This paper is published in Volume-3, Issue-2, 2017
Area
Dot Net
Author
Lakshmi Varsha .D
Co-authors
Nandhini S. M, Ramya .P
Org/Univ
Sri Vidya College Of Engineering & Technology, Virudhunagar, Tamil Nadu, India
Pub. Date
24 March, 2017
Paper ID
V3I2-1293
Publisher
Keywords
GPS, Geographical Information, Rating Prediction Model, Recommendation, Geo Social Factor.

Citationsacebook

IEEE
Lakshmi Varsha .D, Nandhini S. M, Ramya .P. Nearest ATM in My Location, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Lakshmi Varsha .D, Nandhini S. M, Ramya .P (2017). Nearest ATM in My Location. International Journal of Advance Research, Ideas and Innovations in Technology, 3(2) www.IJARIIT.com.

MLA
Lakshmi Varsha .D, Nandhini S. M, Ramya .P. "Nearest ATM in My Location." International Journal of Advance Research, Ideas and Innovations in Technology 3.2 (2017). www.IJARIIT.com.

Abstract

In this application we have to initialize the available ATMs of all banks and its locations. We also have to initialize the end user mobile number to access it. In order to find the nearby ATMs the user has to give the current location of that particular user. We will give the ATMs that are available in the nearest area and also provide the distance and Rating for them. It makes full use of the mobile user’s location sensitive characteristics to carry out rating predication. Refer to these social networks involving geographical information as location-based social networks. Such information brings opportunities and challenges for recommender systems. It makes full use of the mobile user’s location sensitive characteristics to carry out rating predication. The relevance between user’s ratings and user-item geographical location distances called as user-item geographical connection. It is discovered that human’s rating behaviors are affected by geographical location significantly. The personalized Location Based Rating Prediction model is proposed by combining three factors: user-item geographical connection user-user geographical connection and interpersonal interest similarity. In particular the geographical location denotes user’s real-time mobility especially when users travel to new cities and these factors are multiple together to improve the accuracy and applicability of recommender systems.
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