Web recommendation systems have become a popular means to im-prove the usability of web sites. This paper describes the architecture of a rule-based recommendation system and presents its evaluation on two real-life ap-plications. The architecture combines recommendations from different algo-rithms in a recommendation database and applies feedback-based machine learning to optimize the selection of the presented recommendations. The rec-ommendations database also stores ontology graphs, which are used to semanti-cally enrich the recommendations. We describe the general architecture of the system and the test setting, illustrate the application of several optimization ap-proaches and present comparative results.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:32785 |
| Date | 24 January 2019 |
| Creators | Golovin, Nick, Rahm, Erhard |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-3-540-27996-9 |
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