Workshops
Papers
1. Chris Burges et al., “Learning to rank using gradient descent,” in Proceedings of the 22nd international conference on Machine learning (Bonn, Germany: ACM, 2005), 89-96, doi:10.1145/1102351.1102363, http://portal.acm.org/citation.cfm?id=1102351.1102363&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
2. Filip Radlinski and Thorsten Joachims, “Query chains: learning to rank from implicit feedback,” in Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (Chicago, Illinois, USA: ACM, 2005), 239-248, doi:10.1145/1081870.1081899, http://portal.acm.org/citation.cfm?id=1081870.1081899.
3. Andrew Trotman, “Learning to Rank,” Inf. Retr. 8, no. 3 (2005): 359-381.
4. Alekh Agarwal, Soumen Chakrabarti, and Sunny Aggarwal, “Learning to rank networked entities,” in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (Philadelphia, PA, USA: ACM, 2006), 14-23, doi:10.1145/1150402.1150409, http://portal.acm.org/citation.cfm?id=1150402.1150409&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
5. Yunbo Cao et al., “Adapting ranking SVM to document retrieval,” in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (Seattle, Washington, USA: ACM, 2006), 186-193, doi:10.1145/1148170.1148205, http://portal.acm.org/citation.cfm?id=1148170.1148205&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
6. Alekh Agarwal and Soumen Chakrabarti, “Learning random walks to rank nodes in graphs,” in Proceedings of the 24th international conference on Machine learning (Corvalis, Oregon: ACM, 2007), 9-16, doi:10.1145/1273496.1273498, http://portal.acm.org/citation.cfm?id=1273496.1273498&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
7. Zhe Cao et al., “Learning to rank: from pairwise approach to listwise approach,” in Proceedings of the 24th international conference on Machine learning (Corvalis, Oregon: ACM, 2007), 129-136, doi:10.1145/1273496.1273513, http://portal.acm.org/citation.cfm?id=1273496.1273513&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
8. Xiubo Geng et al., “Feature selection for ranking,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (Amsterdam, The Netherlands: ACM, 2007), 407-414, doi:10.1145/1277741.1277811, http://portal.acm.org/citation.cfm?id=1277741.1277811&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
9. Thorsten Joachims et al., “Learning to rank for information retrieval (LR4IR 2007),” SIGIR Forum 41, no. 2 (2007): 58-62, doi:10.1145/1328964.1328974.
10. Tao Qin et al., “Ranking with multiple hyperplanes,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (Amsterdam, The Netherlands: ACM, 2007), 279-286, doi:10.1145/1277741.1277791, http://portal.acm.org/citation.cfm?id=1277741.1277791&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
11. Filip Radlinski and Thorsten Joachims, “Active exploration for learning rankings from clickthrough data,” in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (San Jose, California, USA: ACM, 2007), 570-579, doi:10.1145/1281192.1281254, http://portal.acm.org/citation.cfm?id=1281192.1281254&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
12. Ming-Feng Tsai et al., “FRank: a ranking method with fidelity loss,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (Amsterdam, The Netherlands: ACM, 2007), 383-390, doi:10.1145/1277741.1277808, http://portal.acm.org/citation.cfm?id=1277741.1277808&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
13. Jingfang Xu and Xing Li, “Learning to rank collections,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (Amsterdam, The Netherlands: ACM, 2007), 765-766, doi:10.1145/1277741.1277898, http://portal.acm.org/citation.cfm?id=1277741.1277898&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
14. Jun Xu and Hang Li, “AdaRank: a boosting algorithm for information retrieval,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (Amsterdam, The Netherlands: ACM, 2007), 391-398, doi:10.1145/1277741.1277809, http://portal.acm.org/citation.cfm?id=1277741.1277809&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
15. Massih Reza Amini, Tuong Vinh Truong, and Cyril Goutte, “A boosting algorithm for learning bipartite ranking functions with partially labeled data,” in Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (Singapore, Singapore: ACM, 2008), 99-106, doi:10.1145/1390334.1390354, http://portal.acm.org/citation.cfm?id=1390334.1390354&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
16. Soumen Chakrabarti et al., “Structured learning for non-smooth ranking losses,” in Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (Las Vegas, Nevada, USA: ACM, 2008), 88-96, doi:10.1145/1401890.1401906, http://portal.acm.org/citation.cfm?id=1401890.1401906&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
17. Kevin Duh and Katrin Kirchhoff, “Learning to rank with partially-labeled data,” in Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (Singapore, Singapore: ACM, 2008), 251-258, doi:10.1145/1390334.1390379, http://portal.acm.org/citation.cfm?id=1390334.1390379&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
18. Jonathan L. Elsas, Vitor R. Carvalho, and Jaime G. Carbonell, “Fast learning of document ranking functions with the committee perceptron,” in Proceedings of the international conference on Web search and web data mining (Palo Alto, California, USA: ACM, 2008), 55-64, doi:10.1145/1341531.1341542, http://portal.acm.org/citation.cfm?id=1341531.1341542&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
19. John Guiver and Edward Snelson, “Learning to rank with SoftRank and Gaussian processes,” in SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (Singapore, Singapore: ACM, 2008), 266, 259, http://dx.doi.org/10.1145/1390334.1390380.
20. Rong Jin, Hamed Valizadegan, and Hang Li, “Ranking refinement and its application to information retrieval,” in Proceeding of the 17th international conference on World Wide Web (Beijing, China: ACM, 2008), 397-406, doi:10.1145/1367497.1367552, http://portal.acm.org/citation.cfm?id=1367497.1367552&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
21. Yanyan Lan et al., “Query-level stability and generalization in learning to rank,” in Proceedings of the 25th international conference on Machine learning (Helsinki, Finland: ACM, 2008), 512-519, doi:10.1145/1390156.1390221, http://portal.acm.org/citation.cfm?id=1390156.1390221&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
22. Weijian Ni, Yalou Huang, and Maoqiang Xie, “A Query Dependent Approach to Learning to Rank for Information Retrieval,” in Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management - Volume 00 (IEEE Computer Society, 2008), 262-269, http://portal.acm.org/citation.cfm?id=1446298.1446672&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
23. Tao Qin et al., “Learning to rank relational objects and its application to web search,” in Proceeding of the 17th international conference on World Wide Web (Beijing, China: ACM, 2008), 407-416, doi:10.1145/1367497.1367553, http://portal.acm.org/citation.cfm?id=1367497.1367553&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
24. Tao Qin et al., “Query-level loss functions for information retrieval,” Inf. Process. Manage. 44, no. 2 (2008): 838-855.
25. Filip Radlinski, Robert Kleinberg, and Thorsten Joachims, “Learning diverse rankings with multi-armed bandits,” in Proceedings of the 25th international conference on Machine learning (Helsinki, Finland: ACM, 2008), 784-791, doi:10.1145/1390156.1390255, http://portal.acm.org/citation.cfm?id=1390156.1390255&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
26. Michael Taylor et al., “SoftRank: optimizing non-smooth rank metrics,” in Proceedings of the international conference on Web search and web data mining (Palo Alto, California, USA: ACM, 2008), 77-86, doi:10.1145/1341531.1341544, http://portal.acm.org/citation.cfm?id=1341531.1341544&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
27. Adriano A. Veloso et al., “Learning to rank at query-time using association rules,” in Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (Singapore, Singapore: ACM, 2008), 267-274, doi:10.1145/1390334.1390381, http://portal.acm.org/citation.cfm?id=1390381.
28. Fen Xia et al., “Listwise approach to learning to rank: theory and algorithm,” in Proceedings of the 25th international conference on Machine learning (Helsinki, Finland: ACM, 2008), 1192-1199, doi:10.1145/1390156.1390306, http://portal.acm.org/citation.cfm?id=1390156.1390306&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
29. Jun Xu et al., “Directly optimizing evaluation measures in learning to rank,” in Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (Singapore, Singapore: ACM, 2008), 107-114, doi:10.1145/1390334.1390355, http://portal.acm.org/citation.cfm?id=1390334.1390355&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
30. Ke Zhou et al., “Learning to rank with ties,” in Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (Singapore, Singapore: ACM, 2008), 275-282, doi:10.1145/1390334.1390382, http://portal.acm.org/citation.cfm?id=1390334.1390382&coll=GUIDE&dl=GUIDE&CFID=7216732&CFTOKEN=41760401.
