ReFilter: Bridging Embeddings and LLM Filtering for Similar Mobile App Retrieval

2026-09-21

AI Summary

为解决移动应用相似性检索中功能相似性识别问题，提出ReFilter框架，结合嵌入式检索与大语言模型过滤，提高检索精度至90%。

Abstract

Retrieving similar mobile applications (apps) is essential for researchers, developers, and end-users. Researchers use similarity detection to study app ecosystems and trends, developers for competitor analysis, and end-users for focused app recommendations. Existing approaches rely on embedding-based retrieval, which captures semantic similarity but fails to identify functionally similar apps. To our knowledge, no prior work has applied large language model (LLM)-based filtering to this task, due to the high computational cost of evaluating large numbers of app pairs. To address this gap, we propose ReFilter, a hybrid framework that first Retrieves semantically related candidate apps using embeddings and then applies LLM-based contextual Filtering to identify true functionally similar apps with higher precision. This design balances efficiency and accuracy, achieving an F1-score of 90% for retrieving similar apps. By improving the relevance of app alternatives, ReFilter enables more accurate app comparisons and supports improved ecosystem understanding, competitor analysis, and recommendations.

Problem

• embedding-based retrieval
• functional similarity
• mobile applications

Innovation

• hybrid framework
• embedding-based retrieval
• LLM-based filtering
• functional similarity