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| Content Provider | ACM Digital Library |
|---|---|
| Author | Kharitonov, Eugene Serdyukov, Pavel |
| 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. |
| Starting Page | 2555 |
| Ending Page | 2558 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450311564 |
| DOI | 10.1145/2396761.2398690 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-10-29 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Click models Personalization Web search Context-aware ranking |
| Content Type | Text |
| Resource Type | Article |
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