Demographic context in web search re-ranking

Authors:
Eugene Kharitonov;Pavel Serdyukov
Affiliations:
Yandex, LLC & University of Glasgow, Moscow, Russian Fed.;Yandex, LLC, Moscow, Russian Fed.
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

In this paper we study usefulness of user's demographical context for improving ranking of ambiguous queries. Context-aware relevance model is learnt from implicit user behaviour by using a simple yet general modification of a state-of-art click model which is capable to catch dependences from the search context. After that the machine learned click model is used in an offline re-ranking experiment and it is demonstrated that the demographical context ranking features provide improvements in ranking quality. Further, we perform a study to investigate the impact of different facets of demographical features (gender, age, and income) on search ranking performance and manually analyse queries which exhibit strong context dependences to get an additional understanding of the model behaviour.