The tremendous growth of Web has made information overload problem increasingly serious. Users are often confused by huge amount of information available on the internet and they are faced with the problem of finding the most relevant information
that meets their needs. Recommender systems have proven to be an important solution approach to this problem. This thesis will present OPENMORE, a movie recommendation system, which is primarily based on content-based filtering technique. The distinctive point of this study lies in the methodology used to
construct and update user and item profiles and the optimizations used to fine-tune the constructed user models. The proposed system arranges movie content data as features of a set of dimension slots, where each feature is assigned a stable feature weight regardless of individual movies. These feature weights and the explicit feedbacks provided by the user are then used to construct the user profile, which is fine-tuned through a set of optimization mechanisms. Users are enabled to view their profile, update them and create multiple contexts where they can provide negative and positive feedback for the movies on the feature level.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12609479/index.pdf |
| Date | 01 May 2008 |
| Creators | Kirmemis, Oznur |
| 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 public access |
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