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@MASTERSTHESIS{ba:rahmouni,
    author = {Rahmouni, Khalil},
     month = oct,
     title = {Evaluating Personalised Website Ranking Using Small Scale User Feedback: A User Study},
      type = {Bachelor Thesis},
      year = {2018},
    school = {TU Darmstadt, Knowledge Engineering Group},
       url = {https://www.ke.tu-darmstadt.de/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.},
betreuer={ELM}
}