Reweighting Network for Video Recommendation Efficiency
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Solution Overview
Problem
Existing recommendation systems for video delivery systems face inefficiencies in generating real-time recommendations due to the computational expense of complex neural networks, which are not suitable for online environments where quick and relevant recommendations are necessary.
Innovation Solution
A reweight network is introduced to characterize sequence-wise relationships of original weights, allowing the recommendation engine to generate reweight values that improve the relevance of recommendations while reducing computational complexity by focusing on the relationships between original weights rather than analyzing multi-dimensional subsidiary features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex neural networks are used to analyze sequence behavior, then recommendation quality is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent segments the complex sequence analysis task into two parts: (1) generating initial weights from subsidiary features using simple point-wise operations, and (2) refining these weights using a lightweight reweight network that processes only the generated weights rather than raw multi-dimensional features. This segmentation reduces computational complexity while maintaining recommendation quality.
Solution Approach 2:
The patent introduces an intermediary reweight network that acts as a mediator between the subsidiary features and the final recommendation. Instead of directly analyzing complex multi-dimensional subsidiary features, the system first generates intermediate weight representations and then refines them. This intermediary step simplifies the computational burden while preserving the ability to capture sequence-wise relationships.
2Reliability
If complex neural networks are used to capture sequence relationships, then recommendation relevance is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential weight relationships from the subsidiary features, separating the critical sequence-wise patterns from the redundant multi-dimensional feature data. By taking out only the necessary weight information and processing solely that, the system maintains recommendation relevance while significantly reducing processing time.
Solution Approach 2:
Instead of processing all subsidiary features exhaustively, the patent applies partial action by generating weights for only the most relevant subsidiary features and then focusing computational resources on refining these selected weights. This partial processing approach maintains recommendation quality while reducing overall processing time.
3Loss of information
If multi-dimensional subsidiary features are analyzed directly, then feature information is preserved, but computational complexity increases
Solution Approach 1:
The patent changes the parameter representation from multi-dimensional subsidiary features to one-dimensional weight values. By transforming the feature space from high-dimensional raw features to compressed weight representations, the system preserves the essential information needed for recommendations while dramatically reducing computational complexity.
Solution Approach 2:
The patent performs dimensionality reduction by projecting multi-dimensional subsidiary features into a one-dimensional weight space. This dimensional transformation maintains the critical information about feature importance while eliminating the computational burden of processing high-dimensional data, enabling efficient sequence-wise relationship modeling.
Data Source
AI summary
In some embodiments, a method receives a sequence of subsidiary features that are associated with a sequence of main features. A subsidiary feature provides subsidiary information for a main feature. A sequence of first weights for the sequence of subsidiary features is generates where a first weight in the sequence of first weights is generated based on a respective subsidiary feature. The method processes the sequence of first weights to generate a sequence of second weights. The processing uses relationships in the sequence of first weights to generate values of the second weights. The method uses the sequence of second weights to process the sequence of main features to generate an output for the sequence of main features.