Geo activity recommendations by using improved feature combination

Date

2012-09-08

Author

Sattari, Masoud
Manguoğlu, Murat
Toroslu, İsmail Hakkı
Panagiotis, Symeonidis
Karagöz, Pınar
Yannis, Manolopoulos

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In this paper, we propose a new model to integrate additional data, which is obtained from geospatial resources other than original data set in order to improve Location/Activity recommendations. The data set that is used in this work is a GPS trajectory of some users, which is gathered over 2 years. In order to have more accurate predictions and recommendations, we present a model that injects additional information to the main data set and we aim to apply a mathematical method on the merged data. On the merged data set, singular value decomposition technique is applied to extract latent relations. Several tests have been conducted, and the results of our proposed method are compared with a similar work for the same data set.

Subject Keywords

Geospatial recommendation, Matrix factorization, Feature combination

URI

https://hdl.handle.net/11511/84048
https://dl.acm.org/doi/pdf/10.1145/2370216.2370432

DOI

https://doi.org/10.1145/2370216.2370432

Conference Name

14th International Conference on Ubiquitous Computing, UbiComp ( 2012)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

M. Sattari, M. Manguoğlu, İ. H. Toroslu, S. Panagiotis, P. Karagöz, and M. Yannis, “Geo activity recommendations by using improved feature combination,” Pittsburgh, United States, 2012, p. 996, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/84048.