Evaluating Personalised Website Ranking Using Small Scale User Feedback: A User Study
| Type of publication: | Mastersthesis |
| Citation: | ba:rahmouni |
| Type: | Bachelor Thesis |
| Year: | 2018 |
| Month: | October |
| School: | TU Darmstadt, Knowledge Engineering Group |
| URL: | /lehre/arbeiten/bachelor/2018/Rahmouni_Khalil.pdf |
| Abstract: | This thesis evaluates the ranking quality of a web browser extension search engine that uses explicit relevance feedback to learn a personalized model. A user study is conducted to collect a small scale data that will be used in the evaluation process and the comparison with classi?cation SVM and SVM Rank. We conclude that the learned personalized model enhances the ranking performance and outperforms the original rank, classi?cation SVM and SVM Rang in a small-scale data. |
| Userfields: | betreuer={ELM} |
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