the session interactions array  
    userFeatures: userContextFeatures, // Can also include features  
    limit: itemsLimit,  
    returnMetadata: true  
});  
console.log(`Got ${response.ids.length} recommendations influenced by session clicks.`);
  • Step C (Your Backend): The response contains items ranked by Shaped, taking into account the very recent interactions provided, blended intelligently with any user_features and the model's baseline understanding. Display these highly relevant, up-to-the-moment recommendations.

5. What if NO Context is Available? (Same as before) If you call rank with no user_id, user_features, or interactions, Shaped falls back to its intelligent baseline recommendations (e.g. trending, chronological).

Conclusion: Make Every First Impression Count​

The cold start user problem is a significant hurdle in delivering consistently relevant digital experiences. Traditional methods often involve generic fallbacks, brittle rules, or complex, piecemeal engineering.

Shaped provides a cohesive and intelligent solution built into its core platform. By leveraging sophisticated baselines, integrating real-time context via Connectors, allowing direct feature injection (user_features), and enabling immediate reaction to in-session behavior via API-provided interactions, Shaped ensures that even brand new or anonymous users receive relevant recommendations. Its models intelligently combine historical patterns, user similarities, attribute relationships, and immediate context to deliver relevance from the very first interaction. This unified approach simplifies development and helps you make a positive, adaptive first impression, every time.

Ready to turn anonymous visitors into engaged users?

Request a demo of Shaped today to see how it handles cold starts for your specific use case. Or, start exploring immediately with our free trial sandbox.