LLM Resource Recommendation for Sparse User Behavior
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Solution Overview
Problem
Conventional resource recommendation models struggle in scenarios with limited resource information and sparse user behaviors, leading to inaccurate recommendations.
Innovation Solution
Utilizing a large language model trained through machine learning to process resource prompt information derived from positive user behaviors, generating resource texts describing preferences, and determining correlations to recommend resources accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation models are used, then system complexity is low, but recommendation accuracy deteriorates in scenarios with sparse user behaviors and limited resource information
Solution Approach 1:
The patent introduces an intermediary component (resource text generation module using large language model) that transforms sparse user behavior data into enriched resource representations. This intermediary bridges the gap between limited input data and accurate recommendations, allowing the system to achieve high recommendation accuracy without requiring complex multi-stage architectures.
Solution Approach 2:
The patent changes the parameter representation from traditional sparse feature vectors to dense natural language descriptions generated by large language models. This parameter transformation enables the system to capture nuanced user preferences and resource characteristics, significantly improving recommendation accuracy while maintaining computational efficiency through unified model processing.
2Reliability
If traditional recommendation approaches are used, then computational resources are conserved, but the ability to handle sparse data scenarios deteriorates
Solution Approach 1:
The patent employs a universal large language model that can process diverse input types (user behaviors, resource attributes, contextual information) through a single unified architecture. This multi-functional approach improves reliability in sparse scenarios by leveraging the model's generalization capability, while avoiding the need for multiple specialized models that would consume more computational resources.
3Manufacturing precision
If conventional models are used, then processing speed is maintained, but recommendation quality deteriorates when resource information is limited
Solution Approach 1:
The patent performs preliminary action by pre-processing user behavior data into structured resource prompt information before the main recommendation inference. This preliminary structuring of sparse data into meaningful representations enables the large language model to process information more efficiently, achieving high recommendation quality without excessive processing time during the actual recommendation generation phase.
Data Source
AI summary
A resource recommendation method includes constructing resource prompt information based on positive behavior information of a target object for a resource, the positive behavior information representing a positive behavior of the target object for a resource preference, and the resource prompt information representing a resource preferred by the target object; processing the resource prompt information by using a large language model, to obtain a resource text, the resource text being configured for describing the resource preference of the target object in a form of a natural language; determining, for any candidate resource in a resource library for recommendations, a correlation between the candidate resource and the resource text, the correlation representing a correlation between the resource preference of the target object and the candidate resource; and recommending a resource to the target object based on correlations corresponding to multiple candidate resources in the resource library.