Universal Entity Retrieval With ANN Filtering and Dynamic Distance Scoring
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Solution Overview
Problem
Existing entity retrieval (EER) deep learning models face challenges in handling large quantities of candidate pairs and varying interaction functions between source and destination entity embeddings, leading to inefficient scoring computations and suboptimal objectives.
Innovation Solution
A universal EER model incorporating a candidate reduction module, distance function selection, and an approximate nearest neighbor (ANN) search engine to efficiently score large candidate pools and provide flexible model objectives, leveraging a pre-trained large language model for embedding generation and dynamic distance functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional EER models score all candidate pairs exhaustively, then retrieval accuracy is improved, but computational time and complexity increase significantly
Solution Approach 1:
The candidate pool is segmented into multiple batches processed in parallel, and the search space is divided into regions using ANN indexing. This allows the system to evaluate candidates in manageable chunks rather than processing all candidates exhaustively in a single pass, reducing computational time while maintaining accuracy through comprehensive search.
Solution Approach 2:
An approximate nearest neighbor (ANN) search engine serves as an intermediary between the query and the candidate pool. The ANN engine provides a mechanism to efficiently filter and rank candidates without requiring exhaustive evaluation, acting as a mediator that balances computational efficiency with retrieval accuracy through intelligent search algorithms.
2Adaptability or versatility
If the model handles diverse embedding spaces with different interaction functions, then adaptability is improved, but model complexity increases
Solution Approach 1:
The model employs a universal EER architecture that can handle multiple embedding spaces and interaction functions through a single unified framework. The distance function selection module automatically adapts to different embedding spaces by selecting appropriate distance metrics, eliminating the need for separate models for each embedding type and reducing overall system complexity.
Solution Approach 2:
The model dynamically selects and adjusts distance functions based on the specific embedding spaces and interaction functions being processed. This dynamic adaptation allows the model to handle diverse embedding spaces efficiently by choosing the most appropriate similarity metrics for each case, rather than using a fixed complex architecture for all scenarios.
3Quantity of substance
If the model processes large quantities of candidate pairs, then retrieval completeness is improved, but scoring computation efficiency deteriorates
Solution Approach 1:
The system performs preliminary candidate reduction using ANN search before the main scoring computation. By pre-filtering and ranking candidates based on embedding similarity, the system reduces the number of pairs that require full scoring evaluation, thereby maintaining completeness for large candidate pools while significantly improving scoring computation efficiency.
Solution Approach 2:
The model efficiently processes large candidate pools by skipping unnecessary detailed evaluations. Through ANN-based filtering and ranking, the system rapidly identifies and processes only the most relevant candidates, rushing through the scoring computation for high-priority pairs while avoiding time-consuming detailed analysis of less relevant candidates.
Data Source
AI summary
Aspects of the disclosure include methods for leveraging a universal embedding based entity retrieval deep learning model for candidate recommendations. A method can include receiving a request for a candidate pair having a first entity and a second entity and generating a filtered candidate pool including a first number of candidates. The filtered candidate pool can include a subset of an initial candidate pool having a second number of candidates larger than the first number of candidates. A learned distance function is selected from a plurality of distance functions. At least one distance function was predetermined prior to receiving the request and at least one distance function is generated in response to receiving the request. A distance measure is determined for each candidate in the filtered candidate pool using the learned distance function and a response is returned including top K candidates according to the determined distance measures.