Interactively optimizing information retrieval systems as a Dueling Bandits Problem

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 1201-1208

  Yue, Yisong ^a   Joachims, Thorsten ^a  

^a Cornell University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMPLICIT FEEDBACK; ONLINE LEARNING; PAIR-WISE COMPARISON; REAL-TIME LEARNING; SIMULATION RESULT; USER BEHAVIORS;

BEHAVIORAL RESEARCH; EDUCATION; INFORMATION RETRIEVAL SYSTEMS; INFORMATION SERVICES; REAL TIME SYSTEMS; ROBOT LEARNING; SEARCH ENGINES;

INFORMATION RETRIEVAL;

EID: 71149114227     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Agichtein, E., Brill, E., & Dumais, S. (2006). Improving Web Search Ranking by Incorporating User Behavior Information. ACM Conference on Information Retrieval (SIGIR) (pp. 19-26).
2. Carterette, B., Bennett, P., Chickering, D. M., & Dumais, S. (2008). Here or There: Preference Judgments for Relevance. European Conference on Information Retrieval (ECIR) (pp. 16-27).
3. Carterette, B., & Jones, R. (2007). Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks. Neural Information Processing Systems (NIPS) (pp. 217-224).
4. Chakrabarti, S., Khanna, R., Sawant, U., & Bat-tacharyya, C. (2008). Structured Learning for Non-Smooth Ranking Losses. ACM Conference on Knowledge Discovery and Data Mining (KDD) (pp. 88-96).
5. Donmez, P., Svore, K., & Burges, C. (2009). On the Local Optimality of LambdaRank. ACM Conference on Information Retrieval (SIGIR).
6. Dupret, G., & Piwowarski, B. (2008). A User Browsing Model to Predict Search Engine Click Data from Past Observations. ACM Conference on Information Retrieval (SIGIR) (pp. 331-338).
7. Flaxman, A., Kalai, A., & McMahan, H. B. (2005). Online Convex Optimization in the Bandit Setting: Gradient Descent Without a Gradient. ACM-SIAM Symposium on Discrete Algorithms (SODA) (pp. 385-394).
8. Joachims, T., Granka, L., Pan, B., Hembrooke, H., Radlinski, F., & Gay, G. (2007). Evaluating the Accuracy of Implicit Feedback from Clicks and Query Reformulations in Web Search. ACM Transactions on Information Systems (TOIS), 25, 7:1-26.
9. Kleinberg, R. (2004). Nearly tight bounds for the continuum-armed bandit problem. Neural Information Processing Systems (NIPS) (pp. 697-704).
10. Langford, J., & Zhang, T. (2007). The Epoch-Greedy Algorithm for Contextual Multi-armed Bandits. Neural Information Processing Systems (NIPS) (pp. 817-824).
11. Pandey, S., Agarwal, D., Chakrabarti, D., & Josi-fovski, V. (2007). Bandits for Taxonomies: A Model-based Approach. SIAM Conference on Data Mining (SDM) (pp. 216-227).
12. Radlinski, F., Kleinberg, R., & Joachims, T. (2008a). Learning Diverse Rankings with Multi-Armed Bandits. International Conference on Machine Learning (ICML) (pp. 784-791).
13. Radlinski, F., Kurup, M., & Joachims, T. (2008b). How Does Clickthrough Data Reflect Retrieval Quality? ACM Conference on Information and Knowledge Management (CIKM) (pp. 43-52).
14. Yue, Y., Broder, J., Kleinberg, R., & Joachims, T. (2009). The K-armed Dueling Bandits Problem. Conference on Learning Theory (COLT).
15. Yue, Y., & Joachims, T. (2008). Predicting Diverse Subsets Using Structural SVMs. International Conference on Machine Learning (ICML) (pp. 1224-1231).
16. Zinkevich, M. (2003). Online Convex Programming and Generalized Infinitesimal Gradient Ascent. International Conference on Machine Learning (ICML) (pp. 928-936).