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

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2014

Öğrenci: BEHLÜL UÇAR

Eş Danışman: İSMAİL HAKKI TOROSLU, PINAR KARAGÖZ

Özet:

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.