Performance Evaluation and Optimization of Math-Similarity Search
This work addresses the need for more effective retrieval of similar mathematical content, but it is incremental as it builds on prior MSS research.
The paper tackles the problem of similarity search for mathematical expressions by optimizing an existing math similarity search (MSS) method, resulting in significant improvements in relevance ranking and recall.
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.