Hypernetwork Module for Click Rate Prediction Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional click rate prediction models are less effective when faced with a small amount of item data, leading to poor recommendation accuracy, especially for new items, due to the cold start problem, where feature interactions are difficult to perform effectively.
Innovation Solution
A method involving a hypernetwork module and a prediction module to generate adjacent matrices for feature interaction, allowing for click rate prediction even with limited data, by transforming discrete feature information into a dense space and using graph neural networks to optimize feature interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional click rate prediction models are used with limited item data, then model simplicity is maintained, but prediction accuracy deteriorates due to cold start problem
Solution Approach 1:
The patent introduces an intermediary mechanism (hypernetwork module) that generates dynamic prediction parameters based on available data patterns. This intermediary enables the system to bridge the gap between limited data and accurate predictions by generating adaptive parameters that compensate for data scarcity, thereby resolving the contradiction between prediction accuracy and data quantity
Solution Approach 2:
The patent applies preliminary action by pre-training the hypernetwork module on available data to learn data generation patterns before actual prediction. This preliminary training enables the model to establish initial prediction capabilities even with limited data, allowing it to provide accurate predictions during the cold start phase before sufficient interaction data accumulates
2Reliability
If traditional models are used for new items with minimal data, then implementation simplicity is maintained, but recommendation quality deteriorates
Solution Approach 1:
The patent segments the prediction model into two distinct modules: a hypernetwork module for generating dynamic parameters and a prediction module for actual click rate prediction. This segmentation allows each module to specialize in specific tasks, improving overall recommendation reliability while managing complexity through modular architecture that can be trained and deployed separately
Solution Approach 2:
The patent dynamically changes prediction parameters generated by thehypernetwork module based on the specific characteristics of each item and user context. This parameter adaptability enables the model to maintain high recommendation reliability for new items by adjusting parameters according to available data patterns, rather than using fixed parameters that would be suboptimal for cold start scenarios
Data Source
AI summary
A method for training a click rate prediction model includes: obtaining sample feature information and a label value, in which the sample feature information includes feature information of a sample user and feature information of a target object, and the label value is configured to indicate whether the sample user interacts with the target object; obtaining a plurality of adjacent matrixes for feature interaction by processing the feature information of the target object based on the hypernetwork module; obtaining a click rate prediction value of the sample user on the target object using the prediction module, according to the sample feature information and the plurality of adjacent matrixes; and training the click rate prediction model according to the label value and the click rate prediction value.