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Location Based Personalization and Prediction of Data Services using a Learning Algorithm

by Sagar Jhobalia, Monal Vora, Bhavin Palan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 7
Year of Publication: 2012
Authors: Sagar Jhobalia, Monal Vora, Bhavin Palan
10.5120/7357-9656

Sagar Jhobalia, Monal Vora, Bhavin Palan . Location Based Personalization and Prediction of Data Services using a Learning Algorithm. International Journal of Computer Applications. 48, 7 ( June 2012), 1-7. DOI=10.5120/7357-9656

@article{ 10.5120/7357-9656,
author = { Sagar Jhobalia, Monal Vora, Bhavin Palan },
title = { Location Based Personalization and Prediction of Data Services using a Learning Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 7 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number7/7357-9656/ },
doi = { 10.5120/7357-9656 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:45:05.026369+05:30
%A Sagar Jhobalia
%A Monal Vora
%A Bhavin Palan
%T Location Based Personalization and Prediction of Data Services using a Learning Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 7
%P 1-7
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper outlines a model that enriches the user experience of using Mobile Value Added Services (MVAS). Specifically the location based data services segment of MVAS is taken into consideration for experimentation purposes. The model describes a method to personalize user information related to various data services offered to the user on a mobile phone. Our model is trained to predict the services required by specific set of users at a given time and location. In addition to prediction the algorithm ranks services to personalize the user experience; anticipating user's need and preferences. The model delineated is an intelligent model that evolves with usage of the application. The user interacts via query-alert and feedback. The paper also describes some efficiency measures, implementation guidelines and the details of surveys conducted and the conclusions deduced on experiments performed with a simulated prototype.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Location Based Data Services Relative Ranking Algorithm Personalization Prediction

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