2019/04/14 by Ahsaas Bajaj, Bajaj, Ahsaas, Shubham Krishna +8
Computer Science · Decision Sciences · Physics and Astronomy · #Artificial intelligence #Categorization #Cluster analysis #Complex Network Analysis Techniques #Computer science #Data mining #Dissemination #Embedding #FOS: Computer and information sciences #Image retrieval #Information Retrieval (cs.IR) #Information retrieval #Mobile apps #Mobile device #Personal Information Management and User Behavior #Recommender Systems and Techniques #Relevance (law) #Relevance feedback #Web Data Mining and Analysis #World Wide Web #cs.IR
paper · pdf · doi:10.48550/arxiv.1904.06672
published in arXiv (Cornell University) (Cornell University) · 13 Pages. Accepted at CICLing 2019
arxiv created 2019/04/14 · openalex publication_date 2019/04/14 · arxiv updated 2019/04/16 · openalex created_date 2022/07/29 · openalex updated_date 2026/07/28
Information Retrieval Systems have revolutionized the organization and\nextraction of Information. In recent years, mobile applications (apps) have\nbecome primary tools of collecting and disseminating information. However,\nlimited research is available on how to retrieve and organize mobile apps on\nusers' devices. In this paper, authors propose a novel method to estimate\napp-embeddings which are then applied to tasks like app clustering,\nclassification, and retrieval. Usage of app-embedding for query expansion,\nnearest neighbor analysis enables unique and interesting use cases to enhance\nend-user experience with mobile apps.\n