Performance Evaluation and Optimization of Math-Similarity Search

TL;DR

This paper evaluates and optimizes a math similarity search system, demonstrating significant improvements in performance, relevance ranking, and recall through proposed optimization techniques.

Contribution

It introduces an optimization process for math similarity search, enhancing efficiency and effectiveness over initial implementations.

Findings

Optimization significantly improved search performance

Enhanced relevance ranking accuracy

Increased recall in mathematical expression retrieval

Abstract

Similarity search in math is to find mathematical expressions that are similar to a user's query. We conceptualized the similarity factors between mathematical expressions, and proposed an approach to math similarity search (MSS) by defining metrics based on those similarity factors [11]. Our preliminary implementation indicated the advantage of MSS compared to non-similarity based search. In order to more effectively and efficiently search similar math expressions, MSS is further optimized. This paper focuses on performance evaluation and optimization of MSS. Our results show that the proposed optimization process significantly improved the performance of MSS with respect to both relevance ranking and recall.