Recommender systems are information retrieval tools helping users in their information
seeking tasks and guiding them in a large space of possible options. Many hybrid
recommender systems are proposed so far to overcome shortcomings born of pure
content-based (PCB) and pure collaborative filtering (PCF) systems. Most studies on
recommender systems aim to improve the accuracy and efficiency of predictions. In
this thesis, we propose an online hybrid recommender strategy (CBCFdfc) based on
content boosted collaborative filtering algorithm which aims to improve the prediction
accuracy and efficiency. CBCFdfc combines content-based and collaborative characteristics
to solve problems like sparsity, new item and over-specialization. CBCFdfc uses
fuzzy clustering to keep a certain level of prediction accuracy while decreasing online
prediction time. We compare CBCFdfc with PCB and PCF according to prediction
accuracy metrics, and with CBCFonl (online CBCF without clustering) according to
online recommendation time. Test results showed that CBCFdfc performs better than
other approaches in most cases. We, also, evaluate the effect of user-specified parameters
to the prediction accuracy and efficiency. According to test results, we determine
optimal values for these parameters. In addition to experiments made on simulated
data, we also perform a user study and evaluate opinions of users about recommended movies. The results that are obtained in user evaluation are satisfactory. As a result,
the proposed system can be regarded as an accurate and efficient hybrid online movie
recommender.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/2/12611667/index.pdf |
| Date | 01 March 2010 |
| Creators | Gurcan, Fatih |
| Contributors | Birturk, Aysenur |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for METU campus |
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