RelEmb: A relevance-based application embedding for Mobile App retrieval and categorization
This addresses the problem of mobile app organization for users, but it appears incremental as it applies embedding techniques to a specific domain without broad SOTA claims.
The authors tackled the problem of retrieving and organizing mobile apps on users' devices by proposing a novel method to estimate app-embeddings, which were applied to tasks like app clustering, classification, and retrieval, resulting in enhanced end-user experience through use cases such as query expansion and nearest neighbor analysis.
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.