A comparison of different recommendation techniques for a hybrid mobile game recommender system

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Tezin Türü: Yüksek Lisans

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

Tezin Onay Tarihi: 2012

Öğrenci: HASSANE NATU HASSANE CABİR

Danışman: FERDA NUR ALPASLAN

Özet:

As information continues to grow at a very fast pace, our ability to access this information effectively does not, and we are often realize how harder is getting to locate an object quickly and easily. The so-called personalization technology is one of the best solutions to this information overload problem: by automatically learning the user profile, personalized information services have the potential to offer users a more proactive and intelligent form of information access that is designed to assist us in finding interesting objects. Recommender systems, which have emerged as a solution to minimize the problem of information overload, provide us with recommendations of content suited to our needs. In order to provide recommendations as close as possible to a user’s taste, personalized recommender systems require accurate user models of characteristics, preferences and needs. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users, But it suffers from several issues like data sparsity and cold start. In one-class collaborative filtering, a special type of collaborative filtering methods that aims to deal with datasets that lack counter-examples, the challenge is even greater, since these datasets are even sparser. In this thesis, we present a series of experiments conducted on a real-life customer purchase database from a major Turkish E-Commerce site. The sparsity problem is handled by the use of content-based technique combined with TFIDF weights, memory based collaborative filtering combined with different similarity measures and also hybrids approaches, and also model based collaborative filtering with the use of Singular Value Decomposition (SVD). Our study showed that the binary similarity measure and SVD outperform conventional measures in this OCCF dataset.