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Scalable Bayesian Preference Learning for Crowds [article]

Edwin Simpson, Iryna Gurevych
<span title="2019-12-11">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels.  ...  We address these challenges by combining matrix factorisation with Gaussian processes, using a Bayesian approach to account for uncertainty arising from noisy and sparse data.  ...  We would like to thank the journal editors and reviewers for their valuable feedback.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.01987v2">arXiv:1912.01987v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wpfk75m265atraoicmfbrlcnce">fatcat:wpfk75m265atraoicmfbrlcnce</a> </span>
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Scalable Bayesian preference learning for crowds

Edwin Simpson, Iryna Gurevych
<span title="2020-02-06">2020</span> <i title="Society for Mining, Metallurgy and Exploration Inc."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/h4nnd7sxwzcwhetu5qkjbcdh6u" style="color: black;">Machine Learning</a> </i> &nbsp;
We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels.  ...  We make our software publicly available for future work (https ://githu b.com/UKPLa b/tacl2 018-prefe rence -convi ncing /tree/crowd GPPL).  ...  We would like to thank the journal editors and reviewers for their valuable feedback.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10994-019-05867-2">doi:10.1007/s10994-019-05867-2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wcix2mt24jgnbn3j3rhotlwfby">fatcat:wcix2mt24jgnbn3j3rhotlwfby</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200509202350/https://link.springer.com/content/pdf/10.1007/s10994-019-05867-2.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/95/d4/95d47771807d4aaf962786c0ec57c47dce389685.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10994-019-05867-2"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Stagewise learning for noisy k-ary preferences

Yuangang Pan, Bo Han, Ivor W. Tsang
<span title="2018-05-11">2018</span> <i title="Springer Nature America, Inc"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/h4nnd7sxwzcwhetu5qkjbcdh6u" style="color: black;">Machine Learning</a> </i> &nbsp;
To ensure reliability, we introduce an uncertainty vector for each crowd worker in COUPLE, which recovers the ground truth of the noisy preferences with a certain probability.  ...  To address both of these challenges, we propose a reliable CrowdsOUrced Plackett-LucE (COUPLE) model combined with an efficient Bayesian learning technique.  ...  The online generalized Bayesian moment matching (OnlineGBMM) for COUPLE Bayesian moment matching (Jaini et al. 2016 ) is a scalable technique for estimating a model's parameters.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10994-018-5716-2">doi:10.1007/s10994-018-5716-2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/omnc7orybndt3hqk5bnlyeqvcq">fatcat:omnc7orybndt3hqk5bnlyeqvcq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200310095448/https://link.springer.com/content/pdf/10.1007%2Fs10994-018-5716-2.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/8f/45/8f453c10f1d6c1082c766502332ec56fc54b7f04.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10994-018-5716-2"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Community-based bayesian aggregation models for crowdsourcing

Matteo Venanzi, John Guiver, Gabriella Kazai, Pushmeet Kohli, Milad Shokouhi
<span title="">2014</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s4hirppq3jalbopssw22crbwwa" style="color: black;">Proceedings of the 23rd international conference on World wide web - WWW &#39;14</a> </i> &nbsp;
To mitigate this issue, we propose a novel community-based Bayesian label aggregation model, CommunityBCC, which assumes that crowd workers conform to a few different types, where each type represents  ...  Prior work has focused on modeling the reliability of individual workers, for instance, by way of confusion matrices, and using these latent traits to estimate the true labels more accurately.  ...  ACKNOWLEDGMENTS The authors gratefully thank Tom Minka for the support and discussion about the model. Matteo Venanzi would also like to thank Oliver Parson for early discussions about this work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2566486.2567989">doi:10.1145/2566486.2567989</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/www/VenanziGKKS14.html">dblp:conf/www/VenanziGKKS14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fj4ol7n3z5gj5m562d4jlvhcbm">fatcat:fj4ol7n3z5gj5m562d4jlvhcbm</a> </span>
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Language Understanding in the Wild

Edwin D. Simpson, Matteo Venanzi, Steven Reece, Pushmeet Kohli, John Guiver, Stephen J. Roberts, Nicholas R. Jennings
<span title="">2015</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s4hirppq3jalbopssw22crbwwa" style="color: black;">Proceedings of the 24th International Conference on World Wide Web - WWW &#39;15</a> </i> &nbsp;
To overcome this problem, we present a novel Bayesian approach to language understanding that relies on aggregated crowdsourced judgements.  ...  Compared to the six state-of-the-art methods, we reduce by up to 67% the number of crowd responses required to achieve comparable accuracy.  ...  We introduce a scalable Bayesian inference mechanism for BCCWords, which learns posterior distributions over the workers' reliability and document classifications, given the documents' text features and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2736277.2741689">doi:10.1145/2736277.2741689</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/www/SimpsonVRKGRJ15.html">dblp:conf/www/SimpsonVRKGRJ15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hsrmqa4zw5f5lngrecrhodixkq">fatcat:hsrmqa4zw5f5lngrecrhodixkq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201106174409/https://research-information.bris.ac.uk/ws/files/235037745/WWW15_BCCWords_ES3.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/c1/d8/c1d8ac488728d7fda71dc48da35d2d3c13706cf6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2736277.2741689"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Foreword: special issue for the journal track of the 9th Asian Conference on Machine Learning (ACML 2017)

Wee Sun Lee, Robert J. Durrant
<span title="2017-12-21">2017</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/h4nnd7sxwzcwhetu5qkjbcdh6u" style="color: black;">Machine Learning</a> </i> &nbsp;
An online Bayesian inference method is proposed to make the method scalable, with good results shown in experiments.  ...  The paper "Robust Plackett-Luce Model for k-ary Crowdsourced Preferences", by Bo Han, Yuangang Pan, and Ivor W. Tsang studies the problem of aggregrating the ranking of k-ary preferences.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10994-017-5691-z">doi:10.1007/s10994-017-5691-z</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wzshsay22jbsxnqfq2wqjj66ka">fatcat:wzshsay22jbsxnqfq2wqjj66ka</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180728143643/https://link.springer.com/content/pdf/10.1007%2Fs10994-017-5691-z.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/b9/0b/b90b6c21fc3a2abb1142522e3e4a3abacbc42958.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10994-017-5691-z"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

On the Design of Strategic Task Recommendations for Sustainable Crowdsourcing-Based Content Moderation [article]

Sainath Sanga, Venkata Sriram Siddhardh Nadendla
<span title="2021-06-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Crowdsourcing-based content moderation is a platform that hosts content moderation tasks for crowd workers to review user submissions (e.g. text, images and videos) and make decisions regarding the admissibility  ...  to the worker's cognitive atrophy rate and task preferences.  ...  This can severely impair the scalability of the proposed system across a large number of crowd workers.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.02708v1">arXiv:2106.02708v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5bchkdvs2jfzlosbralos2wspy">fatcat:5bchkdvs2jfzlosbralos2wspy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210609124306/https://arxiv.org/pdf/2106.02708v1.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/0c/74/0c74b2479e29a34db7d77a957ac131d672063f33.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.02708v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning

Billy Okal, Kai O. Arras
<span title="">2016</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nytnrt3gtzbixld5r6a4talbky" style="color: black;">2016 IEEE International Conference on Robotics and Automation (ICRA)</a> </i> &nbsp;
We thus develop a flexible graphbased representation able to capture relevant task structure and extend Bayesian inverse reinforcement learning to use sampled trajectories from this representation.  ...  In this paper, we address this task using a learning approach that enables a mobile robot to acquire navigation behaviors from demonstrations of socially normative human behavior.  ...  We then extend an existing Bayesian IRL algorithm to realise a scalable variant that takes advantage of the new representation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icra.2016.7487452">doi:10.1109/icra.2016.7487452</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icra/OkalA16.html">dblp:conf/icra/OkalA16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yp6veapukfgd3bcrre5xsphre4">fatcat:yp6veapukfgd3bcrre5xsphre4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170401122009/http://www.spencer.eu:80/papers/okalICRA16.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/4a/b3/4ab360cbafaf4fb499904455e4f434495b8d2919.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icra.2016.7487452"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Survey Paper on Recommendation System for Tourist Reviews using Aspect Based Sentiment Classification

Kande Trupti V
<span title="2021-08-15">2021</span> <i title="International Journal for Research in Applied Science and Engineering Technology (IJRASET)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hsp44774azcezeyiq4kuzpfh5a" style="color: black;">International Journal for Research in Applied Science and Engineering Technology</a> </i> &nbsp;
So that, a major challenge faced by tourism sector is to utilize the accumulate information for detecting tourist preferences.  ...  Unfortunately, some user's comments are irrelevant and complex for understanding and long-winded these become hard for recommendation.  ...  , And developed a scalable optimization algorithm for jointly learning latent factors and hyper parameters [4] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22214/ijraset.2021.37450">doi:10.22214/ijraset.2021.37450</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ke5mfgfz4nfnjhma76wbrfko2m">fatcat:ke5mfgfz4nfnjhma76wbrfko2m</a> </span>
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Hierarchical Bayesian Nonparametric Approach to Modeling and Learning the Wisdom of Crowds of Urban Traffic Route Planning Agents

Jiangbo Yu, Kian Hsiang Low, Ali Oran, Patrick Jaillet
<span title="">2012</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wl6t75bdqrdlrgsukwo2coosly" style="color: black;">2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology</a> </i> &nbsp;
This paper presents a hierarchical Bayesian non-parametric approach to efficient and scalable route prediction that can harness the wisdom of crowds of route planning agents by aggregating their sequential  ...  Route prediction is important to analyzing and understanding the route patterns and behavior of traffic crowds.  ...  CONCLUSION This paper describes a hierarchical Bayesian nonparametric model to aggregate the sequential decisions from the crowds of route planning agents for performing efficient and scalable route prediction  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/wi-iat.2012.216">doi:10.1109/wi-iat.2012.216</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/iat/YuLOJ12.html">dblp:conf/iat/YuLOJ12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f56nkzbitjfpvcxi6xphbahxye">fatcat:f56nkzbitjfpvcxi6xphbahxye</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170819192108/http://dspace.mit.edu/bitstream/handle/1721.1/86892/Jaillet_Hierarchical%20bayesian.pdf?sequence=1" 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/7b/b7/7bb7b21781f6744c202525d280cfb878f8eff491.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/wi-iat.2012.216"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications

Adnan Akbar, George Kousiouris, Haris Pervaiz, Juan Sancho, Paula Ta-Shma, Francois Carrez, Klaus Moessner
<span title="">2018</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;
In this context, we propose a two layer architecture for analyzing IoT data.  ...  In the second layer, we extend state-ofthe-art event processing using Bayesian networks (BNs) in order to take uncertainty into account while detecting complex events.  ...  We would like to acknowledge the support of the University of Surrey 5GIC members (http://www.surrey.ac.uk/5gic) for this work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2018.2804623">doi:10.1109/access.2018.2804623</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rd3mwnsccrdbfebbpjbl4rynyy">fatcat:rd3mwnsccrdbfebbpjbl4rynyy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180720105136/http://epubs.surrey.ac.uk/845779/1/Real-time%20Probabilistic%20Data%20Fusion%20for%20Large-scale%20IoT%20Applications.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/a1/2c/a12ca02e25c9599c303ff15d60605c557481f75b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2018.2804623"> <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>

Eliciting Worker Preference for Task Completion [article]

Mohammadreza Esfandiari, Senjuti Basu Roy, Sihem Amer-Yahia
<span title="2018-01-10">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Explicit elicitation can indeed help to build more accurate worker models for task completion that captures the evolving nature of worker preferences.  ...  We design a worker model whose accuracy is improved iteratively by requesting preferences for task factors such as required skills, task payment, and task relevance.  ...  To the best of our knowledge, we present the first principled solution for explicit preference elicitation and rigorously study scalability. VI.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1801.03233v1">arXiv:1801.03233v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2qmjb5xdlvhflp3gxyev663zry">fatcat:2qmjb5xdlvhflp3gxyev663zry</a> </span>
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People search and activity mining in large-scale community-contributed photos

Yan-Ying Chen
<span title="">2012</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lahlxihmo5fhzpexw7rundu24u" style="color: black;">Proceedings of the 20th ACM international conference on Multimedia - MM &#39;12</a> </i> &nbsp;
Most importantly, this framework effectively relieves costly annotation efforts and ensures scalability for large-scale media.  ...  In this work, we aim at learning facial attributes (gender, race, age, etc.) by these publicly available photos and exploiting the detected facial attributes for locating designated persons, profiling  ...  facial attributes for retrieving photos containing designated persons (as (b)) and mining user preferences for personalized/group recommendation (as (c)).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2393347.2396498">doi:10.1145/2393347.2396498</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/mm/Chen12.html">dblp:conf/mm/Chen12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zhtpqh5zdbfwrpwfgswjzz5veq">fatcat:zhtpqh5zdbfwrpwfgswjzz5veq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809033112/http://www.cmlab.csie.ntu.edu.tw/~yanying/paper/dsp006-chen.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/af/51/af515b40cce77cd6b01cde12c72d06a3b2169512.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2393347.2396498"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

2020 Index IEEE Transactions on Services Computing Vol. 13

<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zve2it4dovamndfi3ijj4n3dzi" style="color: black;">IEEE Transactions on Services Computing</a> </i> &nbsp;
-Dec. 2020 985-998 Learning User Preference from Heterogeneous Information for Store-Type Recommendation. Chen, Y., +, TSC Nov.  ...  -Feb. 2020 144-157 Mobile handsets Learning User Preference from Heterogeneous Information for Store-Type Recommendation. Chen, Y., +, TSC Nov.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tsc.2021.3055723">doi:10.1109/tsc.2021.3055723</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eumbihmezvehxdfbmlp6ufzkwe">fatcat:eumbihmezvehxdfbmlp6ufzkwe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210211035212/https://ieeexplore.ieee.org/ielx7/4629386/9346133/09346134.pdf?tp=&amp;arnumber=9346134&amp;isnumber=9346133&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/eb/9c/eb9cf6fdf9172030b5abe5f43b4791c6f264e920.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tsc.2021.3055723"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Context Embedding Networks [article]

Kun Ho Kim, Oisin Mac Aodha, Pietro Perona
<span title="2018-03-29">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Existing models for learning embeddings from the crowd typically make simplifying assumptions such as all individuals estimate similarity using the same criteria, the list of criteria is known in advance  ...  In addition to learning interpretable embeddings from images, CENs also model worker biases for different attributes along with the visual context i.e. the visual attributes highlighted by a set of images  ...  Acknowledgements We thank Google for supporting the Visipedia project and AWS Research Credits for their donation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1710.01691v3">arXiv:1710.01691v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bnfwusspyzgabmhjwi75x54ic4">fatcat:bnfwusspyzgabmhjwi75x54ic4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200929033513/https://arxiv.org/pdf/1710.01691v3.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/e9/b3/e9b3e5354986459b4b8403155e5148d6b602cdd0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1710.01691v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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