2021
[1]
A. Ghazimatin, S. Pramanik, R. Saha Roy, and G. Weikum, “ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models,” 2021. [Online]. Available: https://arxiv.org/abs/2102.09388. (arXiv: 2102.09388)
Abstract
System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.
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@online{Ghazimatin_2102.09388, TITLE = {{ELIXIR}: {L}earning from User Feedback on Explanations to Improve Recommender Models}, AUTHOR = {Ghazimatin, Azin and Pramanik, Soumajit and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2102.09388}, EPRINT = {2102.09388}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.}, }
Endnote
%0 Report %A Ghazimatin, Azin %A Pramanik, Soumajit %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0309-B %U https://arxiv.org/abs/2102.09388 %D 2021 %X System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback. %K Computer Science, Information Retrieval, cs.IR,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Learning, cs.LG
[2]
A. Ghazimatin, S. Pramanik, R. Saha Roy, and G. Weikum, “ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models,” in Proceedings of The Web Conference 2021 (WWW 2021), Ljubljana, Slovenia. (Accepted/in press)
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@inproceedings{Ghazimatin_WWW21, TITLE = {{ELIXIR}: {L}earning from User Feedback on Explanations to Improve Recommender Models}, AUTHOR = {Ghazimatin, Azin and Pramanik, Soumajit and Saha Roy, Rishiraj and Weikum, Gerhard}, LANGUAGE = {eng}, DOI = {10.1145/3442381.3449848}, PUBLISHER = {ACM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of The Web Conference 2021 (WWW 2021)}, ADDRESS = {Ljubljana, Slovenia}, }
Endnote
%0 Conference Proceedings %A Ghazimatin, Azin %A Pramanik, Soumajit %A Saha Roy, Rishiraj %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0303-1 %R 10.1145/3442381.3449848 %D 2021 %B 30th The Web Conference %Z date of event: 2021-04-30 - %C Ljubljana, Slovenia %B Proceedings of The Web Conference 2021 %I ACM
[3]
A. Guimarães and G. Weikum, “X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums,” in Proceedings of the Fifteenth International Conference on Web and Social Media (ICWSM 2021), Atlanta, GA, USA. (Accepted/in press)
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@inproceedings{Guimaraes_ICWSM2021, TITLE = {X-Posts Explained: {A}nalyzing and Predicting Controversial Contributions in Thematically Diverse {R}eddit Forums}, AUTHOR = {Guimar{\~a}es, Anna and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {AAAI}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the Fifteenth International Conference on Web and Social Media (ICWSM 2021)}, ADDRESS = {Atlanta, GA, USA}, }
Endnote
%0 Conference Proceedings %A Guimarães, Anna %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T X-Posts Explained: Analyzing and Predicting Controversial Contributions in Thematically Diverse Reddit Forums : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0345-7 %D 2021 %B 15th International Conference on Web and Social Media %Z date of event: 2021-06-07 - 2021-06-10 %C Atlanta, GA, USA %B Proceedings of the Fifteenth International Conference on Web and Social Media %I AAAI
[4]
V. T. Ho, K. Pal, and G. Weikum, “QuTE: Answering Quantity Queries from Web Tables,” in SIGMOD 2021, Xi’an, Shaanxi, China. (Accepted/in press)
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@inproceedings{Thinh_SIG21, TITLE = {Qu{TE}: Answering Quantity Queries from Web Tables}, AUTHOR = {Ho, Vinh Thinh and Pal, Koninika and Weikum, Gerhard}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGMOD 2021}, ADDRESS = {Xi'an, Shaanxi, China}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Pal, Koninika %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T QuTE: Answering Quantity Queries from Web Tables : %G eng %U http://hdl.handle.net/21.11116/0000-0008-052E-0 %D 2021 %B SIGMOD 2021 %Z date of event: 2021-06-19 - 2021-06-25 %C Xi'an, Shaanxi, China %B SIGMOD 2021
[5]
V. T. Ho, K. Pal, S. Razniewski, K. Berberich, and G. Weikum, “Extracting Contextualized Quantity Facts from Web Tables,” in Proceedings of The Web Conference 2021 (WWW 2021), Ljubljana, Slovenia. (Accepted/in press)
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@inproceedings{Thinh_WWW21, TITLE = {Extracting Contextualized Quantity Facts from Web Tables}, AUTHOR = {Ho, Vinh Thinh and Pal, Koninika and Razniewski, Simon and Berberich, Klaus and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {ACM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of The Web Conference 2021 (WWW 2021)}, ADDRESS = {Ljubljana, Slovenia}, }
Endnote
%0 Conference Proceedings %A Ho, Vinh Thinh %A Pal, Koninika %A Razniewski, Simon %A Berberich, Klaus %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Extracting Contextualized Quantity Facts from Web Tables : %G eng %U http://hdl.handle.net/21.11116/0000-0008-04A0-E %D 2021 %B 30th The Web Conference %Z date of event: 2021-04-30 - %C Ljubljana, Slovenia %B Proceedings of The Web Conference 2021 %I ACM
[6]
A. Marx, L. Yang, and M. van Leeuwen, “Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multidimensional Adaptive Histograms,” in Proceedings of the SIAM International Conference on Data Mining (SDM). (Accepted/in press)
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@inproceedings{marx:20:myl, TITLE = {Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multidimensional Adaptive Histograms}, AUTHOR = {Marx, Alexander and Yang, Lincen and van Leeuwen, Matthijs}, LANGUAGE = {eng}, PUBLISHER = {SIAM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the SIAM International Conference on Data Mining (SDM)}, }
Endnote
%0 Conference Proceedings %A Marx, Alexander %A Yang, Lincen %A van Leeuwen, Matthijs %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multidimensional Adaptive Histograms : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0BC7-C %D 2021 %B Proceedings of the SIAM International Conference on Data Mining (SDM) %I SIAM
[7]
A. Marx, A. Gretton, and J. M. Mooij, “A Weaker Faithfulness Assumption based on Triple Interactions,” 2021. [Online]. Available: https://arxiv.org/abs/2010.14265. (arXiv: 2010.14265)
Abstract
One of the core assumptions in causal discovery is the faithfulness assumption---i.e. assuming that independencies found in the data are due to separations in the true causal graph. This assumption can, however, be violated in many ways, including xor connections, deterministic functions or cancelling paths. In this work, we propose a weaker assumption that we call 2-adjacency faithfulness. In contrast to adjacency faithfulness, which assumes that there is no conditional independence between each pair of variables that are connected in the causal graph, we only require no conditional independence between a node and a subset of its Markov blanket that can contain up to two nodes. Equivalently, we adapt orientation faithfulness to this setting. We further propose a sound orientation rule for causal discovery that applies under weaker assumptions. As a proof of concept, we derive a modified Grow and Shrink algorithm that recovers the Markov blanket of a target node and prove its correctness under strictly weaker assumptions than the standard faithfulness assumption.
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@online{Marxarxiv21, TITLE = {A Weaker Faithfulness Assumption based on Triple Interactions}, AUTHOR = {Marx, Alexander and Gretton, Arthur and Mooij, Joris M.}, LANGUAGE = {eng}, URL = {https://arxiv.org/abs/2010.14265}, EPRINT = {2010.14265}, EPRINTTYPE = {arXiv}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, ABSTRACT = {One of the core assumptions in causal discovery is the faithfulness assumption---i.e. assuming that independencies found in the data are due to separations in the true causal graph. This assumption can, however, be violated in many ways, including xor connections, deterministic functions or cancelling paths. In this work, we propose a weaker assumption that we call 2-adjacency faithfulness. In contrast to adjacency faithfulness, which assumes that there is no conditional independence between each pair of variables that are connected in the causal graph, we only require no conditional independence between a node and a subset of its Markov blanket that can contain up to two nodes. Equivalently, we adapt orientation faithfulness to this setting. We further propose a sound orientation rule for causal discovery that applies under weaker assumptions. As a proof of concept, we derive a modified Grow and Shrink algorithm that recovers the Markov blanket of a target node and prove its correctness under strictly weaker assumptions than the standard faithfulness assumption.}, }
Endnote
%0 Report %A Marx, Alexander %A Gretton, Arthur %A Mooij, Joris M. %+ Databases and Information Systems, MPI for Informatics, Max Planck Society External Organizations External Organizations %T A Weaker Faithfulness Assumption based on Triple Interactions : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0BCE-5 %U https://arxiv.org/abs/2010.14265 %D 2021 %X One of the core assumptions in causal discovery is the faithfulness assumption---i.e. assuming that independencies found in the data are due to separations in the true causal graph. This assumption can, however, be violated in many ways, including xor connections, deterministic functions or cancelling paths. In this work, we propose a weaker assumption that we call 2-adjacency faithfulness. In contrast to adjacency faithfulness, which assumes that there is no conditional independence between each pair of variables that are connected in the causal graph, we only require no conditional independence between a node and a subset of its Markov blanket that can contain up to two nodes. Equivalently, we adapt orientation faithfulness to this setting. We further propose a sound orientation rule for causal discovery that applies under weaker assumptions. As a proof of concept, we derive a modified Grow and Shrink algorithm that recovers the Markov blanket of a target node and prove its correctness under strictly weaker assumptions than the standard faithfulness assumption. %K Statistics, Machine Learning, stat.ML,Computer Science, Artificial Intelligence, cs.AI,Computer Science, Learning, cs.LG
[8]
O. Mian, A. Marx, and J. Vreeken, “Discovering Fully Oriented Causal Networks,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada. (Accepted/in press)
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@inproceedings{mian:20:globe, TITLE = {Discovering Fully Oriented Causal Networks}, AUTHOR = {Mian, Osman and Marx, Alexander and Vreeken, Jilles}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)}, ADDRESS = {Vancouver, Canada}, }
Endnote
%0 Conference Proceedings %A Mian, Osman %A Marx, Alexander %A Vreeken, Jilles %+ External Organizations Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Discovering Fully Oriented Causal Networks : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0BCB-8 %D 2021 %B The Thirty-Fifth Conference on Artificial Intelligence %Z date of event: 2021-02-02 - 2021-02-09 %C Vancouver, Canada %B Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)
[9]
S. Nag Chowdhury, S. Razniewski, and G. Weikum, “SANDI: Story-and-Images Alignment,” in EACL 2021, 16th Conference of the European Chapter of the Association for Computational Linguistics, Online. (Accepted/in press)
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@inproceedings{Thinh_EACL21, TITLE = {{SANDI}: Story-and-Images Alignment}, AUTHOR = {Nag Chowdhury, Sreyasi and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {EACL 2021, 16th Conference of the European Chapter of the Association for Computational Linguistics}, ADDRESS = {Online}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T SANDI: Story-and-Images Alignment : %G eng %U http://hdl.handle.net/21.11116/0000-0008-04A2-C %D 2021 %B 16th Conference of the European Chapter of the Association for Computational Linguistics %Z date of event: 2021-04-19 - 2021-04-23 %C Online %B EACL 2021
[10]
S. Nag Chowdhury, “Exploiting Image-Text Synergy for Contextual Image Captioning,” in EACL 2021, Virtual. (Accepted/in press)
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@inproceedings{Chod_ECAL2021, TITLE = {Exploiting Image-Text Synergy for Contextual Image Captioning}, AUTHOR = {Nag Chowdhury, Sreyasi}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {EACL 2021}, ADDRESS = {Virtual}, }
Endnote
%0 Conference Proceedings %A Nag Chowdhury, Sreyasi %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Exploiting Image-Text Synergy for Contextual Image Captioning : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0E60-D %D 2021 %B The First Workshop Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN 2021) at EACL 2021 %Z date of event: 2021-04-19 - 2021-04-20 %C Virtual %B EACL 2021
[11]
T.-P. Nguyen, S. Razniewski, and G. Weikum, “Advanced Semantics for Commonsense Knowledge Extraction,” in Proceedings of The Web Conference 2021 (WWW 2021), Ljubljana, Slovenia. (Accepted/in press)
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@inproceedings{Nguyen_WWW21, TITLE = {Advanced Semantics for Commonsense Knowledge Extraction}, AUTHOR = {Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {ACM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Proceedings of The Web Conference 2021 (WWW 2021)}, ADDRESS = {Ljubljana, Slovenia}, }
Endnote
%0 Conference Proceedings %A Nguyen, Tuan-Phong %A Razniewski, Simon %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Advanced Semantics for Commonsense Knowledge Extraction : %G eng %U http://hdl.handle.net/21.11116/0000-0008-0196-D %D 2021 %B 30th The Web Conference %Z date of event: 2021-04-30 - %C Ljubljana, Slovenia %B Proceedings of The Web Conference 2021 %I ACM
[12]
J. Romero, “Pyformlang: An Educational Library for Formal Language Manipulation,” in SIGCSE’21, The 52nd ACM Technical Symposium on Computer Science Education, Virtual Event, USA. (Accepted/in press)
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@inproceedings{Romero_SIGCSE21, TITLE = {Pyformlang: {An} Educational Library for Formal Language Manipulation}, AUTHOR = {Romero, Julien}, LANGUAGE = {eng}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {SIGCSE'21, The 52nd ACM Technical Symposium on Computer Science Education}, ADDRESS = {Virtual Event, USA}, }
Endnote
%0 Conference Proceedings %A Romero, Julien %+ Databases and Information Systems, MPI for Informatics, Max Planck Society %T Pyformlang: An Educational Library for Formal Language Manipulation : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F836-5 %D 2021 %B The 52nd ACM Technical Symposium on Computer Science Education %Z date of event: 2021-03-13 - 2021-03-20 %C Virtual Event, USA %B SIGCSE'21
[13]
A. Tigunova, P. Mirza, A. Yates, and G. Weikum, “Exploring Personal Knowledge Extraction from Conversations with CHARM,” in WSDM’21, 14th International Conference on Web Search and Data Mining, Jerusalem, Israel (Online). (Accepted/in press)
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@inproceedings{Tigunova_WSDM21, TITLE = {Exploring Personal Knowledge Extraction from Conversations with {CHARM}}, AUTHOR = {Tigunova, Anna and Mirza, Paramita and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {ACM}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {WSDM'21, 14th International Conference on Web Search and Data Mining}, ADDRESS = {Jerusalem, Israel (Online)}, }
Endnote
%0 Conference Proceedings %A Tigunova, Anna %A Mirza, Paramita %A Yates, Andrew %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T Exploring Personal Knowledge Extraction from Conversations with CHARM : %G eng %U http://hdl.handle.net/21.11116/0000-0007-F850-7 %D 2021 %B 14th International Conference on Web Search and Data Mining %Z date of event: 2021-03-08 - 2021-03-12 %C Jerusalem, Israel (Online) %B WSDM'21 %I ACM
[14]
G. H. Torbati, A. Yates, and G. Weikum, “You Get What You Chat: Using Conversations to Personalize Search-based Recommendations,” in Advances in Information Retrieval (ECIR 2021), Lucca, Italy (Online Event). (Accepted/in press)
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@inproceedings{Torbati_ECIR2021, TITLE = {You Get What You Chat: {U}sing Conversations to Personalize Search-based Recommendations}, AUTHOR = {Torbati, Ghazaleh Haratinezhad and Yates, Andrew and Weikum, Gerhard}, LANGUAGE = {eng}, PUBLISHER = {Springer}, YEAR = {2021}, PUBLREMARK = {Accepted}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Advances in Information Retrieval (ECIR 2021)}, SERIES = {Lecture Notes in Computer Science}, ADDRESS = {Lucca, Italy (Online Event)}, }
Endnote
%0 Conference Proceedings %A Torbati, Ghazaleh Haratinezhad %A Yates, Andrew %A Weikum, Gerhard %+ Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society Databases and Information Systems, MPI for Informatics, Max Planck Society %T You Get What You Chat: Using Conversations to Personalize Search-based Recommendations : %G eng %U http://hdl.handle.net/21.11116/0000-0007-ECA2-8 %D 2021 %B 43rd European Conference on IR Research %Z date of event: 2021-03-28 - 2021-04-01 %C Lucca, Italy (Online Event) %B Advances in Information Retrieval %I Springer %B Lecture Notes in Computer Science