End-to-End Generative Slate Recommendation for Contextual Feeds
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Solution Overview
Problem
Existing multi-stage recommendation systems often ignore the influence of other content items in the feed and are typically trained separately, leading to suboptimal quality of recommended content items.
Innovation Solution
An end-to-end generative recommendation model trained using a multi-stage approach, combining a sequence model and a reward model, where the sequence model learns user interactions and contextual information, and the reward model fine-tunes the model using reinforcement learning to bias slate recommendations based on objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-stage recommendation systems are used to generate content feeds, then the system can process and recommend content items, but the quality of recommended content items deteriorates because stages are trained separately and individual content item scores ignore influence from other content items in the feed
Solution Approach 1:
The patent combines multiple separately trained recommendation stages into a single end-to-end trained model. The model processes content items and determines feed composition by considering the influence of other content items simultaneously, rather than processing items through separate stages that ignore contextual relationships. This unified approach resolves the contradiction by maintaining reliability through holistic processing while managing complexity through integrated architecture.
Solution Approach 2:
The model incorporates feedback mechanisms where the determination of one content item's inclusion in the feed considers the influence of other content items already selected for the feed. This iterative feedback process allows the model to adjust recommendations based on the evolving composition of the feed, improving quality by accounting for inter-item relationships that separate stages would miss.
2Productivity
If learn to rank techniques are used to generate content feeds based on individual content item scores, then the system can efficiently rank content items, but the quality deteriorates because these techniques are greedy and ignore the influence of other content items in the feed
Solution Approach 1:
The patent merges the ranking function with the feed composition determination into a single end-to-end model. Instead of using separate greedy ranking techniques that process items independently, the unified model simultaneously optimizes both ranking and feed composition, considering the influence of all content items on each other. This maintains productivity through efficient processing while improving reliability by accounting for contextual relationships.
Solution Approach 2:
The model implements feedback loops where the selection and ranking of one content item influences the ranking and selection of subsequent items in the feed. This interdependent ranking approach replaces greedy independent scoring with a system where each item's position and inclusion are determined considering the entire feed context, thereby improving quality without sacrificing efficiency.
3Ease of manufacture
If separately trained machine learning models are used in multi-stage recommendation systems, then the system can be modular and easier to train individually, but the overall quality of recommended content items deteriorates due to lack of coordinated optimization
Solution Approach 1:
The patent merges multiple separately trained models into a single end-to-end trained model where all components are optimized together in one training process. This eliminates the need for separate training stages and ensures coordinated optimization across all recommendation functions. The unified training approach improves quality by ensuring all parts of the system work together harmoniously, while the modular architecture within the unified model still allows for manageable training.
Data Source
AI summary
Described is an end-to-end generative recommendation model configured to determine a slate of content items to present to a user and the training thereof. The slate recommendation model may employ a generative model (e.g., generative transformer-based model, etc.) that is trained employing a multi-stage training approach. First, a generative model may be trained to learn a sequence model configured to generate a sequence of content items. The trained model may then be fine-tuned to better learn a distribution of slate recommendations. After fine-tuning of the model, a reward model may be trained based on one or more objectives. The reward model may be employed using a reinforcement learning technique or direct preference optimization technique to further fine-tune the model to bias the slate recommendations in view of the one or more objectives.