The First Impression Challenge: Engaging New Users

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 Standard Approach: Patching Together Cold Start Solutions

When faced with a new or anonymous user, teams typically resort to a combination of strategies, often requiring significant manual effort and infrastructure:

Step 1: Defaulting to Global Popularity/Trending

• Method: The simplest approach is to show everyone the same list of globally best-selling, most-viewed, or trending items.
• Implementation: Requires aggregating interaction data to calculate popularity scores and serving these static lists. Tuning "trending" (balancing recency vs. popularity) adds complexity.
• The Challenge: Completely ignores any potential context about the user. Highly generic and often irrelevant to individual needs or intent.

Step 2: Building Rule-Based Segmentation

• Method: Manually define rules based on basic, easily obtainable context. For example: "If user is from Location X, show items popular in Location X," or "If user is on Mobile Device Y, show accessories for Y."
• Implementation: Requires infrastructure to capture context (GeoIP lookups, User-Agent parsing) and a rules engine to manage and apply these segments.
• The Challenge: Rules are brittle, hard to scale, require constant manual updating, and only capture very coarse-grained context. Doesn't adapt or learn.

Step 3: Leveraging Explicitly Provided Preferences (e.g., Onboarding Surveys)

• Method: Use information explicitly provided by the user, often during signup or an onboarding flow (e.g., selecting categories of interest). Match these preferences to item metadata.
• Implementation: Requires storing user preferences, maintaining accurate item metadata tagging, and building logic (often content-based filtering) to match preferences to items.
• The Challenge: Relies on users completing surveys, requires robust metadata, and the matching logic can be simplistic (basic tag matching) or complex (requiring embedding models for semantic matching). Only works for users who provide this info.

Step 4: Integrating Real-time Contextual Signals

• Method: Attempt to use real-time signals like referral source, landing page category, or limited in-session clicks (if available) to infer intent.
• Implementation: Needs systems to capture and process these signals in near real-time and feed them into the decisioning logic (often complex hybrid approaches combining rules, popularity, and basic context).
• The Challenge: Requires low-latency data pipelines and sophisticated logic to interpret sparse signals effectively. Often results in only marginal improvements over basic approaches.

Step 5: Maintaining Separate Logic Paths

• Method: Often results in completely separate code paths and potentially different systems for handling known users versus various types of cold-start users.
• Implementation: Increases system complexity, maintenance overhead, and makes A/B testing and unified analysis difficult.
• The Challenge: Architectural complexity and operational burden.

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)

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)

1 shaped create-model --file cold_start_model.yaml
2 
3 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 = [ 

1 [
2   { itemId: "ITEM_ABC" }, // Basic interaction
3   { itemId: "ITEM_XYZ", label: 1.0 } // Interaction with optional label
4 ];
5 
6 // Maybe we also know their general location
7 const userContextFeatures = {
8   country: "CA"
9 };

• 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.

1 const { Shaped } = require('@shaped/shaped');
2 const shapedClient = new Shaped();
3 const modelName = 'discovery_engine_v1';
4 const itemsLimit = 10;
5 
6 const response = await shapedClient.rank({
7   modelName: modelName,
8   interactions: sessionInteractions, // Pass the session interactions array
9   userFeatures: userContextFeatures, // Can also include features
10   limit: itemsLimit,
11   returnMetadata: true
12 });
13 
14 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.

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.