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&rsquo / 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.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12615173/index.pdf |
| Date | 01 November 2012 |
| Creators | Cabir, Hassane Natu Hassane |
| Contributors | Alpaslan, Ferda Nur |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | Access forbidden for 1 year |
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