Artificial intelligence & Information retrieval

Category: Uncategorized (Page 1 of 3)

SIGIR eCom

SIGIR eCom is a full day hybrid workshop taking place on Friday, July 15, 2022 in conjunction with SIGIR 2022. The SIGIR Workshop on eCommerce serves as a platform for publication and discussion of Information Retrieval and NLP research & their applications in the domain of eCommerce. This workshop will bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to product search and recommendation in eCommerce.

The theme of this year’s workshop is Bridging IR Metrics and Business Metrics and Multi-objective Optimization.

Workshop website:  https://sigir-ecom.github.io/

Publications update

Here’s an update of recent and upcoming publications:

  • Mozhdeh Ariannezhad, Mohamed Yahya, Edgar Meij, Sebastian Schelter, and Maarten de Rijke. Understanding Financial Information Seeking Behavior from User Interactions with Company Filings. In The 2nd Workshop on Financial Technology on the Web (FinWeb). ACM, April 2022. Bibtex, PDF
    @inproceedings{ariannezhad-2022-understanding,
    author = {Ariannezhad, Mozhdeh and Yahya, Mohamed and Meij, Edgar and Schelter, Sebastian and de Rijke, Maarten},
    booktitle = {The 2nd Workshop on Financial Technology on the Web (FinWeb)},
    date-added = {2022-03-03 06:29:45 +0100},
    date-modified = {2022-03-03 06:34:05 +0100},
    month = {April},
    publisher = {ACM},
    title = {Understanding Financial Information Seeking Behavior from User Interactions with Company Filings},
    year = {2022}}
  • Maurits Bleeker and Maarten de Rijke. Do Lessons from Metric Learning Generalize to Image-Caption Retrieval?. In ECIR 2022: 44th European Conference on Information Retrieval. Springer, April 2022. Bibtex, PDF
    @inproceedings{bleeker-2022-do,
    author = {Bleeker, Maurits and de Rijke, Maarten},
    booktitle = {ECIR 2022: 44th European Conference on Information Retrieval},
    date-added = {2021-11-26 06:08:03 +0100},
    date-modified = {2021-11-26 06:13:00 +0100},
    month = {April},
    publisher = {Springer},
    title = {Do Lessons from Metric Learning Generalize to Image-Caption Retrieval?},
    year = {2022}}
  • Mariya Hendriksen, Maurits Bleeker, Svitlana Vakulenko, Nanne van Noord, Ernst Kuiper, and Maarten de Rijke. Extending CLIP for Category-to-image Retrieval in E-commerce. In ECIR 2022: 44th European Conference on Information Retrieval. Springer, April 2022. Bibtex, PDF
    @inproceedings{hendriksen-2022-extending,
    author = {Hendriksen, Mariya and Bleeker, Maurits and Vakulenko, Svitlana and van Noord, Nanne and Kuiper, Ernst and de Rijke, Maarten},
    booktitle = {ECIR 2022: 44th European Conference on Information Retrieval},
    date-added = {2021-11-19 11:35:01 +0100},
    date-modified = {2021-11-19 11:38:50 +0100},
    month = {April},
    publisher = {Springer},
    title = {Extending {CLIP} for Category-to-image Retrieval in E-commerce},
    year = {2022}}
  • Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei, Maarten de Rijke, and Fabrizio Silvestri. CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks. In AISTATS 2022: 25th International Conference on Artificial Intelligence and Statistics. PMLR, March 2022. Bibtex, PDF
    @inproceedings{lucic-2022-cf-gnnexplainer,
    author = {Lucic, Ana and ter Hoeve, Maartje and Tolomei, Gabriele and de Rijke, Maarten and Silvestri, Fabrizio},
    booktitle = {AISTATS 2022: 25th International Conference on Artificial Intelligence and Statistics},
    date-added = {2022-01-18 15:54:25 +0100},
    date-modified = {2022-02-26 10:22:14 +0100},
    month = {March},
    publisher = {PMLR},
    title = {CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks},
    year = {2022}}
  • Vaishali Pal, Evangelos Kanoulas, and Maarten de Rijke. Parameter-Efficient Abstractive Question Answering over Tables and Text. In 2nd DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering. ACL, May 2022. Bibtex, PDF
    @inproceedings{pal-2022-parameter-efficient,
    author = {Pal, Vaishali and Kanoulas, Evangelos and de Rijke, Maarten},
    booktitle = {2nd DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering},
    date-added = {2022-03-28 17:49:11 +0200},
    date-modified = {2022-03-28 17:50:16 +0200},
    month = {May},
    publisher = {ACL},
    title = {Parameter-Efficient Abstractive Question Answering over Tables and Text},
    year = {2022}}
  • Chen Wu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, and Xueqi Cheng. PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models. arXiv preprint arXiv:2204.01321, April 2022. Bibtex, PDF
    @article{wu-2022-prada-arxiv,
    author = {Wu, Chen and Zhang, Ruqing and Guo, Jiafeng and de Rijke, Maarten and Fan, Yixing and Cheng, Xueqi},
    date-added = {2022-04-05 06:14:47 +0200},
    date-modified = {2022-05-05 11:06:18 +0200},
    journal = {arXiv preprint arXiv:2204.01321},
    month = {April},
    title = {{PRADA}: Practical Black-Box Adversarial Attacks against Neural Ranking Models},
    year = {2022}}
  • Yangjun Zhang, Pengjie Ren, Wentao Deng, Zhumin Chen, and Maarten de Rijke. Improving Multi-label Malevolence Detection in Dialogues through Multi-faceted Label Correlation Enhancement. In ACL 2022: 60th Annual Meeting of the Association for Computational Linguistics. ACL, May 2022. Bibtex, PDF
    @inproceedings{zhang-2022-improving,
    author = {Zhang, Yangjun and Ren, Pengjie and Deng, Wentao and Chen, Zhumin and de Rijke, Maarten},
    booktitle = {ACL 2022: 60th Annual Meeting of the Association for Computational Linguistics},
    date-added = {2022-02-24 11:39:52 +0100},
    date-modified = {2022-02-24 11:42:02 +0100},
    month = {May},
    publisher = {ACL},
    title = {Improving Multi-label Malevolence Detection in Dialogues through Multi-faceted Label Correlation Enhancement},
    year = {2022}}

New publications

Papers about to be published this month:

  • Yifan Chen, Yang Wang, Pengjie Ren, Meng Wang, and Maarten de Rijke. Bayesian Feature Interaction Selection for Factorization Machines. Artificial Intelligence, 302:103589, January 2022. Bibtex, PDF
    @article{chen-2022-bayesian,
    author = {Chen, Yifan and Wang, Yang and Ren, Pengjie and Wang, Meng and de Rijke, Maarten},
    date-added = {2020-05-09 14:15:34 +0200},
    date-modified = {2021-09-14 05:59:23 +0200},
    journal = {Artificial Intelligence},
    month = {January},
    pages = {103589},
    title = {Bayesian Feature Interaction Selection for Factorization Machines},
    volume = {302},
    year = {2022}}
  • Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, and Maarten de Rijke. Multi-interest Diversification for End-to-end Sequential Recommendation. ACM Transactions on Information Systems, 40(1):Article 20, January 2022. Bibtex, PDF
    @article{chen-2022-multi-interest,
    author = {Chen, Wanyu and Ren, Pengjie and Cai, Fei and Sun, Fei and de Rijke, Maarten},
    date-added = {2020-12-30 19:09:27 +0100},
    date-modified = {2021-09-14 05:58:00 +0200},
    journal = {ACM Transactions on Information Systems},
    month = {January},
    number = {1},
    pages = {Article 20},
    title = {Multi-interest Diversification for End-to-end Sequential Recommendation},
    volume = {40},
    year = {2022}}
  • Jin Huang, Harrie Oosterhuis, and Maarten de Rijke. It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences are Dynamic. In WSDM 2022: The Fifteenth International Conference on Web Search and Data Mining, page 381–289. ACM, February 2022. Bibtex, PDF
    @inproceedings{huang-2022-different,
    author = {Huang, Jin and Oosterhuis, Harrie and de Rijke, Maarten},
    booktitle = {WSDM 2022: The Fifteenth International Conference on Web Search and Data Mining},
    date-added = {2021-10-12 06:17:51 +0200},
    date-modified = {2022-02-26 10:20:25 +0100},
    month = {February},
    pages = {381--289},
    publisher = {ACM},
    title = {It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences are Dynamic},
    year = {2022}}
  • Ana Lucic, Harrie Oosterhuis, Hinda Haned, and Maarten de Rijke. FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles. In AAAI 2022: Thirty-Sixth AAAI Conference on Artificial Intelligence. AAAI, February 2022. Bibtex, PDF
    @inproceedings{lucic-2022-focus,
    author = {Lucic, Ana and Oosterhuis, Harrie and Haned, Hinda and de Rijke, Maarten},
    booktitle = {AAAI 2022: Thirty-Sixth AAAI Conference on Artificial Intelligence},
    date-added = {2021-12-01 11:40:23 +0100},
    date-modified = {2021-12-01 11:42:46 +0100},
    month = {February},
    publisher = {AAAI},
    title = {FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles},
    year = {2022}}
  • Ana Lucic, Maurits Bleeker, Sami Jullien, Samarth Bhargav, and Maarten de Rijke. Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence. In Twelfth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-22). AAAI Press, February 2022. Bibtex, PDF
    @inproceedings{lucic-2022-reproducibility,
    author = {Lucic, Ana and Bleeker, Maurits and Jullien, Sami and Bhargav, Samarth and de Rijke, Maarten},
    booktitle = {Twelfth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-22)},
    date-added = {2021-11-13 06:14:16 +0100},
    date-modified = {2021-11-13 06:16:19 +0100},
    month = {February},
    publisher = {AAAI Press},
    title = {Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence},
    year = {2022}}
  • Fatemeh Sarvi, Maria Heuss, Mohammad Aliannejadi, Sebastian Schelter, and Maarten de Rijke. Understanding and Mitigating the Effect of Outliers in Fair Ranking. In WSDM 2022: The Fifteenth International Conference on Web Search and Data Mining, page 861–869. ACM, February 2022. Bibtex, PDF
    @inproceedings{sarvi-2022-understanding,
    author = {Sarvi, Fatemeh and Heuss, Maria and Aliannejadi, Mohammad and Schelter, Sebastian and de Rijke, Maarten},
    booktitle = {WSDM 2022: The Fifteenth International Conference on Web Search and Data Mining},
    date-added = {2021-10-12 06:20:17 +0200},
    date-modified = {2022-02-26 10:21:08 +0100},
    month = {February},
    pages = {861--869},
    publisher = {ACM},
    title = {Understanding and Mitigating the Effect of Outliers in Fair Ranking},
    year = {2022}}

Materials for tutorial on conversational recommendation

Materials to accompany the RecSys 2021 tutorial on conversational recommendation presented by Chongming Gao, Wenqiang Lei and myself are available now:

  • Slides: Chongming Gao, Wenqiang Lei, Maarten de Rijke. RecSys 2021 Tutorial on Conversational Recommendation: Formulation, Methods, and Evaluation, Fifteenth ACM Conference on Recommender Systems (RecSys 2021). PDF
  • Survey: Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. Advances and Challenges in Conversational Recommender Systems: A Survey. AI Open, 2:100–126, July 2021. PDF

New publications

New publications that are scheduled to appear this month:

  • Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, and Maarten de Rijke. Improving End-to-End Sequential Recommendations with Intent-aware Diversification. In CIKM 2020: 29th ACM International Conference on Information and Knowledge Management, page 175–184. ACM, October 2020. Bibtex, PDF
    @inproceedings{chen-2020-improving,
    author = {Chen, Wanyu and Ren, Pengjie and Cai, Fei and Sun, Fei and de Rijke, Maarten},
    booktitle = {CIKM 2020: 29th ACM International Conference on Information and Knowledge Management},
    date-added = {2020-07-17 12:46:55 +0200},
    date-modified = {2020-10-20 07:31:14 +0200},
    month = {October},
    pages = {175--184},
    publisher = {ACM},
    title = {Improving End-to-End Sequential Recommendations with Intent-aware Diversification},
    year = {2020}}
  • Xiangsheng Li, Maarten de Rijke, Yiqun Liu, Jiaxin Mao, Weizhi Ma, Min Zhang, and Shaoping Ma. Learning Better Representations for Neural Information Retrieval with Graph Information. In CIKM 2020: 29th ACM International Conference on Information and Knowledge Management, page 795–804. ACM, October 2020. Bibtex, PDF
    @inproceedings{li-2020-learning,
    author = {Li, Xiangsheng and de Rijke, Maarten and Liu, Yiqun and Mao, Jiaxin and Ma, Weizhi and Zhang, Min and Ma, Shaoping},
    booktitle = {CIKM 2020: 29th ACM International Conference on Information and Knowledge Management},
    date-added = {2020-07-17 12:41:46 +0200},
    date-modified = {2020-10-20 07:31:34 +0200},
    month = {October},
    pages = {795--804},
    publisher = {ACM},
    title = {Learning Better Representations for Neural Information Retrieval with Graph Information},
    year = {2020}}
  • Zhiqiang Pan, Fei Cai, Wanyu Chen, Honghui Chen, and Maarten de Rijke. Star Graph Neural Networks for Session-based Recommendation. In CIKM 2020: 29th ACM International Conference on Information and Knowledge Management, page 1195–1204. ACM, October 2020. Bibtex, PDF
    @inproceedings{pan-2020-star,
    author = {Pan, Zhiqiang and Cai, Fei and Chen, Wanyu and Chen, Honghui and de Rijke, Maarten},
    booktitle = {CIKM 2020: 29th ACM International Conference on Information and Knowledge Management},
    date-added = {2020-07-17 12:44:13 +0200},
    date-modified = {2020-10-20 07:31:54 +0200},
    month = {October},
    pages = {1195--1204},
    publisher = {ACM},
    title = {Star Graph Neural Networks for Session-based Recommendation},
    year = {2020}}
  • Ridho Reinanda, Edgar Meij, and Maarten de Rijke. Knowledge Graphs: An Information Retrieval Perspective. Foundations and Trends in Information Retrieval, 14(4):289–444, October 2020. Bibtex, PDF
    @article{reinanda-2020-knowledge,
    author = {Reinanda, Ridho and Meij, Edgar and de Rijke, Maarten},
    date-added = {2018-07-20 09:10:39 +0000},
    date-modified = {2020-10-16 22:32:43 +0200},
    journal = {Foundations and Trends in Information Retrieval},
    month = {October},
    number = {4},
    pages = {289--444},
    title = {Knowledge Graphs: An Information Retrieval Perspective},
    volume = {14},
    year = {2020}}
  • Ali Vardasbi, Harrie Oosterhuis, and Maarten de Rijke. When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank. In CIKM 2020: 29th ACM International Conference on Information and Knowledge Management, page 1475–1484. ACM, October 2020. Bibtex, PDF
    @inproceedings{vardasbi-2020-inverse,
    author = {Vardasbi, Ali and Oosterhuis, Harrie and de Rijke, Maarten},
    booktitle = {CIKM 2020: 29th ACM International Conference on Information and Knowledge Management},
    date-added = {2020-07-17 12:38:45 +0200},
    date-modified = {2020-10-20 07:32:14 +0200},
    month = {October},
    pages = {1475--1484},
    publisher = {ACM},
    title = {When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank},
    year = {2020}}

Schiet nou toch eens op. De tijd dringt

Vandaag presenteerde de EU haar “white paper” over AI in Europa. Samenvatting van mijn commentaar:

  • Zet veel duidelijker en veel ambitieuzer in op AI-talent — daar staat of valt alles mee.
  • Waarom geeft de EU de hoop over consumentendata op? Is consumentendata nou niet precies waar je het vertrouwen wint of verliest?
  • Beleidsmakers zijn voor de kaders en regelgeving: zij moeten zich niet bemoeien met de inhoud van de AI-wetenschap en AI-innovatie — dat moet je overlaten aan wetenschappers en ondernemers.
  • Het idee van één enkel “lighthouse” als Europees AI-centrum is slecht doordacht, geld verslindend, en staat sociaal-economische impact van AI-technologie in de weg.
  • Waarom wordt er wel gesproken van een netwerk van excellentie (4 pagina’s) en een netwerk van vertrouwen (16 pagina’s!), maar niet van een netwerk van kansen en uitdagingen? Is dat niet waar het begint?
  • Bovenal: doe nu eens wat!

Zie De Volkskrant

Aan de slag!

Morgen, 8 oktober, presenteert VNO-NCW een `Nationale AI Coalitie’. Het Nederlandse bedrijfsleven steekt de komende zeven jaar ruim €1 mrd in kunstmatige intelligentie (AI). Van de overheid wordt ook een flinke bijdrage gevraagd. Het Financieele Dagblad besteedt aandacht aan de verwachte aankondigingen van deze week.


Genoeg gepraat. Genoeg gepolderd. Aan de slag.

UAI 2019 paper on safe online learning to re-rank via implicit click feedback online

BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback by Chang Li, Branislav Kveton, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvari, and Masrour Zoghi is online now at this location.

In the paper we study the problem of safe on-line learning to re-rank, where user feedback is used to improve the quality of displayed lists. Learning to rank has traditionally been studied in two settings. In the offline setting, rankers are typically learned from relevance labels created by judges. This approach has generally be- come standard in industrial applications of ranking, such as search. However, this approach lacks exploration and thus is limited by the information content of the offline training data. In the online setting, an algorithm can experiment with lists and learn from feedback on them in a sequential fashion. Bandit algorithms are well-suited for this setting but they tend to learn user preferences from scratch, which results in a high initial cost of exploration. This poses an additional challenge of safe exploration in ranked lists. We propose BubbleRank, a bandit algorithm for safe re-ranking that combines the strengths of both the offline and online settings. The algorithm starts with an initial base list and improves it online by gradually exchanging higher-ranked less attractive items for lower-ranked more attractive items. We prove an up- per bound on the n-step regret of BubbleRankthat degrades gracefully with the quality of the initial base list. Our theoretical findings are supported by extensive experiments on a large-scale real-world click dataset.

The paper will be presented at UAI 2019: Conference on Uncertainty in Artificial Intelligence, July 2019.

Using process mining for understanding the structure of interaction processes

Svitlana Vakulenko explains how we have recently used process mining techniques to understand the structure of interaction processes, which will in turn help us to improve information-seeking dialogue systems. We extract a new model of information-seeking dialogues, QRFA, for Query, Request, Feedback, Answer. The QRFA model better reflects conversation flows observed in real information-seeking conversations than models proposed previously. QRFA allows us to identify malfunctioning in dialogue system transcripts as deviations from the expected conversation flow described by the model via conformance analysis.

Read the full post.

Investeer in kennisbasis AI of word een toeschouwer

In een opiniestuk voor NRC Handelsblad en NRC Next beargumenteer ik dat artificiële intelligentie ons leven hoe dan ook zal veranderen en dat Nederland voor de keuze staat om vol mee te doen in de ontwikkeling van AI en het spel mee te bepalen, of om de bank te blijven zitten. Wie niet actief meedoet, heeft geen invloed – niet op het spel en al helemaal niet op de spelregels. Investeer in de AI-kennisbasis, investeer in talent. Kom van de bank!

Het hele stuk is hier te vinden.

« Older posts

© 2022 Maarten de Rijke

Theme by Anders NorenUp ↑