RelEmb: A relevance-based application embedding for Mobile App retrieval and categorization

TL;DR

This paper introduces RelEmb, a relevance-based embedding method for mobile apps that improves retrieval, clustering, and classification, enhancing user experience by enabling more effective app organization and search.

Contribution

The paper presents a novel app-embedding technique tailored for mobile app retrieval and categorization, addressing a gap in existing information retrieval research.

Findings

Effective app clustering and classification achieved

Improved app retrieval accuracy demonstrated

Enhanced user experience through query expansion and nearest neighbor analysis

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.