Recommendation Feature Fusion Using Multi-Layer Cross Aggregation
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Solution Overview
Problem
Existing recommendation systems face challenges in improving the accuracy of information recommendation in an information overload environment.
Innovation Solution
Implementing a multi-layer constructor to perform layer construction and weighted aggregation processing on field features, followed by indicator prediction to obtain a recommendation indicator, enhancing the fusion of field features and improving the accuracy of recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation systems are used to determine information from a large amount of to-be-recommended information, then the system can provide recommendations, but the accuracy of information recommendation needs to be improved
Solution Approach 1:
The recommendation system is segmented into multiple processing layers including a first processing layer for initial feature extraction and a second processing layer for deep feature fusion. This segmentation allows the system to handle complex recommendation tasks with improved accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces a multi-layer processing architecture that adds depth to the feature processing pipeline. By transitioning from single-layer to multi-layer processing, the system captures both shallow and deep features, effectively adding a dimensional aspect to feature representation that improves recommendation accuracy without proportionally increasing overall system complexity.
2Measurement precision
If multi-layer constructor is used to perform layer construction and weighted aggregation processing on field features, then accuracy and diversity of aggregated features are improved, but device complexity increases
Solution Approach 1:
The multi-layer constructor is divided into distinct processing layers where each layer performs specific functions: the first processing layer handles initial feature extraction and the second processing layer performs deep feature fusion. This segmentation of the constructor into specialized sub-components improves feature aggregation accuracy while making the complexity manageable through functional decomposition.
Solution Approach 2:
The system employs dynamic weighted aggregation where weights are adaptively assigned to different features based on their relevance. This dynamic approach allows the constructor to optimize feature fusion in real-time, improving aggregation accuracy while the weights act as adjustable parameters that can be learned rather than fixed complex structures.
Data Source
AI summary
An information recommendation method, apparatus, electronic device, computer-readable storage medium, and a computer program product are provided. The method includes: obtaining a plurality of field features of a to-be-recommended task, the plurality of field features including at least one item feature of to-be-recommended information and at least one object feature of a target object; performing layer construction on the plurality of field features by using each layer constructor of a multi-layer constructor, to obtain cross features of each layer constructor; performing weighted aggregation on cross features corresponding to the multi-layer constructor, to obtain an aggregated feature of the to-be-recommended task; performing metric prediction on the aggregated feature, to obtain a recommendation metric that corresponds to the target object and that is of the to-be-recommended information; and performing a recommendation based on the recommendation metric that corresponds to the target object and that is of the to-be-recommended information.