Graph-Based Entity Embeddings for Search Latency Reduction
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Solution Overview
Problem
Current search architectures face challenges in processing complex search queries efficiently, often sacrificing processing speed for relevance or vice versa, leading to latency issues.
Innovation Solution
The implementation of a neural network architecture that uses unsupervised and supervised learning algorithms to generate semantic representations of entities through graph data structures, optimizing embedding vectors for similarity based on user interactions and profile data, enabling efficient and relevant search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex data processing is performed to improve search result relevance, then search result relevance is improved, but processing latency increases
Solution Approach 1:
The system pre-computes and stores entity embeddings, graph representations, and search result rankings before actual query processing. When a query arrives, the system retrieves pre-computed results and performs only necessary refinements, significantly reducing processing latency while maintaining high relevance through the pre-processed complex data structures.
Solution Approach 2:
The search processing pipeline is divided into separate stages: data ingestion and graph construction, embedding generation, search query processing, and result ranking. This segmentation allows complex data processing to occur in advance during dedicated batches, while query processing benefits from pre-computed results, resolving the contradiction between processing complexity and latency.
2Measurement precision
If more computational resources are allocated to process complex queries, then search result relevance improves, but processing speed decreases
Solution Approach 1:
Computationally intensive tasks such as graph construction, embedding generation, and result ranking are performed in advance during dedicated processing batches. This preliminary action stores the results for rapid retrieval during actual query processing, allowing the system to maintain high relevance through comprehensive computational analysis while achieving fast query response times.
Solution Approach 2:
The system continuously updates and refines entity representations, graph structures, and embeddings in the background during idle periods or low-query periods. This continuous preparation ensures that when queries arrive, the system can immediately provide high-quality results without waiting for computational processing, thus maintaining both relevance and speed.
Data Source
AI summary
Techniques for implementing a learning semantic representations of sparse entities using unsupervised embeddings are disclosed herein. In some embodiments, a computer system accesses corresponding profile data of users indicating at least one entity of a first facet type associated with the user, and generating a graph data structure comprising nodes and edges based on the accessed profile data, with each node corresponding to a different entity indicated by the accessed profile data, and each edge directly connecting a different pair of nodes and indicating a number of users whose profile data indicates both entities of the pair of nodes. The computer system generating a corresponding embedding vector for the entities based on the graph data structure using an unsupervised machine learning algorithm.