Enhanced Value Modeling

While representing user interactions as simple positive (e.g., 1) or negative (e.g., 0) events often suffices for basic recommendation scenarios, more nuanced approaches can unlock significantly greater accuracy and business value. Shaped empowers you to go beyond binary interactions with value modeling, enabling you to capture the varying degrees of positive and negative feedback inherent in user behavior.

Understanding Value Modeling

Value modeling moves beyond simple binary classifications of interactions by assigning continuous labels to events. This allows you to:

• Capture Interaction Strength: Instead of treating all positive interactions equally, you can weight them based on their perceived value. For example:
  • A purchase might be assigned a label of 5.
  • An add-to-cart event might be labeled 2.
  • A simple product view might be labeled 1.
• Differentiate Negative Feedback: Similarly, you can distinguish between various levels of negative feedback:
  • Explicitly disliking an item could be labeled -3.
  • Implicitly skipping over an item in a list might be labeled -1.

Value Modeling in Shaped: Label Conventions

To implement value modeling in Shaped, adhere to these label conventions:

• Positive Events: Assign labels greater than 0 to events reflecting positive user sentiment or engagement. The magnitude of the label (1, 2, 5, etc.) should correspond to your confidence in the positivity of the interaction.
• Negative Events: Assign labels less than 0 to events indicating negative user sentiment or a lack of engagement. The magnitude here represents your confidence in the negativity of the interaction.

Benefits of Value Modeling

Embracing value modeling in Shaped unlocks several key advantages:

• Enhanced Accuracy: By capturing the nuances of user interactions, your recommendation models can learn more accurate representations of user preferences, leading to more relevant recommendations.
• Optimized Objectives: Value modeling allows you to directly optimize for business-critical metrics. For instance, if maximizing revenue is paramount, weight purchase events more heavily during training.
• Fine-Grained Personalization: Understanding the degree of user preferences facilitates highly personalized recommendations that cater to individual tastes and levels of engagement.

Implementing Value Modeling

1. Data Preparation:

• Analyze your interaction data and define a meaningful labeling scheme based on the principles outlined above.
• Transform your raw event data to incorporate these value labels.

2. Model Creation:

• No specific configurations are needed during model creation, as Shaped automatically recognizes and handles value labels during training.

3. Evaluation and Iteration:

• Continuously monitor your model's performance using appropriate evaluation metrics that align with your business objectives.
• Iterate on your labeling scheme and model parameters as needed to optimize results.

Example Scenario

Let's say you run an e-commerce platform. Instead of just tracking purchases and clicks, you might implement this value model:

• Purchase: +5
• Add to Cart: +3
• Product View (Over 1 Minute): +1
• Product View (Under 1 Minute): 0
• Add to Wishlist: +2
• Remove from Cart: -2

This model allows Shaped to differentiate between various levels of user intent and engagement, leading to a more sophisticated understanding of user preferences.

Conclusion

Value modeling empowers you to move beyond simplistic binary interactions and unlock the full potential of your data for building highly accurate and effective recommendation systems. By carefully considering the nuances of user behavior and assigning meaningful value labels, you can train Shaped's models to deliver truly personalized and impactful recommendations.