Sequence Processing Model Alignment with Recommendation Knowledge
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Solution Overview
Problem
Traditional recommender systems face challenges related to data storage requirements, user interaction data handling, and complexity, which can lead to technical difficulties and vulnerabilities.
Innovation Solution
A system and method that train a sequence processing model using auxiliary prompts to enhance its performance for recommendation tasks, aligning it with recommendation knowledge and reducing the need for extensive databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommender systems use extensive databases to store user, item, and interaction data, then recommendation accuracy is improved, but system complexity and data storage requirements increase
Solution Approach 1:
The patent extracts and stores only essential item metadata (title, description, category) in the database rather than comprehensive user-item interaction data. The sequence processing model processes recommendations locally without requiring extensive database queries, thereby reducing system complexity while maintaining recommendation accuracy through the distilled knowledge in the model weights.
Solution Approach 2:
The patent creates a copy of recommendation knowledge within the sequence processing model through training on auxiliary prompts. This knowledge copy enables the model to perform recommendations independently without relying on complex database infrastructure, effectively replacing the traditional database-dependent architecture with a model-based approach.
2Measurement precision
If traditional recommender systems store long-range user interaction data, then recommendation personalization is improved, but data privacy and security risks increase
Solution Approach 1:
The patent extracts only necessary item-level information for training the sequence processing model and stores minimal metadata in the database. The model learns user preferences and interaction patterns during training but does not store long-range user interaction data, thereby maintaining personalization capability while eliminating the privacy risks associated with storing extensive user data.
Solution Approach 2:
The sequence processing model performs recommendation tasks autonomously using its internal knowledge distilled during training. It processes user queries and generates recommendations without requiring access to stored user interaction data, enabling the system to serve itself without relying on persistent data storage that creates privacy vulnerabilities.
3Adaptability or versatility
If traditional recommender systems use multiple specialized components, then functional modularity is improved, but system reliability decreases
Solution Approach 1:
The patent merges multiple specialized components (data processing layer, recommendation engine, feedback loop) into a single sequence processing model. This unified model integrates all recommendation functions including data processing, preference learning, and recommendation generation, thereby improving system reliability by eliminating failure points in modular architecture while maintaining functional versatility through the model's multi-capability design.
Data Source
AI summary
The present disclosure provides systems and methods that align sequence processing models with recommendation knowledge. Example training systems can generate natural language prompts, which can be referred to as ‘auxiliary prompts’, that encode different types of recommendation-related knowledge, such as item attributes and user preferences. These auxiliary prompts encode into natural language format various operations and losses that can be used to impart recommendation knowledge to a sequence processing model, including item embedding, Bayesian personalized ranking (BPR), and masked item modeling.