Binary Vector Semantic Search Reducing Memory and Latency
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Solution Overview
Problem
Conventional search engine technologies rely on keyword matching techniques like TF-IDF, which fail to capture user intent by not considering semantic similarity between search terms and result candidates, leading to irrelevant results and high computational costs due to memory-intensive floating-point vector representations.
Innovation Solution
The approach generates search results based on semantic similarity by converting queries and search result candidates into binary vectors, using neural networks to map them into semantic space and then converting these vectors into binary codes, reducing memory consumption and network latency while improving relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If TF-IDF algorithms with floating-point vector representations are used for search matching, then search results can be generated based on term frequency and inverse document frequency, but memory consumption increases and computational efficiency decreases
Solution Approach 1:
The patent changes the parameter type of vector representations from floating-point numbers to binary values (0 or 1). This parameter transformation maintains the ability to represent search term importance and semantic relationships while dramatically reducing memory consumption. Binary vectors require significantly less storage space compared to floating-point vectors, directly resolving the contradiction between measurement precision and quantity of substance.
Solution Approach 2:
The patent employs binary vectors as a simplified, lightweight alternative to complex floating-point vector representations. These binary vectors act as efficient proxies that capture essential semantic information without the computational overhead and memory requirements of floating-point arithmetic, enabling faster and more memory-efficient search operations.
2Measurement precision
If floating-point vector representations are used for semantic similarity calculation, then semantic similarity between queries and search results can be measured, but network latency increases and throughput decreases
Solution Approach 1:
The patent transforms the parameter representation from floating-point to binary, enabling extremely fast comparison operations. Binary vector comparison using operations like Hamming distance or bitwise XOR is computationally much less intensive than floating-point arithmetic, directly improving search throughput and reducing network latency while maintaining semantic similarity measurement capability.
Solution Approach 2:
The patent substitutes complex floating-point arithmetic operations with simpler binary comparison operations. This substitution replaces computationally expensive mechanical processes (floating-point multiplication, addition, and division) with efficient bitwise operations, significantly improving processing speed and system productivity.
3Ease of manufacture
If keyword matching techniques are used for search, then exact term matches can be identified, but user intent cannot be captured when terms are semantically similar but structurally different
Solution Approach 1:
The patent introduces binary vectors as an intermediary representation between raw search terms and similarity measurement. These binary vectors encode semantic relationships and term importance, serving as a mediator that preserves user intent information while enabling efficient computation. The binary vector representation captures semantic meaning beyond exact keyword matches, reducing information loss about user intent.
Solution Approach 2:
The patent changes the representation parameter from exact keyword matching to binary-encoded semantic features. This parameter transformation allows the system to recognize semantically similar terms (e.g., 'shoes' and 'footwear') by their binary vector similarity rather than requiring exact string matches, thereby capturing user intent while maintaining implementation simplicity.
Data Source
AI summary
In some aspects, a query for one or more items is received. The query includes a plurality of terms. It is determined that a search result candidate is semantically similar to the query based at least in part on a distance between a first vector that represents the query and a second vector that represents the search result candidate in semantic space. The first vector and the second vector can each correspond to a single vector of binary values. Based at least in part on the determining that the search result candidate is semantically similar to the query, the search result candidate may be caused to be displayed. The search result candidate can include at least a first term that has a same meaning as at least a second term of the query, and the first term and the second term may have a different structure.