RelEmb: A relevance-based application embedding for Mobile App retrieval
and categorization
release_ivipglhnpjcqvakd56aysrvlly
by
Ahsaas Bajaj, Shubham Krishna, Mukund Rungta, Hemant Tiwari, Vanraj
Vala
2019
Abstract
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.
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