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

Cabir, Hassane Natu Hassane
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.


Improved probabilistic matrix factorization model for sparse datasets /
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The amount of information on the World Wide Web has increased significantly owing to advancing web and information technologies. This has made it difficult for users to obtain relevant and useful information thus there is a need for information filtering. Recommender Systems (RS) have emerged as a technique to overcome the problem. Collaborative Filtering (CF) that is one of the widely used RS approaches aims to predict users’ preference concerning an item. The main idea behind CF is the users who agreed in...
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Saçak Düzgün, Sayıl; Birtürk, Ayşe Nur; Department of Computer Engineering (2012)
With the increasing amount of data on web, people start to need tools which will help them to deal with the most significant ones among the thousands. The idea of a system which recommends items to its users emerged to fulfill this inevitable need. But most of the recommender systems make recommendations for individuals. On the other hand, some people need recommendation for items which they will use or for activities which they will attend together. Group recommenders serve for these purposes. Group recomm...
Explicit diversification of search results across multiple dimensions for educational search
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Making use of search systems to foster learning is an emerging research trend known assearch as learning. Earlier works identified result diversification as a useful technique to support learning-oriented search, since diversification ensures a comprehensive coverage of various aspects of the queried topic in the result list. Inspired by this finding, first we define a new research problem, multidimensional result diversification, in the context of educational search. We argue that in a search engine for th...
Probabilistic matrix factorization based collaborative filtering with implicit trust derived from review ratings information
Ercan, Eda; Taşkaya Temizel, Tuğba; Department of Information Systems (2010)
Recommender systems aim to suggest relevant items that are likely to be of interest to the users using a variety of information resources such as user profiles, trust information and users past predictions. However, typical recommender systems suffer from poor scalability, generating incomprehensible and not useful recommendations and data sparsity problem. In this work, we have proposed a probabilistic matrix factorization based local trust boosted recommendation system which handles data sparsity, scalabil...
A Usability study on electronic document management system in Middle East Technical University
Karagöz, Alpay; Özkan Yıldırım, Sevgi; Department of Information Systems (2013)
The development of information technologies (IT) in recent years has started to affect the daily routines of the people. These technologies have changed the way that the things are done. One of these technologies is Electronic Document Management System. Considering the increasing amount of documents needed for the institutions, it could be said that there was a need for a system to manage this complexity. However, usability of such technologies depend on the people who would use the system. Usability probl...
Citation Formats
H. N. H. Cabir, “A Comparison of different recommendation techniques for a hybrid mobile game recommender system,” M.S. - Master of Science, Middle East Technical University, 2012.