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We're excited to launch Shaped Value Modeling, an interface for optimizing recommendation and search models for multiples objectives. We needed a way to combine multiple objectives—like relevance, conversions, and diversity — without constantly redeploying models or sacrificing performance. So we created a python DSL that lets you combine these objectives in real time, dynamically adjust ranking logic, and A/B test different strategies without redeploying.
Here's the core idea:
- Composable Value Models: Define your ranking logic in Python by combining different models and signals (e.g., click prediction, purchase likelihood, item quality).
- Dynamic API Control: Adjust the weighting of these signals on the fly through a simple API. Want to boost engagement for a specific user segment? Change the weighting in real time.
- Built-in Analytics: Track the impact of your changes with integrated analytics.
How it Works: 1.Train separate models for different objectives (e.g., LightGBM for click prediction, BERT4Rec for purchase likelihood). 2. Define a "value model" that combines these models using mathematical expressions and custom logic. Let's say we have a lightgbm click-through-rate model called ctr_model and a transformerd based next purchase model called purchase_model, you can create an ensembled value model as follows:
``` value_model = 0.5 * lightgbm + 0.5 * bert4rec + log(item.quality_score) ```
3. Dynamically adjust the value_model via API calls to optimize for different goals or user segments:
``` { "user_id": "83NSLX", "config": { "value_model": "0.3 * lightgbm + 0.7 * bert4rec" } } ```
Check it out in our linked demo!
