Adapting Document Ranking to Users' Preferences Using Click-Through Data

@inproceedings{Zhao2006AdaptingDR,
  title={Adapting Document Ranking to Users' Preferences Using Click-Through Data},
  author={Min Zhao and Hang Li and Adwait Ratnaparkhi and Hsiao-Wuen Hon and Jue Wang},
  booktitle={AIRS},
  year={2006}
}
This paper proposes a new approach to ranking the documents retrieved by a search engine using click-through data. The goal is to make the final ranked list of documents accurately represent users’ preferences reflected in the click-through data. Our approach combines the ranking result of a traditional IR algorithm (BM25) with that given by a machine learning algorithm (Naïve Bayes). The machine learning algorithm is trained on clickthrough data (queries and their associated documents), while… CONTINUE READING