一句话：提出融合 NER、BERT 与主题建模的移动应用功能挖掘与相似应用推荐方法，F1 达 87.38%。
移动应用开发需要提取符合用户需求与市场趋势的领域功能，但判断哪些功能具有竞争优势通常依赖产品经理人工完成。本文提出一种数据驱动方法，通过用户提供的描述识别相似应用，从而自动化功能挖掘与推荐。方法整合命名实体识别（NER）从 Google Play 应用数据中提取功能，并利用 BERT 与主题建模寻找可比应用。评估中，采用非负矩阵分解（NMF）进行主题建模并结合 Sentence-BERT（SBERT）嵌入的模型表现最佳，F1 分数达到 87.38%。
Mobile app development necessitates extracting domain-specific, essential, and innovative features that align with user needs and market trends. Determining which features provide a competitive advantage is a complex task, often managed manually by product managers. This study addresses the challenge of automating feature mining and recommendation by identifying similar apps based on user-provided descriptions. The proposed approach integrates Named Entity Recognition (NER) for feature extraction from mined Google Play app data with BERT (Bidirectional Encoder Representations from Transformers) and Topic Modeling to find comparable apps. Our top-performing model, which uses Non-negative Matrix Factorization (NMF) for Topic Modeling with Sentence-BERT (SBERT) embeddings, achieves an F1 score of 87.38%.
@inproceedings{paperbot8352,
title = {A Data-driven Approach for Mining Software Features based on Similar App Descriptions and User Reviews Analysis},
author = {Khubaib Amjad Alam and Ramsha Ali and Zyena Kamran and Sabeen Fatima and Irum Inayat},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695342}
}