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%.

Mobile app development necessitates the extraction of domain specific, essential and innovative features, aligning with user needs and market dynamics. Identifying features to provide competitive edge to the app developers, is a non-trivial task that is often performed manually by product managers. This study addresses the challenge of mining and recommending app features by automatically identifying similar apps corresponding to the description of apps provided by the user. The proposed approach, APPFIRE, integrates Named Entity Recognition (NER) for feature extraction and BERT (Bidirectional Encoder Representations from Transformers) coupled with Topic Modeling for identifying similar apps. Our top-performing model, utilizing Non-negative Matrix Factorization (NMF) for Topic Modeling with SBERT embeddings, achieves an F1 score of 87.38%.