Object-to-Object Recommendation With Label Prototype Attention
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Solution Overview
Problem
Existing extreme classification methods for product-to-product recommendations face challenges due to insufficient label-text descriptions, especially in short-text scenarios, leading to distorted encoder training and computational inefficiencies, and lack of incorporating relevant document information for improved label representations.
Innovation Solution
The DEXA architecture enhances encoder training by using label prototypes and a self-attention module to aggregate document information, optimizing similarity in the semantic space through a modified loss function, and employing estimated label prototypes to reduce computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing extreme classification methods are used for product-to-product recommendations, then the system can process recommendations, but the label-text descriptions are insufficient especially in short-text scenarios leading to distorted encoder training
Solution Approach 1:
The patent introduces label prototypes as intermediary representations that bridge the gap between insufficient label-text descriptions and the encoder training process. These prototypes serve as mediators that capture essential semantic information from multiple sources (label text, auxiliary parameters, and document information) to provide richer training signals without requiring improved input text quality.
Solution Approach 2:
The system performs preliminary actions by pre-computing label prototypes from auxiliary parameters and document information before the main encoder training process. This preliminary computation creates enriched label representations that can guide the encoder training more effectively, compensating for the insufficient quality of original label-text descriptions.
2Productivity
If existing extreme classification methods are used, then recommendations can be generated, but computational inefficiencies occur
Solution Approach 1:
The patent applies preliminary action by computing label prototypes in advance using auxiliary parameters and document information before the main recommendation generation process. This pre-computation step creates reusable label representations that reduce the computational burden during actual recommendation queries, improving efficiency while reducing energy consumption.
Solution Approach 2:
The system implements self-service by having the label prototypes serve dual purposes: they are computed from auxiliary parameters and document information, then reused multiple times during encoder training and recommendation generation. This self-reusable mechanism reduces redundant computations and optimizes resource utilization.
3Reliability
If existing extreme classification methods are used, then recommendations can be generated, but relevant document information is not incorporated for improved label representations
Solution Approach 1:
The patent merges multiple information sources (label text, auxiliary parameters, and document information) into unified label prototypes. This combination integrates diverse data types and sources to create comprehensive label representations that improve reliability, while the merging process itself manages the complexity by creating a cohesive integrated structure.
Solution Approach 2:
The label prototypes serve multiple functions: they are computed from auxiliary parameters, enriched with document information, used for encoder training, and applied during recommendation generation. This multi-functionality increases reliability by utilizing the same robust representations across different system components, while reducing overall complexity by avoiding separate processing paths.
4Measurement precision
If label prototypes and self-attention module are used to aggregate document information, then semantic similarity is optimized, but computational overhead increases
Solution Approach 1:
The system performs preliminary computation of label prototypes using auxiliary parameters and document information before the self-attention module processes them. This preliminary preparation reduces the complexity of the subsequent self-attention computation by providing pre-processed, consolidated inputs, thereby optimizing semantic similarity measurement while managing computational overhead.
Solution Approach 2:
The label prototypes serve as intermediaries between the complex document information and the self-attention module. By aggregating and condensing document information into prototype representations first, the system reduces the computational complexity fed into the self-attention mechanism, allowing high-precision semantic similarity measurement with reduced overhead.
Data Source
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AI summary
An example apparatus disclosed includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of execute or instantiate the machine readable instructions to identify a first source of object label representation and a second source of object label representation, the first source or the second source including an estimated label prototype vector associated with an input text-based object query, determine a first contextualized embedding for the first source and a second contextualized embedding for the second source, and combine the first contextualized embedding and the second contextualized embedding to generate a candidate object representation associated with the input text-based object query.