ML Anthology

Aggregating User-Centered Rankings to Improve Web Search

Lin Li, Zhenglu Yang, Masaru Kitsuregawa
AAAI 2007 pp. 1884-1885
/aaai/2007/li2007aaai-aggregating/

Abstract

This paper is to investigate rank aggregation based on multi-ple user-centered measures in the context of the web search. We introduce a set of techniques to combine ranking lists in order of user interests termed as a user profile. Moreover, based on the click-history data, a kind of taxonomic hierar-chy automatically models the user profile which can include a variety of attributes of user interests. We mainly focus on the topics a user is interested in and the degrees of user in-terests in these topics. The primary goal of our work is to form a broadly acceptable ranking list, rather than that deter-mined by an individual ranking measure. Experiment results on a real click-history data set show the effectiveness of our aggregation techniques to improve the web search.

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Cite

Text

Li et al. "Aggregating User-Centered Rankings to Improve Web Search." AAAI Conference on Artificial Intelligence, 2007.

Markdown

[Li et al. "Aggregating User-Centered Rankings to Improve Web Search." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/li2007aaai-aggregating/)

BibTeX

@inproceedings{li2007aaai-aggregating,
  title     = {{Aggregating User-Centered Rankings to Improve Web Search}},
  author    = {Li, Lin and Yang, Zhenglu and Kitsuregawa, Masaru},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2007},
  pages     = {1884-1885},
  url       = {https://mlanthology.org/aaai/2007/li2007aaai-aggregating/}
}