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Trier, in: Lecture Notes in Information Systems and Organisation, Springer International Publishing, Cham, 2021.","chicago":"Meydani, Elnaz, Christoph Düsing, and Matthias Trier. “Towards a Trust-Aware Item Recommendation System on a Graph Autoencoder with Attention Mechanism.” In <i>Lecture Notes in Information Systems and Organisation</i>. Cham: Springer International Publishing, 2021. <a href=\"https://doi.org/10.1007/978-3-030-86797-3_5\">https://doi.org/10.1007/978-3-030-86797-3_5</a>.","apa":"Meydani, E., Düsing, C., &#38; Trier, M. (2021). Towards a Trust-Aware Item Recommendation System on a Graph Autoencoder with Attention Mechanism. In <i>Lecture Notes in Information Systems and Organisation</i>. Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-86797-3_5\">https://doi.org/10.1007/978-3-030-86797-3_5</a>","ieee":"E. Meydani, C. Düsing, and M. 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Springer International Publishing; 2021. doi:<a href=\"https://doi.org/10.1007/978-3-030-86797-3_5\">10.1007/978-3-030-86797-3_5</a>","bibtex":"@inbook{Meydani_Düsing_Trier_2021, place={Cham}, title={Towards a Trust-Aware Item Recommendation System on a Graph Autoencoder with Attention Mechanism}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-86797-3_5\">10.1007/978-3-030-86797-3_5</a>}, booktitle={Lecture Notes in Information Systems and Organisation}, publisher={Springer International Publishing}, author={Meydani, Elnaz and Düsing, Christoph and Trier, Matthias}, year={2021} }","mla":"Meydani, Elnaz, et al. “Towards a Trust-Aware Item Recommendation System on a Graph Autoencoder with Attention Mechanism.” <i>Lecture Notes in Information Systems and Organisation</i>, Springer International Publishing, 2021, doi:<a href=\"https://doi.org/10.1007/978-3-030-86797-3_5\">10.1007/978-3-030-86797-3_5</a>."},"type":"book_chapter","department":[{"_id":"198"}],"place":"Cham","date_created":"2023-01-10T10:27:20Z"},{"_id":"24547","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://dtai.cs.kuleuven.be/events/MLSA21/papers/MLSA21_paper_muller.pdf"}],"user_id":"60721","conference":{"end_date":"2021-09-17","location":"Online","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021)","start_date":"2021-09-13"},"author":[{"id":"72849","full_name":"Müller, Oliver","first_name":"Oliver","last_name":"Müller"},{"id":"60721","last_name":"Caron","first_name":"Matthew","full_name":"Caron, Matthew"},{"last_name":"Döring","first_name":"Michael","full_name":"Döring, Michael"},{"last_name":"Heuwinkel","first_name":"Tim","full_name":"Heuwinkel, Tim"},{"full_name":"Baumeister, Jochen","orcid":"0000-0003-2683-5826","last_name":"Baumeister","first_name":"Jochen","id":"46"}],"year":"2021","status":"public","title":"PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data","date_updated":"2023-02-28T08:58:24Z","publication_status":"inpress","date_created":"2021-09-16T08:33:04Z","department":[{"_id":"196"},{"_id":"172"}],"keyword":["expected possession value","handball","tracking data","time series classification","deep learning"],"type":"conference","citation":{"short":"O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.","chicago":"Müller, Oliver, Matthew Caron, Michael Döring, Tim Heuwinkel, and Jochen Baumeister. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball Using Tracking Data.” In <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>, n.d.","apa":"Müller, O., Caron, M., Döring, M., Heuwinkel, T., &#38; Baumeister, J. (n.d.). PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data. <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.","ieee":"O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data,” presented at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.","ama":"Müller O, Caron M, Döring M, Heuwinkel T, Baumeister J. PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data. In: <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>.","bibtex":"@inproceedings{Müller_Caron_Döring_Heuwinkel_Baumeister, title={PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data}, booktitle={8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)}, author={Müller, Oliver and Caron, Matthew and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen} }","mla":"Müller, Oliver, et al. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball Using Tracking Data.” <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>."},"publication":"8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)","abstract":[{"text":"Over the last years, several approaches for the data-driven estimation of expected possession value (EPV) in basketball and association football (soccer) have been proposed. In this paper, we develop and evaluate PIVOT: the first such framework for team handball. Accounting for the fast-paced, dynamic nature and relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep learning architecture that relies solely on tracking data. This efficient approach is capable of predicting the probability that a team will score within the near future given the fine-grained spatio-temporal distribution of all players and the ball over the last seconds of the game. Our experiments indicate that PIVOT is able to produce accurate and calibrated probability estimates, even when trained on a relatively small dataset. We also showcase two interactive applications of PIVOT for valuing actual and counterfactual player decisions and actions in real-time.","lang":"eng"}]},{"date_created":"2021-09-24T09:51:35Z","type":"conference","keyword":["Retail investors","GameStop","Social Networks","Reddit","WallStreetBets"],"department":[{"_id":"196"}],"publication":"42nd International Conference on Information Systems (ICIS 2021)","citation":{"mla":"Caron, Matthew, et al. “To the Moon! Analyzing the Community of ‘Degenerates’ Engaged in the Surge of the GME Stock.” <i>42nd International Conference on Information Systems (ICIS 2021)</i>, 2021.","bibtex":"@inproceedings{Caron_Gulenko_Müller_2021, title={To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge of the GME Stock}, booktitle={42nd International Conference on Information Systems (ICIS 2021)}, author={Caron, Matthew and Gulenko, Maryna and Müller, Oliver}, year={2021} }","ama":"Caron M, Gulenko M, Müller O. To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge of the GME Stock. In: <i>42nd International Conference on Information Systems (ICIS 2021)</i>. ; 2021.","ieee":"M. Caron, M. Gulenko, and O. Müller, “To the Moon! Analyzing the Community of ‘Degenerates’ Engaged in the Surge of the GME Stock,” presented at the 42nd International Conference on Information Systems (ICIS 2021), Austin, Texas, 2021.","apa":"Caron, M., Gulenko, M., &#38; Müller, O. (2021). To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge of the GME Stock. <i>42nd International Conference on Information Systems (ICIS 2021)</i>. 42nd International Conference on Information Systems (ICIS 2021), Austin, Texas.","short":"M. Caron, M. Gulenko, O. Müller, in: 42nd International Conference on Information Systems (ICIS 2021), 2021.","chicago":"Caron, Matthew, Maryna Gulenko, and Oliver Müller. “To the Moon! Analyzing the Community of ‘Degenerates’ Engaged in the Surge of the GME Stock.” In <i>42nd International Conference on Information Systems (ICIS 2021)</i>, 2021."},"abstract":[{"text":"In early 2021, the finance world was taken by storm by the dramatic price surge of the GameStop Corp. stock. This rise is being, at least in part, attributed to a group of Redditors belonging to the now-famous r/wallstreetbets (WSB) subreddit group. In this work, we set out to address if user activity on the WSB subreddit is associated with the trading volume of the GME stock. Leveraging a unique dataset containing more than 4.9 million WSB posts and comments, we assert that user activity is associated with the trading volume of the GameStop stock. We further show that posts have a significantly higher predictive power than comments and are especially helpful for predicting unusually high trading volume. Lastly, as recent events have shown, we believe that these findings have implications for retail and institutional investors, trading platforms, and policymakers, as these can have disruptive potential.","lang":"eng"}],"main_file_link":[{"url":"https://aisel.aisnet.org/icis2021/social_media/social_media/13/"}],"_id":"25029","language":[{"iso":"eng"}],"user_id":"60721","title":"To the Moon! 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