Session based click features for recency ranking

Proceedings of the National Conference on Artificial Intelligence

Volumn 3, Issue , 2010, Pages 1334-1339

  Inagaki, Yoshiyuki ^a   Sadagopan, Narayanan ^a   Dupret, Georges ^a   Liao, Ciya ^a   Dong, Anlei ^a   Chang, Yi ^a   Zheng, Zhaohui ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHAINS; SEARCH ENGINES;

CLICK-THROUGH RATE; CLICKTHROUGH DATA; QUERY REFORMULATION; RECENCY RANKING;

ARTIFICIAL INTELLIGENCE;

EID: 77958599396     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Agichtein, E.; Brill, E.; and Dumais, S. 2006. Improving web search ranking by incorporating user behavior informa tion. Proc. of ACM SIGIR Conference.
2. Berberich, K.; Vazirgiannis, M.; and Weikum, G. 2005. Time-aware authority rankings. Internet Math 2(3):301-332.
3. Boldi, P.; Bonchi, F.; Castillo, C; Donato, D.; Gionis, A.; and Vigna, S. 2008. The query-flow graph: model and ap plications. In Proceedings of the ACM Conference on Infor mation and Knowledge Management (CIKM).
4. Burges, C; Shaked, T.; Renshaw, E.; Lazier, A.; Deeds, M.; Hamilton, N.; and Hullender, G. 2005. Learning to rank us ing gradient descent. Proc. of Intl. Conf. on Machine Learning.
5. Diaz, F. 2009. Integration of news content into web results. Proceedings of the Second ACM International Conference on Web Search and Data Mining (WSDM) 182-191.
6. Dong, A.; Chang, Y.; Zheng, Z.; and Gilad Mishne, Jing Bai Karolina Buchner, R. Z. C. L. G. L. 2009. Towards recency ranking in web search. WSDM.
7. Freund, Y.; Iyer, R. D.; Schapire, R. E.; and Singer, Y. 1998. An efficient boosting algorithm for combining preferences. Proceedings of International Conference on Machine Learning.
8. He, D., and Goker, A. 2000. Detecting session boundaries from web user logs. In Proceedings of the BCS-IRSG 22nd annual colloquium on information retrieval research.
9. He, D.; Goker, A.; and Harper, D. 2002. Combining evi dence for automatic web session identification. Inf. Process. Manage. 38(5):727-742.
10. Jarvelin, K., and Kekalainen, J. 2002. Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems 20:422-446.
11. Joachims, T. 2002a. Optimizing search engines using click-through data. Proc. of ACM SIGKDD Conference.
12. Joachims, T. 2002b. Optimizing search engines using click-through data. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD).
13. Jones, R., and Klinkner, K. 2008. Beyond the session time out: Automatic hierarchical segmentation of search topics in query logs. In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM).
14. Radlinski, F., and Joachims, T. 2005. Query chains: Learning to rank from implicit feedback. In ACM SIGKDD In ternational Conference On Knowledge Discovery and Data Mining (KDD).
15. Y. Cao, J. Xu, T.-Y. L. H. L. Y. H., and Hon, H.-W. 2006. Adapting ranking svm to document retrieval. Proceedings of ACM SIGIR conference.
16. Zhang, R.; Chang, Y; Zheng, Z.; Metzler, D.; and Nie, J. 2009. Search result re-ranking by feedback control ad justment for time-sensitive query. North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT).
17. Zheng, Z.; Zhang, H.; Zhang, T; Chapelle, O.; Chen, K.; and Sun, G. 2007. A general boosting method and its appli cation to learning ranking functions for web search. NIPS.