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

04/14/2019
by   Ahsaas Bajaj, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/02/2018

App Store 2.0: From Crowd Information to Actionable Feedback in Mobile Ecosystems

Given the increasing competition in mobile app ecosystems, improving the...
research
09/14/2021

The Impact of User Demographics and Task Types on Cross-App Mobile Search

Recent developments in the mobile app industry have resulted in various ...
research
09/18/2020

A Knowledge Graph based Approach for Mobile Application Recommendation

With the rapid prevalence of mobile devices and the dramatic proliferati...
research
10/13/2022

Decomposing User-APP Graph into Subgraphs for Effective APP and User Embedding Learning

APP-installation information is helpful to describe the user's character...
research
04/19/2019

StegoAppDB: a Steganography Apps Forensics Image Database

In this paper, we present a new reference dataset simulating digital evi...
research
12/17/2018

Understanding Mobile Search Task Relevance and User Behaviour in Context

Improvements in mobile technologies have led to a dramatic change in how...
research
07/18/2019

OCC: A Smart Reply System for Efficient In-App Communications

Smart reply systems have been developed for various messaging platforms....

Please sign up or login with your details

Forgot password? Click here to reset