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ShareScore release 0.7.1
Dataset results
2 results for “implicit feedback”
LFM User Groups Implicit Feedback
<p>This is a variant of the popular LFM User Groups dataset [1] used in the paper "The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias" [2]. Each user's listening events for an artist are scaled to values between 1 and 5 (the user's min. value being 1 and the max. value being 5). Then, scores above the mean are considered positive feedback whereas scores below the mean are considered as negative feedback. This dataset only includes the positive feedback data. The source-code for generating this dataset is available in our repository [3].</p> <p>[1] https://zenodo.org/records/3475975</p> <p>[2] https://link.springer.com/chapter/10.1007/978-3-031-56066-8_33</p> <p>[3] https://github.com/pmuellner/ImpactOfDP</p>
Dataset for Paper "The EMPATHIC Framework for Task Learning from Implicit Human Feedback"
<p>This is the <strong>raw</strong> data set for the paper "The EMPATHIC Framework for Task Learning from Implicit Human Feedback", published at CoRL 2020. (<a href="https://sites.google.com/utexas.edu/empathic/home">project website</a>)</p> <p>The data set contains human facial reactions recorded while the subjects watching autonomous agents completing a task that determines their reward. This data set contains data recorded in two domains: <em>Robotaxi</em> and <em>RoboticSortingTask</em> (details of each domain can be found in the paper).</p> <p>This data set file is organized as:</p> <ul> <li><strong>RobotaxiData</strong>/: <ul> <li>subject_id/ <ul> <li>episode_N/ <ul> <li>[subject_id]_episode_N_[time]_[time_step]_[frame_id].jpg</li> </ul> </li> </ul> </li> <li>RLstatistics_episode_N.csv: time_step, reward, optimality</li> </ul> </li> <li><strong>RoboticSortingTaskData</strong>/ <ul> <li>subject_id/ <ul> <li>[subject_id]_[trajectory_order]_[trajectory_id]</li> </ul> </li> <li>trajectory_returns.csv: trajectory_id, final_return</li> </ul> </li> </ul> <p>If you find this data set useful for your research, please consider citing our paper:</p> <pre>@inproceedings{cui2020empathic, title={The EMPATHIC Framework for Task Learning from Implicit Human Feedback}, author={Cui, Yuchen and Zhang, Qiping and Allievi, Alessandro and Stone, Peter and Niekum, Scott and Knox, W Bradley}, booktitle={Conference on Robot Learning}, year={2020}, organization={PMLR} }</pre> <p> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.