Distributed Embedding Training for Search Latency Reduction
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Solution Overview
Problem
Modern search engines that use complex machine-trained models for determining query-item matches require significant computing resources, leading to high latency and inefficiency, especially when dealing with high-dimensional training examples.
Innovation Solution
A resource-efficient technique utilizing a distributed training framework to produce trained embeddings, which are then used in an inference stage that avoids complex models and high-dimensional embeddings, allowing for parallel processing and expedited query processing by calculating similarity scores and adjusting click counts based on embedding similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine-trained models with large number of neural network layers and parameters are used to determine query-item matches, then matching accuracy is improved, but computing resource consumption increases significantly
Solution Approach 1:
The patent segments the complex matching process into two distinct stages: (1) a training stage that produces embedding representations for queries and items, and (2) an inference stage that uses these embeddings for efficient similarity computation. This segmentation allows the computationally intensive model training to be performed once offline, while online inference uses lightweight embedding comparisons, thereby reducing real-time computing resource consumption while maintaining matching accuracy.
Solution Approach 2:
The patent performs preliminary action by pre-computing embedding representations for queries and items during the training stage before actual inference occurs. These pre-computed embeddings capture the semantic meaning of queries and items in a condensed vector form, enabling efficient similarity calculations during inference without requiring repeated execution of complex neural network forward passes, thus significantly reducing online computing resource consumption.
2Measurement precision
If complex machine-trained models are used for query processing, then matching accuracy is improved, but processing latency increases
Solution Approach 1:
The patent divides the processing pipeline into offline training phase and online inference phase. During offline training, complex models generate embedding representations that are stored for reuse. During online inference, the system only performs lightweight embedding similarity computations rather than full model forward passes, dramatically reducing processing latency for user queries while preserving matching accuracy through the use of pre-trained embeddings.
Solution Approach 2:
The system performs preliminary computation by generating and storing embedding representations for queries and items before inference time. This pre-computation allows the inference stage to quickly compute similarities using simple vector operations (e.g., cosine similarity, dot product) instead of executing complex neural network computations, thereby reducing processing latency while maintaining matching accuracy.
3Adaptability or versatility
If high-dimensional embeddings are used in inference processing, then representation capability is improved, but computing resource requirements increase
Solution Approach 1:
The patent extracts the essential semantic information from high-dimensional embeddings into compact, fixed-dimensional vector representations during the training stage. These extracted embeddings retain the core representational power needed for accurate matching while being significantly more efficient for storage and computation during inference, thereby reducing computing resource requirements while preserving representation capability.
Solution Approach 2:
The patent changes the dimensional parameters of embeddings by projecting high-dimensional representations into lower-dimensional spaces during training. This parameter transformation maintains the essential semantic relationships and representation capability needed for accurate query-item matching while reducing the computational complexity and resource requirements for similarity computations during inference.
Data Source
AI summary
A resource-efficient technique is described for producing and utilizing a set of trained embeddings. With respect to its training phase, the technique receives a group of sparsely-expressed training examples of high dimensionality. The technique processes the training examples using a distributed training framework of computing devices. With respect to its inference stage, the technique draws on the embeddings produced by the training framework. But in one implementation, the inference-stage processing applies a different prediction function than that used by the training framework. One implementation of interference-stage processing involves determining a distance between a query embedding and a candidate item embedding, where each such embedding is obtained or derived from the trained embeddings produced by the training framework. Another manifestation of inference-stage processing involves adjusting click counts based on identified relations among items embeddings.