Match-Tensor Architecture for Search Relevance
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Solution Overview
Problem
Traditional online search solutions rely on manually defined features, requiring significant effort to generate and test new features, and often fail to accurately match query intent due to the mismatch between query terms and document content.
Innovation Solution
The Match-Tensor architecture, which processes term-embeddings of queries and documents using a neural network to produce a three-dimensional tensor, incorporating an exact-match channel to calculate relevance scores without feature engineering, allowing for end-to-end training with a discriminative objective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually defined features are used for search, then the search system can be implemented with traditional methods, but significant effort is required to generate and test new features and the accuracy of matching query intent deteriorates
Solution Approach 1:
The system uses deep neural networks to automatically learn and generate features from raw data without manual intervention. The neural network models automatically identify relevant features and patterns, eliminating the need for manual feature engineering and testing while improving matching accuracy through automated feature learning.
Solution Approach 2:
The patent replaces manual mechanical feature engineering processes with automated neural network-based feature learning. Instead of manually defining and testing features, the system uses deep learning models to automatically discover and extract relevant features from data, substituting human effort with automated computational processes.
2Measurement precision
If manually defined features are used for search, then traditional search methods can be employed, but the accuracy of matching query intent deteriorates due to mismatch between query terms and document content
Solution Approach 1:
The neural network system automatically performs feature engineering tasks without manual intervention. The models self-learn relevant features and representations from raw data, eliminating the need for complex manual feature engineering processes while improving matching accuracy through automated pattern recognition.
Solution Approach 2:
The system transforms the search problem from using manual discrete features to using continuous vector representations learned by neural networks. By changing the parameter space from hand-crafted features to learned embeddings, the system achieves better matching accuracy while reducing the complexity of the feature engineering process.
3Ease of manufacture
If deep neural network architecture is used, then feature engineering effort is eliminated and the system can be trained end-to-end, but the computational complexity of processing increases
Solution Approach 1:
The system performs preliminary processing by pre-training neural network models on large datasets to learn feature representations before deployment. This preliminary action allows the models to be pre-computed and cached, reducing the computational power required during actual search operations while maintaining ease of implementation through reusable pre-trained models.
Data Source
AI summary
In one embodiment, a method includes receiving, from a client system associated with a user, a search query comprising a number of query terms, generating a query match-matrix for the search query, identifying a number of objects matching the search query, retrieving, for each identified object, an object match-matrix for the identified object, constructing, for each identified object, a three-dimensional tensor for the identified object, computing, for each identified object, a relevance score based on the tensor for the identified object, ranking the identified objects based on their respective relevance scores, and sending, to the first client system in response to the search query, instructions for generating a search-results interface for presentation to the user.