Many e-commerce web sites such as online book retailers or specialized
information hubs such as online movie databases make use of recommendation
systems where users are directed to items of interests based on past user
interactions. Keyword-based approaches, collaborative and content filtering
techniques have been tried and used over the years each having their own
shortcomings. While keyword based approaches are naive and do not take content
or context into account collaborative and content filtering techniques suffer from
biased ratings, first item and first-rater problems. Recent approaches try to
incorporate underlying semantic properties of data by employing ontology based
usage mining. This thesis aims to design a recommendation system based on
ontological data where web pages are seen as objects with attributes and relations.
Instead of relying on users&rsquo / content ratings, user sessions are clustered on a
iv
semantic level to capture different behavioral groups. Since semantic information
is used for the clustering distance function, each cluster represents a behavior
group instead of simpler data groups. New users are then assigned to individual
clusters that best represent their behavior and recommendations are generated
accordingly. In this thesis we use the recommendation results as a means for
measuring the effectiveness of the clusters we have generated. We have compared
the results obtained using the ontological data and the results obtained without
using it and shown that semantic integrating semantic knowledge increases both
precision and recall.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12611902/index.pdf |
| Date | 01 June 2010 |
| Creators | Yilmaz, Hakan |
| Contributors | Senkul, Pinar |
| 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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