Sun. Apr 21st, 2024

ABSTRACT
Location prediction is widely used to forecast users’ next place to visit based on his/her mobility logs. It is an essential problem in location data processing, invaluable for surveillance, business, and personal applications. It is very challenging due to the sparsity issues of check-in data. An often ignored problem in recent studies is the variety across different check-in scenarios, which is becoming more urgent due to the increasing availability of more location check-in applications. we propose a new feature fusion-based prediction approach, GALLOP, i.e., GlobAL feature fused LOcation Prediction for different check-in scenarios. Based on the carefully designed feature extraction methods, we utilize a novel combined prediction framework. Specifically, we set out to utilize the density estimation model to profile geographical features, i.e., context information, the factorization method to extract collaborative information, and a graph structure to extract location transition patterns of users’ temporal check-in sequence, i.e., content information.


EXISTING SYSTEM
• We propose a new feature fusion approach, i.e., GlobAL feature fusion for LOcation Prediction (GALLOP), to cope with the variety problem in location prediction.
•  To improve the applicability of location prediction approach, We utilize several kinds of features and discuss their different characteristics in the variety of check-in scenarios. 
• Three classes of features are used in GALLOP: context feature(geographical aspects), collaboration feature(users’ latent interest space) and content feature(places’ description attributes). We introduce intuitive ways to model these check-in features and then formalize a combination framework to deliver the predicted target places to end-users.


DISADVANTAGES
In contrast, spatial feature based methods use the gravity and locality closeness measurements to fit into the location setting, but are usually difficult to generalize the temporal and other related features.Though some recent work pay attention to these check-in behavior differences, the inherent variety between this check-ins and their affect to location prediction is usually missingThat existing predication methods cannot be directly applied to all check-in scenarios, where their performance vary greatly 

PROPOSED SYSTEM
• We design a multiple granularity model to profile the geographical closeness. We select the predicted candidates based on the combination of local district, local city and state scales. The weights of each scale are learned from training data.
• We propose a new feature fusion approach, i.e., GlobAL feature fusion for LOcation Prediction (GALLOP), to cope with the variety problem in location prediction. To improve the applicability of location prediction approach, we utilize several kinds of features and discuss their different characteristics in the variety of check-in scenarios.
• Three classes of features are used in GALLOP: context feature (geographical aspects), collaboration feature (users’ latent interest space) and content feature (places’ description attributes). We introduce intuitive ways to model these check-in features and then formalize a combination framework to deliver the predicted target places to end users.

ADVANTAGES
 We proposed in this work can be extended to enable incremental updating. And new Comprehensive location prediction and update setting can be utilized.
 The feature fusion approach shows the advantage of feature combination to deliver improved accuracy
 The proposed GALLOP prediction approach is not only general over different check-in scenarios but also comprehensive of different features
 To improve the prediction robustness and generality
 Prove that the general location prediction approach is a better choice to tackle the location prediction challenges


SYSTEM ARCHITECTURE


SYSTEM SPECIFICATIONHARDWARE SPECIFICATION

 System   :  Pentium IV 2.4 GHz.
 Hard Disk   :  40 GB.
 Floppy Drive :  1.44 Mb.
 Monitor :  15 VGA Colour.
 Mouse :  Logitech.
 Ram :  2 Gb.


SOFTWARE SPECIFICATION
 Operating system  :  Windows XP/7.
 Coding Language :  ASP.net, C#.net
 Tool : Visual Studio 2010
 Database : SQL SERVER 2008

REFERENCES
J. Sang, T. Mei, J.-T. Sun, C. Xu, and S. Li, “Probabilistic sequential points recommendation via check-in data,” in Proceedings of the 20th International Conference on Advances in Geographic Information Systems, 2012, pp. 402–405.

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