Efficient rating estimation by using similarity in multi-dimensional check-in data

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2014
Uçar, Behlül
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The usage coverage of location based social networks have boomed in the last years as well as the amount of data produced in them. This data is suitable for processing in order to make prediction. One of the requirements of this process is that the method used should be suitable for very big data sets. We propose a graph-based similarity calculation method in location-based social networks which improves the rating prediction performance of Singular Value Decomposition based collaborative filtering systems. We also propose a new rating prediction algorithm which increases the efficiency of rating prediction significantly. The methods are tested on check-in data of several users and the results are presented.
Recommender systems (Information filtering)., Information filtering systems., Semantic computing., Semantic integration (Computer systems).
http://etd.lib.metu.edu.tr/upload/12617687/index.pdf
https://hdl.handle.net/11511/24000
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
B. Uçar, “Efficient rating estimation by using similarity in multi-dimensional check-in data,” M.S. - Master of Science, Middle East Technical University, 2014.
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