The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The budgeted maximum coverage problem
Information Processing Letters
Modern Information Retrieval
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
User performance versus precision measures for simple search tasks
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Characterizing the value of personalizing search
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
SoftRank: optimizing non-smooth rank metrics
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Predicting diverse subsets using structural SVMs
Proceedings of the 25th international conference on Machine learning
Bypass rates: reducing query abandonment using negative inferences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Integration of news content into web results
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Learning consensus opinion: mining data from a labeling game
Proceedings of the 18th international conference on World wide web
Interactively optimizing information retrieval systems as a dueling bandits problem
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Regret-based online ranking for a growing digital library
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptation of offline vertical selection predictions in the presence of user feedback
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Picture this: preferences for image search
Proceedings of the ACM SIGKDD Workshop on Human Computation
An Analysis of NP-Completeness in Novelty and Diversity Ranking
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Probabilistic models of ranking novel documents for faceted topic retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Actively predicting diverse search intent from user browsing behaviors
Proceedings of the 19th international conference on World wide web
Estimating interference in the QPRP for subtopic retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Evaluating search systems using result page context
Proceedings of the third symposium on Information interaction in context
Learning to rank relevant and novel documents through user feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Approximation algorithms for diversified search ranking
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
A comparative analysis of cascade measures for novelty and diversity
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the fourth ACM international conference on Web search and data mining
Towards a collection-based results diversification
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Effective large scale text retrieval via learning risk-minimization and dependency-embedded model
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
An analysis of NP-completeness in novelty and diversity ranking
Information Retrieval
Consideration set generation in commerce search
Proceedings of the 20th international conference on World wide web
Efficient diversification of web search results
Proceedings of the VLDB Endowment
Balancing exploration and exploitation in learning to rank online
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Diversity in ranking via resistive graph centers
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Selecting a comprehensive set of reviews
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
A probabilistic method for inferring preferences from clicks
Proceedings of the 20th ACM international conference on Information and knowledge management
Intent-based diversification of web search results: metrics and algorithms
Information Retrieval
Dynamical information retrieval modelling: a portfolio-armed bandit machine approach
Proceedings of the 21st international conference companion on World Wide Web
Max-Sum diversification, monotone submodular functions and dynamic updates
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
A model for mining relevant and non-redundant information
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Approximating low-dimensional coverage problems
Proceedings of the twenty-eighth annual symposium on Computational geometry
Online learning to diversify from implicit feedback
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
LogUCB: an explore-exploit algorithm for comments recommendation
Proceedings of the 21st ACM international conference on Information and knowledge management
Leaving so soon?: understanding and predicting web search abandonment rationales
Proceedings of the 21st ACM international conference on Information and knowledge management
The effect of aggregated search coherence on search behavior
Proceedings of the 21st ACM international conference on Information and knowledge management
Measuring the coverage and redundancy of information search services on e-commerce platforms
Electronic Commerce Research and Applications
mNIR: diversifying search results based on a mixture of novelty, intention and relevance
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Directing exploratory search: reinforcement learning from user interactions with keywords
Proceedings of the 2013 international conference on Intelligent user interfaces
A unified search federation system based on online user feedback
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive exploratory search for multi page search results
Proceedings of the 22nd international conference on World Wide Web
Ranked bandits in metric spaces: learning diverse rankings over large document collections
The Journal of Machine Learning Research
Augmenting web search surrogates with images
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Exploratory and interactive daily deals recommendation
Proceedings of the 7th ACM conference on Recommender systems
Learning to rank for recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Fidelity, Soundness, and Efficiency of Interleaved Comparison Methods
ACM Transactions on Information Systems (TOIS)
Latent dirichlet allocation based diversified retrieval for e-commerce search
Proceedings of the 7th ACM international conference on Web search and data mining
Machine Learning
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Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two online learning algorithms that directly learn a diverse ranking of documents based on users' clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. Moreover, one of our algorithms asymptotically achieves optimal worst-case performance even if users' interests change.