Catching up with papers published since the last update, in July 2021:

  • Gabriel Bénédict, Vincent Koops, Daan Odijk, and Maarten de Rijke. sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification. arXiv preprint arXiv:2108.10566, August 2021. Bibtex, PDF, URL
    @article{benedict-2021-sigmoidf1-arxiv,
    author = {B{\'e}n{\'e}dict, Gabriel and Koops, Vincent and Odijk, Daan and de Rijke, Maarten},
    date-added = {2021-08-25 10:23:31 +0200},
    date-modified = {2022-05-05 11:10:13 +0200},
    journal = {arXiv preprint arXiv:2108.10566},
    month = {August},
    title = {{sigmoidF1}: A Smooth {F1} Score Surrogate Loss for Multilabel Classification},
    url = {https://arxiv.org/pdf/2108.10566},
    year = {2021},
    bdsk-url-1 = {https://arxiv.org/pdf/2108.10566}}
  • 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. Bibtex, PDF
    @article{gao-2021-advances,
    author = {Gao, Chongming and Lei, Wenqiang and He, Xiangnan and de Rijke, Maarten and Chua, Tat-Seng},
    date-added = {2021-07-25 09:14:58 +0200},
    date-modified = {2021-07-25 09:22:18 +0200},
    journal = {AI Open},
    month = {July},
    pages = {100--126},
    title = {Advances and Challenges in Conversational Recommender Systems: A Survey},
    volume = {2},
    year = {2021},
    bdsk-url-1 = {https://arxiv.org/abs/2101.09459}}
  • Ming Li, Sami Jullien, Mozhdeh Ariannezhad, and Maarten de Rijke. A Next Basket Recommendation Reality Check. arXiv preprint arXiv:2109.14233, September 2021. Bibtex, PDF, URL
    @article{li-2021-next-arxiv,
    author = {Li, Ming and Jullien, Sami and Ariannezhad, Mozhdeh and de Rijke, Maarten},
    date-added = {2021-09-30 10:15:30 +0200},
    date-modified = {2021-09-30 10:16:58 +0200},
    journal = {arXiv preprint arXiv:2109.14233},
    month = {September},
    title = {A Next Basket Recommendation Reality Check},
    url = {https://arxiv.org/pdf/2109.14233},
    year = {2021},
    bdsk-url-1 = {https://arxiv.org/pdf/2108.10566}}
  • Zhongkun Liu, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Maarten de Rijke, and Ming Zhou. Learning to Ask Conversational Questions by Optimizing Levenshtein Distance. In ACL-IJCNLP 2021: The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, August 2021. Bibtex, PDF
    @inproceedings{liu-2021-learning,
    author = {Liu, Zhongkun and Ren, Pengjie and Chen, Zhumin and Ren, Zhaochun and de Rijke, Maarten and Zhou, Ming},
    booktitle = {ACL-IJCNLP 2021: The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing},
    date-added = {2021-05-06 08:54:21 +0200},
    date-modified = {2021-05-06 08:55:39 +0200},
    month = {August},
    title = {Learning to Ask Conversational Questions by Optimizing Levenshtein Distance},
    year = {2021}}
  • Ana Lucic, Harrie Oosterhuis, Hinda Haned, and Maarten de Rijke. Flexible Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles. In RAI@KDD’21: Measures and Best Practices for Responsible AI. ACM, August 2021. Bibtex
    @inproceedings{lucic-2021-flexible-kdd,
    author = {Lucic, Ana and Oosterhuis, Harrie and Haned, Hinda and de Rijke, Maarten},
    booktitle = {RAI@KDD'21: Measures and Best Practices for Responsible AI},
    date-added = {2021-07-02 07:46:30 +0200},
    date-modified = {2021-07-11 22:12:12 +0200},
    month = {August},
    publisher = {ACM},
    title = {Flexible Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles},
    year = {2021}}
  • Ana Lucic, Maurits Bleeker, Sami Jullien, Samarth Bhargav, and Maarten de Rijke. Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence through the Lens of Reproducibility. arXiv preprint arXiv:2111.00826, November 2021. Bibtex, PDF, URL
    @article{lucic-2021-teaching-arxiv,
    author = {Lucic, Ana and Bleeker, Maurits and Jullien, Sami and Bhargav, Samarth and de Rijke, Maarten},
    date-added = {2021-11-07 09:17:03 +0100},
    date-modified = {2021-11-13 07:44:52 +0100},
    journal = {arXiv preprint arXiv:2111.00826},
    month = {November},
    title = {Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence through the Lens of Reproducibility},
    url = {https://arxiv.org/pdf/2111.00826},
    year = {2021},
    bdsk-url-1 = {https://arxiv.org/pdf/2111.00826}}
  • Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei, Maarten de Rijke, and Fabrizio Silvestri. Counterfactual Explanations for Graph Neural Networks. In KDD’21 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD’21). ACM, August 2021. Bibtex
    @inproceedings{lucic-2021-counterfactual,
    author = {Lucic, Ana and ter Hoeve, Maartje and Tolomei, Gabriele and de Rijke, Maarten and Silvestri, Fabrizio},
    booktitle = {KDD'21 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD'21)},
    date-added = {2021-06-22 07:05:27 +0200},
    date-modified = {2021-06-22 07:06:41 +0200},
    month = {August},
    publisher = {ACM},
    title = {Counterfactual Explanations for Graph Neural Networks},
    year = {2021}}
  • Harrie Oosterhuis and Maarten de Rijke. Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions (Extended Abstract). In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, page 4809–4813. International Joint Conferences on Artificial Intelligence Organization, 8 2021. Sister Conferences Best Papers Bibtex, PDF
    @inproceedings{oosterhuis-2021-unifying-ijcai2021,
    author = {Oosterhuis, Harrie and de Rijke, Maarten},
    booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, {IJCAI-21}},
    date-added = {2021-09-04 08:42:30 +0200},
    date-modified = {2021-09-04 08:42:30 +0200},
    month = {8},
    note = {Sister Conferences Best Papers},
    pages = {4809--4813},
    publisher = {International Joint Conferences on Artificial Intelligence Organization},
    title = {Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions (Extended Abstract)},
    year = {2021},
    bdsk-url-1 = {https://doi.org/10.24963/ijcai.2021/656}}
  • Olivier Sprangers, Sebastian Schelter, and Maarten de Rijke. Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression. In KDD ’21: Proceedings of the 27th Conference on Knowledge Discovery and Data Mining, page 1510–1520. ACM, August 2021. Bibtex, PDF
    @inproceedings{sprangers-2021-probabilistic,
    author = {Sprangers, Olivier and Schelter, Sebastian and de Rijke, Maarten},
    booktitle = {KDD '21: Proceedings of the 27th Conference on Knowledge Discovery and Data Mining},
    date-added = {2021-05-16 11:08:21 +0200},
    date-modified = {2021-08-13 07:57:40 +0200},
    month = {August},
    pages = {1510--1520},
    publisher = {ACM},
    title = {Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression},
    year = {2021}}
  • Svitlana Vakulenko, Evangelos Kanoulas, and Maarten de Rijke. A Large-Scale Analysis of Mixed Initiative in Information-Seeking Dialogues for Conversational Search. ACM Transactions on Information Systems, 39(4):Article 49, August 2021. Bibtex, PDF
    @article{vakulenko-2021-large-scale,
    author = {Vakulenko, Svitlana and Kanoulas, Evangelos and de Rijke, Maarten},
    date-added = {2020-05-29 06:56:12 +0200},
    date-modified = {2021-08-18 22:31:05 +0200},
    journal = {ACM Transactions on Information Systems},
    month = {August},
    number = {4},
    pages = {Article 49},
    title = {A Large-Scale Analysis of Mixed Initiative in Information-Seeking Dialogues for Conversational Search},
    volume = {39},
    year = {2021}}
  • Ali Vardasbi, Maarten de Rijke, and Ilya Markov. Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank. In CIKM 2021: 30th ACM International Conference on Information and Knowledge Management. ACM, November 2021. Bibtex, PDF
    @inproceedings{vardasbi-2021-mixture-based,
    author = {Vardasbi, Ali and de Rijke, Maarten and Markov, Ilya},
    booktitle = {CIKM 2021: 30th ACM International Conference on Information and Knowledge Management},
    date-added = {2021-08-08 08:29:38 +0200},
    date-modified = {2021-08-10 06:36:04 +0200},
    month = {November},
    publisher = {ACM},
    title = {Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank},
    year = {2021}}
  • Yangjun Zhang, Pengjie Ren, and Maarten de Rijke. A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues. In ACL-IJCNLP 2021: The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, August 2021. Bibtex, PDF
    @inproceedings{zhang-2021-human-machine,
    author = {Zhang, Yangjun and Ren, Pengjie and de Rijke, Maarten},
    booktitle = {ACL-IJCNLP 2021: The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing},
    date-added = {2021-05-06 08:46:35 +0200},
    date-modified = {2021-05-06 08:54:14 +0200},
    month = {August},
    title = {A Human-machine Collaborative Framework for Evaluating Malevolence in Dialogues},
    year = {2021}}