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Exploration-exploitation tradeoff in interactive relevance feedback

Maryam Karimzadehgan, ChengXiang Zhai
<span title="">2010</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6g37zvjwwrhv3dizi6ffue642m" style="color: black;">Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM &#39;10</a> </i> &nbsp;
Optimizing such an exploration-exploitation tradeoff is key to the optimization of the overall utility of relevance feedback to a user in the entire session of relevance feedback.  ...  Experiment results show that the proposed learning approach can effectively optimize the exploration-exploitation tradeoff and outperforms the traditional relevance feedback approach which only does exploitation  ...  relevance feedback approach which only does exploitation without exploration.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1871437.1871631">doi:10.1145/1871437.1871631</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cikm/KarimzadehganZ10.html">dblp:conf/cikm/KarimzadehganZ10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/et67cgnmfzfw3ccszpcepiy64u">fatcat:et67cgnmfzfw3ccszpcepiy64u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20161223231920/http://sifaka.cs.uiuc.edu/~mkarimz2/cikm112s-karimzadehgan.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/17/dd/17dd395c41eabec9ef73598fa095ca497b287ff3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1871437.1871631"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Explore-exploit in top-N recommender systems via Gaussian processes

Hastagiri P. Vanchinathan, Isidor Nikolic, Fabio De Bona, Andreas Krause
<span title="">2014</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vxvef3lblbcxbliuugiju5sify" style="color: black;">Proceedings of the 8th ACM Conference on Recommender systems - RecSys &#39;14</a> </i> &nbsp;
We propose the CGPRANK algorithm, which exploits prior information specified in terms of a Gaussian process kernel function, which allows to share feedback in three ways: Between positions in a list, between  ...  In our experiments, our CGPRANK approach significantly outperforms state-of-the-art multi-armed bandit and learning-to-rank methods, with an 18% increase in clicks.  ...  Learning this optimal ordering leads to an "explore-exploit" tradeoff, where we need to gather information about the effectiveness of orderings, while at the same time maximizing conversions based Permission  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2645710.2645733">doi:10.1145/2645710.2645733</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/recsys/VanchinathanNBK14.html">dblp:conf/recsys/VanchinathanNBK14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fcg3jbnvhvbcvga5l6jg4qxjeq">fatcat:fcg3jbnvhvbcvga5l6jg4qxjeq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170829225647/https://las.inf.ethz.ch/files/vanchinathan14explore.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5b/e0/5be0ae8724ac03cfc61fe360373083a786d8fef4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2645710.2645733"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Improving the Initial Image Retrieval Set by Inter-Query Learning with One-Class SVMs [chapter]

Iker Gondra, Douglas R. Heisterkamp, Jing Peng
<span title="">2003</span> <i title="Springer Berlin Heidelberg"> Intelligent Systems Design and Applications </i> &nbsp;
Relevance Feedback attempts to reduce the semantic gap between a user's perception of similarity and a feature-based representation of an image by asking the user to provide feedback regarding the relevance  ...  In order to learn the set membership of a user's query concept, a one-class SVM maps the relevant or training images into a nonlinearly transformed kernel-induced feature space and attempts to include  ...  The approach that we take is to combine exploitation and exploration while, at the same time, attempting to maximize the number of relevant images that are presented to the user.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-44999-7_38">doi:10.1007/978-3-540-44999-7_38</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3o4qrzxv4zgo3mxlvuqdzyrw6e">fatcat:3o4qrzxv4zgo3mxlvuqdzyrw6e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809042507/http://cs.okstate.edu/~doug/publications/iker_isda03.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/22/5c/225ce5ef716927d3570f3462d78cf53b26ce4123.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-44999-7_38"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Unpacking the exploration–exploitation tradeoff: A synthesis of human and animal literatures

Katja Mehlhorn, Ben R. Newell, Peter M. Todd, Michael D. Lee, Kate Morgan, Victoria A. Braithwaite, Daniel Hausmann, Klaus Fiedler, Cleotilde Gonzalez
<span title="">2015</span> <i title="American Psychological Association (APA)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dsm6dy2cnvcg3pdbpff7727mli" style="color: black;">Decision</a> </i> &nbsp;
The balance required in these situations is commonly referred to as the exploration-exploitation tradeoff.  ...  Here, we integrate findings from these and other often-isolated literatures in order to gain a better understanding of the possible tradeoffs between exploration and exploitation, and we propose new theoretical  ...  Many approaches to the analysis of decision behavior would characterize these scenarios as representations of a tradeoff between exploration and exploitation (e.g., in reinforcement learning [RL] and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1037/dec0000033">doi:10.1037/dec0000033</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iyzharxp2jf2ti5hyczt6hjnuq">fatcat:iyzharxp2jf2ti5hyczt6hjnuq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180107042302/http://www.ai.rug.nl:80/~katja/pubs/assets/Mehlhorn2015Unpacking.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c5/fe/c5fe24fb5d65bc6a7c3ca61fc89dae298c3a1fdc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1037/dec0000033"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Practical learning from one-sided feedback

D. Sculley
<span title="">2007</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqqihtxlu5bvfaqxjyvqcob35a" style="color: black;">Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD &#39;07</a> </i> &nbsp;
The other method, somewhat surprisingly, is the use of margin-based learners without modification, which we show combines implicit active learning and a greedy strategy to managing the exploration exploitation  ...  In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class.  ...  Exploring other active learning methods in one-sided feedback problems remains for future work. Exploration/Exploitation Tradeoff.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1281192.1281258">doi:10.1145/1281192.1281258</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/kdd/Sculley07.html">dblp:conf/kdd/Sculley07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wwymra5hhjdlzbb3i2fmz45p3y">fatcat:wwymra5hhjdlzbb3i2fmz45p3y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20161022190808/http://www.eecs.tufts.edu:80/~dsculley/papers/oneSidedFeedback.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0e/65/0e6562bdc1f09c63f4e0d56ec867dc0a82eade39.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1281192.1281258"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Exploitation and exploration in a performance based contextual advertising system

Wei Li, Xuerui Wang, Ruofei Zhang, Ying Cui, Jianchang Mao, Rong Jin
<span title="">2010</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqqihtxlu5bvfaqxjyvqcob35a" style="color: black;">Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD &#39;10</a> </i> &nbsp;
The exploitation and exploration (EE) tradeoff has been extensively studied in the reinforcement learning community, however, not been paid much attention in online advertising until recently.  ...  The streaming nature of online data inevitably makes an advertising system choose between maximizing its expected revenue according to its current knowledge in short term (exploitation) and trying to learn  ...  One problem with the -greedy algorithm is how to decide the optimal , a critical tradeoff parameter between exploitation and exploration.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1835804.1835811">doi:10.1145/1835804.1835811</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/kdd/LiWZCMJ10.html">dblp:conf/kdd/LiWZCMJ10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7rupiwrdovcpjepj55naiedeje">fatcat:7rupiwrdovcpjepj55naiedeje</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150513225229/http://www.cs.cmu.edu:80/~xuerui/papers/ee.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a3/2e/a32e477608c3b731739e9bb24aea74289acf5bc6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1835804.1835811"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

A unified optimization framework for robust pseudo-relevance feedback algorithms

Joshua V. Dillon, Kevyn Collins-Thompson
<span title="">2010</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6g37zvjwwrhv3dizi6ffue642m" style="color: black;">Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM &#39;10</a> </i> &nbsp;
We present a flexible new optimization framework for finding effective, reliable pseudo-relevance feedback models that unifies existing complementary approaches in a principled way.  ...  We compare the effectiveness of a unified algorithm to existing methods by examining iterative performance and risk-reward tradeoffs.  ...  Acknowledgements We thank Guy Lebanon for his valuable feedback, Tao Tao for helpful discussions of the TZ model, and several anonymous reviewers for their comments. References  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1871437.1871573">doi:10.1145/1871437.1871573</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cikm/DillonC10.html">dblp:conf/cikm/DillonC10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w3r557nwdrdv7cb4q4a3xvjxu4">fatcat:w3r557nwdrdv7cb4q4a3xvjxu4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170830043957/http://www-personal.umich.edu/~kevynct/pubs/cikm1206-dillon.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/06/c3/06c37a9b7c186712b2582e07a35e112320d18e20.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1871437.1871573"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Semi-supervised distance metric learning for Collaborative Image Retrieval

Steven C.H. Hoi, Wei Liu, Shih-Fu Chang
<span title="">2008</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2008 IEEE Conference on Computer Vision and Pattern Recognition</a> </i> &nbsp;
Hence, relevance feedback has been adopted as a promising approach to improve the search performance.  ...  In this paper, we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called "Collaborative Image Retrieval" (CIR).  ...  Acknowledgments The work was supported in part by Singapore Academic Research Fund Tier 1 Grant (Project No. RG67/07).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2008.4587351">doi:10.1109/cvpr.2008.4587351</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/HoiLC08.html">dblp:conf/cvpr/HoiLC08</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d3jmrgmhejhcpj3nb5y6uka4pm">fatcat:d3jmrgmhejhcpj3nb5y6uka4pm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171025223742/https://core.ac.uk/download/pdf/22871853.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/51/7a/517aef8aee0873c652611ddaa18509f1d3c21fd3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2008.4587351"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

One-bit Feedback Exponential Learning for Beam Alignment in Mobile mmWave

Irched Chafaa, E. Veronica Belmega, Merouane Debbah
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Our MEXP3 comes with optimal theoretical guarantees in terms of asymptotic regret.  ...  Building on the well known exponential weights algorithm (EXP3) and by exploiting the structure and sparsity of the mmWave channel, we propose a modified (MEXP3) policy that requires solely one-bit of  ...  Notice that both parameters have to be very carefully tuned to optimize the tradeoff exploration vs. exploitation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3033419">doi:10.1109/access.2020.3033419</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/43xybi7a2rcotnig4tpalqhcti">fatcat:43xybi7a2rcotnig4tpalqhcti</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201024215934/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09237929.pdf?tp=&amp;arnumber=9237929&amp;isnumber=6514899&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7c/c9/7cc9d86c75bb75cb2be5a429dd5b01ac1c596f0b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.3033419"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Advertising keyword generation using active learning

Hao Wu, Guang Qiu, Xiaofei He, Yuan Shi, Mingcheng Qu, Jing Shen, Jiajun Bu, Chun Chen
<span title="">2009</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s4hirppq3jalbopssw22crbwwa" style="color: black;">Proceedings of the 18th international conference on World wide web - WWW &#39;09</a> </i> &nbsp;
We formulate the ranking of relevant terms as a supervised learning problem and suggest new terms for the seed by leveraging user relevance feedback information.  ...  Active learning is employed to select the most informative samples from a set of candidate terms for user labeling.  ...  Recent related work tends to exploit semantic relation- * Supported by the National Key Technology R&D Program In this paper, we propose an interactive model to explore relevance feedback for keyword generation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1526709.1526873">doi:10.1145/1526709.1526873</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/www/WuQHSQSBC09.html">dblp:conf/www/WuQHSQSBC09</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gorfj5qpcbex7dyyxo7kjikhsa">fatcat:gorfj5qpcbex7dyyxo7kjikhsa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170811094620/http://people.cs.uchicago.edu/~xiaofei/WWW2009-Wu.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/52/aa/52aa87f1202cdf3024c5365e6cce7a62f7d5f2e7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1526709.1526873"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Online learning for recency search ranking using real-time user feedback

Taesup Moon, Lihong Li, Wei Chu, Ciya Liao, Zhaohui Zheng, Yi Chang
<span title="">2010</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6g37zvjwwrhv3dizi6ffue642m" style="color: black;">Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM &#39;10</a> </i> &nbsp;
Although such a batch-learning framework has been tremendously successful in commercial search engines, in scenarios where relevance of documents to a query changes over time, such as ranking recent documents  ...  Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query.  ...  We use online learning, explore-exploit techniques, and feature-based scoring to address these issues, respectively.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1871437.1871657">doi:10.1145/1871437.1871657</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cikm/MoonLCLZC10.html">dblp:conf/cikm/MoonLCLZC10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/w5fssy4rlzdpheudjlpd72mqfa">fatcat:w5fssy4rlzdpheudjlpd72mqfa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170705133521/http://research.cs.rutgers.edu/~lihong/pub/Moon10Online.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/84/98/8498b17163e1afba16486c0841e1cb9ac3ce2d3b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1871437.1871657"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

AIDE

Yanlei Diao, Kyriaki Dimitriadou, Zhan Li, Wenzhao Liu, Olga Papaemmanouil, Kemi Peng, Liping Peng
<span title="2015-08-01">2015</span> <i title="VLDB Endowment"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p6rqwwpkkjbcldejepcehaalby" style="color: black;">Proceedings of the VLDB Endowment</a> </i> &nbsp;
In this demonstration we introduce AIDE , a system that automates these exploration tasks.  ...  In our demonstration, conference attendees will see AIDE in action for a variety of exploration tasks on real-world datasets.  ...  ACKNOWLEDGMENTS This work was funded in part by NSF under grants IIS-1253196 and IIS-1218524.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14778/2824032.2824112">doi:10.14778/2824032.2824112</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qyq4ml3opfhe7o53rwrhwfutjq">fatcat:qyq4ml3opfhe7o53rwrhwfutjq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160128191744/http://www.vldb.org/pvldb/vol8/p1964-Diao.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0d/a2/0da21528180670d94a3f63e70ccc881e1cb7abaa.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14778/2824032.2824112"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Near-Term Liability of Exploitation: Exploration and Exploitation in Multistage Problems

Christina Fang, Daniel Levinthal
<span title="">2009</span> <i title="Institute for Operations Research and the Management Sciences (INFORMS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vj4hqed3c5gjbp2jght7hx3eey" style="color: black;">Organization science (Providence, R.I.)</a> </i> &nbsp;
The classic tradeoff between exploration and exploitation reflects the tension between gaining new information about alternatives to improve future returns and using the information currently available  ...  By considering these issues in the context of a multi-stage, as opposed to a repeated, problem environment, we show that exploratory behavior has value quite apart from its role in revising beliefs.  ...  Less exploitative strategies are shown to lead to a more robust approach to problem solving in a multi-stage setting.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1287/orsc.1080.0376">doi:10.1287/orsc.1080.0376</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r6i4ntz5c5bntbly6hx7uhtvxa">fatcat:r6i4ntz5c5bntbly6hx7uhtvxa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190308061826/http://pdfs.semanticscholar.org/ee52/f91610bb9726b4e59b2a662df4137aaa0e16.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ee/52/ee52f91610bb9726b4e59b2a662df4137aaa0e16.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1287/orsc.1080.0376"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Personalized Recommendation via Parameter-Free Contextual Bandits

Liang Tang, Yexi Jiang, Lei Li, Chunqiu Zeng, Tao Li
<span title="">2015</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ibcfmixrofb3piydwg5wvir3t4" style="color: black;">Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR &#39;15</a> </i> &nbsp;
In this paper, we formulate personalized recommendation as a contextual bandit problem to solve the exploration/exploitation dilemma.  ...  Specifically in our work, we propose a parameter-free bandit strategy, which employs a principled resampling approach called online bootstrap, to derive the distribution of estimated models in an online  ...  in terms of the exploration/exploitation tradeoff.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2766462.2767707">doi:10.1145/2766462.2767707</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sigir/TangJLZL15.html">dblp:conf/sigir/TangJLZL15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gyuie4kilngm5draxc7tivnwlq">fatcat:gyuie4kilngm5draxc7tivnwlq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150714191409/http://users.cis.fiu.edu:80/~ltang002/papers/sigir2015-tang.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7d/f2/7df221da0c0fd12ca948ebbc67885204d27eff7d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2766462.2767707"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Contextual Online Learning for Multimedia Content Aggregation

Cem Tekin, Mihaela van der Schaar
<span title="">2015</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sbzicoknnzc3tjljn7ifvwpooi" style="color: black;">IEEE transactions on multimedia</a> </i> &nbsp;
Our proposed content aggregation algorithm is able to learn online what content to gather and how to match content and users by exploiting similarities between consumer types.  ...  A key challenge for such systems is to accurately predict what type of content each of its consumers prefers in a certain context, and adapt these predictions to the evolving consumers' preferences, contexts  ...  The algorithm is designed in a way to achieve optimal tradeoff between the size of the partition and the past observations that can be used together to learn the relevance scores.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tmm.2015.2403234">doi:10.1109/tmm.2015.2403234</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2uklijueuzexdnb74o7xb6bobe">fatcat:2uklijueuzexdnb74o7xb6bobe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170830032722/http://www.chennaisunday.com/2015DOTNET/Contextual%20Online%20Learning%20for%20Multimedia.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ae/50/ae508082880748a8a85beafcd29537ef3e28ac71.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tmm.2015.2403234"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>
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