Mining and Recommending Mobile App Features using Data-driven Analytics

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....

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Bibliographic Details
Published in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] pp. 2432 - 2434
Main Author: Ali, Ramsha
Format: Conference Proceeding
Language:English
Published: ACM 27.10.2024
Subjects:
app review analytics
bert
Bidirectional control
Data mining
Encoding
Feature extraction
lda
Mobile applications
Named entity recognition
natural language processing
ner language models
nmf
Software engineering
Transformers
ISSN:2643-1572
Online Access:Get full text
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Summary: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%.
ISSN:2643-1572
DOI:10.1145/3691620.3695371