Information Retrieval Systems have revolutionized the organization and extraction of Information. In recent years, mobile applications (apps) have become primary tools of collecting and disseminating information. However, limited research is available on how to retrieve and organize mobile apps on users' devices. In this paper, authors propose a novel method to estimate app-embeddings which are then applied to tasks like app clustering, classification, and retrieval. Usage of app-embedding for query expansion, nearest neighbor analysis enables unique and interesting use cases to enhance end-user experience with mobile apps.
@article{arxiv.1904.06672,
title = {RelEmb: A relevance-based application embedding for Mobile App retrieval and categorization},
author = {Ahsaas Bajaj and Shubham Krishna and Mukund Rungta and Hemant Tiwari and Vanraj Vala},
journal= {arXiv preprint arXiv:1904.06672},
year = {2019}
}