Implicit Feedback

Workshops

• NIPS 2005 Workshop on Machine Learning for Implicit Feedback and User Modeling
• SIGIR 2007 Workshop Web Information-Seeking and Interaction (Proceedings)

Researchers

• Samuel Kaski
• Thorsten Joachims
• Ryen W. White

Lectures

• User models from implicit feedback for proactive information retrieval by Samuel Kaski
• Proactive Information Retrieval by User Modeling from Eye Tracking by Jarkko Salojärvi

Projects

• A Search Engine that Learn from Implicit Feedback at Cornell

Papers

1. Thorsten Joachims et al., “Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search,” ACM Trans. Inf. Syst. 25, no. 2 (2007): 7, doi:10.1145/1229179.1229181.

2. Charles L. A. Clarke et al., “The influence of caption features on clickthrough patterns in web search,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (Amsterdam, The Netherlands: ACM, 2007), 135-142, doi:10.1145/1277741.1277767, http://portal.acm.org/citation.cfm?id=1277767&jmp=references&coll=GUIDE&dl=GUIDE&CFID=81725930&CFTOKEN=51945364.

3. Filip Radlinski and Thorsten Joachims, “Evaluating the Robustness of Learning from Implicit Feedback,” cs/0605036v1 (May 8, 2006), http://arxiv.org/abs/cs/0605036.

4. Eugene Agichtein, Eric Brill, and Susan Dumais, “Improving web search ranking by incorporating user behavior information,” in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (Seattle, Washington, USA: ACM, 2006), 19-26, doi:10.1145/1148170.1148177, http://portal.acm.org/citation.cfm?id=1148170.1148177.

5. Eugene Agichtein et al., “Learning user interaction models for predicting web search result preferences,” in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (Seattle, Washington, USA: ACM, 2006), 3-10, doi:10.1145/1148170.1148175, http://portal.acm.org/citation.cfm?id=1148170.1148175.

6. Ryen W. White, Ian Ruthven, and Joemon M. Jose, “A study of factors affecting the utility of implicit relevance feedback,” in Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (Salvador, Brazil: ACM, 2005), 35-42, doi:10.1145/1076034.1076044, http://portal.acm.org/citation.cfm?id=1076034.1076044.

7. Xuehua Shen, Bin Tan, and ChengXiang Zhai, “Context-sensitive information retrieval using implicit feedback,” in Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (Salvador, Brazil: ACM, 2005), 43-50, doi:10.1145/1076034.1076045, http://portal.acm.org/citation.cfm?id=1076045.

8. 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.

9. Thorsten Joachims et al., “Accurately interpreting clickthrough data as implicit feedback,” in Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (Salvador, Brazil: ACM, 2005), 154-161, doi:10.1145/1076034.1076063, http://portal.acm.org/citation.cfm?id=1076063.

10. Steve Fox et al., “Evaluating implicit measures to improve web search,” ACM Trans. Inf. Syst. 23, no. 2 (2005): 147-168, doi:10.1145/1059981.1059982.

11. Bing Pan et al., “The determinants of web page viewing behavior: an eye-tracking study,” in Proceedings of the 2004 symposium on Eye tracking research \& applications (San Antonio, Texas: ACM, 2004), 147-154, doi:10.1145/968363.968391, http://portal.acm.org/citation.cfm?id=968391.

12. Diane Kelly and Nicholas J. Belkin, “Display time as implicit feedback: understanding task effects,” in Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval (Sheffield, United Kingdom: ACM, 2004), 377-384, doi:10.1145/1008992.1009057, http://portal.acm.org/citation.cfm?id=1008992.1009057&coll=GUIDE&dl=ACM.

13. Diane Kelly and Jaime Teevan, “Implicit feedback for inferring user preference: a bibliography,” SIGIR Forum 37, no. 2 (2003): 18-28, doi:10.1145/959258.959260.