Neural Network Search Ranking via Term Independence
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Solution Overview
Problem
Existing search systems face limitations in using neural networks for large-scale document retrieval and ranking due to computational expenses, which restrict their deployment beyond late-stage re-ranking, especially in environments with low latency requirements.
Innovation Solution
Implementing query term independence in machine learning models allows for pre-computation of term-document scores, enabling efficient retrieval and ranking using deep learning models even in computationally limited environments by simplifying the neural network architecture and utilizing pre-calculated scores with an inverted index.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are used for document retrieval and ranking, then ranking effectiveness is improved, but computational expense increases
Solution Approach 1:
The query is segmented into individual query terms, and the neural network processes each term independently rather than the entire query as a whole. This segmentation allows the system to retrieve pre-computed scores for each term and aggregate them, significantly reducing computational expense while maintaining ranking effectiveness.
Solution Approach 2:
The system pre-computes and stores term-document scores in an inverted index before actual search queries are processed. When a query arrives, the system retrieves these pre-computed scores and aggregates them, eliminating the need to run the neural network during query processing. This preliminary action shifts computational burden from query time to offline preparation time.
2Measurement precision
If neural networks are used for large-scale document retrieval, then retrieval accuracy is improved, but processing speed decreases
Solution Approach 1:
The system performs preliminary computation of term-document scores and stores them in an inverted index structure before actual search operations. During query processing, the system quickly retrieves pre-computed scores and aggregates them, achieving both high retrieval accuracy and fast processing speed by avoiding real-time neural network computation.
3Use of energy by moving object
If query term independence is implemented, then computational burden is reduced, but model complexity changes
Solution Approach 1:
The model is segmented into independent term processors, where each query term is handled separately. This segmentation simplifies the computational process during query execution, as each term can be processed independently and results aggregated, reducing overall computational burden despite the architectural change.
Solution Approach 2:
An inverted index is introduced as an intermediary data structure that stores pre-computed term-document scores. This intermediary layer decouples the neural network model from direct query processing, allowing the model to be trained offline while enabling fast, simple score aggregation during actual search operations.
4Productivity
If pre-computation of term-document scores is performed, then retrieval efficiency is improved, but storage requirements increase
Solution Approach 1:
The system pre-computes and stores term-document scores in an inverted index to enable fast retrieval during actual search operations. This preliminary computation shifts the workload from online query processing to offline preparation, improving retrieval efficiency while requiring additional storage space for the pre-computed scores.
Data Source
AI summary
Described herein is a mechanism for utilizing a neural network to identify and rank search results. A machine learning model is trained by converting training data comprising query-document entries into query term-document entries. The query term-document entries are utilized to train the machine learning model. A set of query terms are identified. The query terms can be derived from a query history. The trained machine learning model is used to calculate document ranking scores for the query terms and the resultant scores are stored in a pre-calculated term-document index. A query to search the document index is broken down into its constituent terms and an aggregate document ranking score is calculated from a weighted sum of the document ranking scores corresponding to the individual query terms. Because the term-document index can be pre-calculated, it can be downloaded to provide deep learning search capabilities in a computationally limited environment.