Online convex optimization in the bandit setting: gradient descent without a gradient
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Learning diverse rankings with multi-armed bandits
Proceedings of the 25th international conference on Machine learning
Predicting diverse subsets using structural SVMs
Proceedings of the 25th international conference on Machine learning
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Structured learning for non-smooth ranking losses
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
How does clickthrough data reflect retrieval quality?
Proceedings of the 17th ACM conference on Information and knowledge management
Here or there: preference judgments for relevance
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Online learning for recency search ranking using real-time user feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Fast active exploration for link-based preference learning using Gaussian processes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Balancing exploration and exploitation in learning to rank online
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Large-scale validation and analysis of interleaved search evaluation
ACM Transactions on Information Systems (TOIS)
The K-armed dueling bandits problem
Journal of Computer and System Sciences
An Online Learning Framework for Refining Recency Search Results with User Click Feedback
ACM Transactions on Information Systems (TOIS)
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
Reusing historical interaction data for faster online learning to rank for IR
Proceedings of the sixth ACM international conference on Web search and data mining
Directing exploratory search: reinforcement learning from user interactions with keywords
Proceedings of the 2013 international conference on Intelligent user interfaces
Lerot: an online learning to rank framework
Proceedings of the 2013 workshop on Living labs for information retrieval evaluation
Relative confidence sampling for efficient on-line ranker evaluation
Proceedings of the 7th ACM international conference on Web search and data mining
| Hi-index | 0.00 |
We present an on-line learning framework tailored towards real-time learning from observed user behavior in search engines and other information retrieval systems. In particular, we only require pairwise comparisons which were shown to be reliably inferred from implicit feedback (Joachims et al., 2007; Radlinski et al., 2008b). We will present an algorithm with theoretical guarantees as well as simulation results.