M. Tech. Information Networks / The growth of the internet has made it easy for people to exchange information resulting in the abundance of information commonly referred to as information overload. It causes retailers to fail to make adequate sales since the customers are swamped with a lot of options and choices. To lessen this problem retailers have begun to find it useful to make use of algorithmic approaches to determine which content to show consumers. These algorithmic approaches are known as recommender systems. Collaborative Filtering recommender systems suggest items to users based on other users reported prior experience with those items. These systems are, however, vulnerable to shilling attacks since they are highly dependent on outside sources of information. Shilling is a process in which syndicating users can connive to promote or demote a certain item, where malicious users benefit from introducing biased ratings. It is, however, critical that shilling detection systems are implemented to detect, warn and shut down shilling attacks within minutes. Modern patented shilling detection systems employ: (a) classification methods, (b) statistical methods, and (c) rules and threshold values defined by shilling detection analysts, using their knowledge of valid shilling cases and the false alarm rate as guidance. The goal of this dissertation is to determine a context for, and assess the performance of Meta-Learning techniques that can be integrated in the shilling detection process.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:tut/oai:encore.tut.ac.za:d1001931 |
| Date | January 2015 |
| Creators | Bhebe, Wilander. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Text |
| Format |
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