Recently, it has become more and more difficult for the existing web based systems
to locate or retrieve any kind of relevant information, due to the rapid growth of the
World Wide Web (WWW) in terms of the information space and the amount of the
users in that space. However, in today' / s world, many systems and approaches make
it possible for the users to be guided by the recommendations that they provide
about new items such as articles, news, books, music, and movies. However, a lot of
traditional recommender systems result in failure when the data to be used
throughout the recommendation process is sparse. In another sense, when there
exists an inadequate number of items or users in the system, unsuccessful
recommendations are produced.
Within this thesis work, ReMovender, a web based movie recommendation system,
which uses a content boosted collaborative filtering approach, will be presented.
ReMovender combines the local/global similarity and missing data prediction
v
techniques in order to handle the previously mentioned sparseness problem
effectively. Besides, by putting the content information of the movies into
consideration during the item similarity calculations, the goal of making more
successful and realistic predictions is achieved.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12610984/index.pdf |
| Date | 01 September 2009 |
| Creators | Ozbal, Gozde |
| 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 | To liberate the content for public access |
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