Geo-activity Recommendations by using Improved Feature Combination Masoud Sattari, Ismail H....

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Geo-activity Recommendations by using Improved Feature

Combination

Masoud Sattari, Masoud Sattari, Ismail H. Toroslu, Ismail H. Toroslu, Pinar SenkulPinar Senkul, Murat Manguoglu, Murat Manguoglu

Panagiotis SymeonidisPanagiotis Symeonidis**, , Yannis ManolopoulosYannis Manolopoulos**

Middle East Technical University (METU), TurkeyMiddle East Technical University (METU), Turkey**Aristotle University of Thessaloniki, GreeceAristotle University of Thessaloniki, Greece

LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

Introduction

• Increasing role of GPS-assisted systems in daily life

• Recommendation based on geographical position of user

LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8 2/24

Problem Definition

• Recommend an activity to a user that is in a location

• Recommend a location for a user that wants to do a specific activity

• Data set is very sparse

• System should be able to predict the values of missing entries

Data set

• Data set gathered by Microsoft Research Asia• GPS trajectory of 162 users in 2.5 years

• Add comments about activities done at a specific location

• Data is organized in different Matrices

• Yellow books of cities give informative data• Number of available Features in a specific areas of a city

(Sport, Food, Shopping, Music,…)

Data set (cont.)

Our method

• We merge additional data to original data set

• Singular Value Decomposition (SVD) is a common method to reveal latent semantic structure

• Use SVD to propagate the effect of additional data

Merging model

Prediction on Similar rows and columns

Example

Example (cont.)

Example (con.)

Predicted Work in [1] Original

x11 21 10.4205 1

x22 18.5 15.3352 17

x33 43.16 7.6185 53

Evaluation method

Experimental Results

Finding optimal parameters:

• Selecting the number of top similar rows and columns affects final results directly.

• Parameters m and n should be selected so that, MAE and RMSE be as low as possible.

• To find optimal value of m, different values of n are examined to get minimum RMSE. Same method is used to find optimal value of m

• In each fold of execution 10% of matrix X is set to 0 randomly and these entries are estimated with our method.

1) Zheng, V. W., Zheng, Y., Xie, X. and Yang, Q. Collaborative location and activity recommendations with GPS history data. In WWW ’10: Proc. of the 19th International World Wide Web Conference. New York, NY, USA: ACM.

Experimental ResultsPrediction with Abstraction:•In the Location-Activity matrix, interval between maximum and minimum values is large

•Some values are very large, in the range of a few hundreds, there are also so many values less than 100.

•Rather than using these actual values, it would be better if abstract and discrete values are used in a small range

•Partition the nonzero values of Location-Activity matrix to clusters using k-means clustering ( k=5 for our data set)

• Ex: C1=1,1,2,3,4, C2=17,17 C3=53,53 C4=76,82

•Find the error between the clusters that original value belongs to and the cluster that predicted value falls into it.

• An abstraction method also is applied to both methods and error terms are as following diagrams.

1) Zheng, V. W., Zheng, Y., Xie, X. and Yang, Q. Collaborative location and activity recommendations with GPS history data. In WWW ’10: Proc. of the 19th International World Wide Web Conference. New York, NY, USA: ACM.

Conclusion

• Merging metrices + SVN

• An abstraction technique is performed to evaluate results fairly

• Optimized parameter for similarity

• Final results especially in RMSE reveal improvement on prediction

• Future work: Integrate user into this schema

The End

Thanks for your attention!Any question?

Geo-activity Recommendations by using Improved Feature Combination Masoud Sattari, Ismail H....

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