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Dataset results
725 results for “recommendation”
MLOps Education - Challenges and Recommendations
Open the record for dataset details and reuse information.
Data-Driven Extract Method Recommendations: A Study at ING: Appendix
<p>The appendix of our FSE 2021 industry track paper.</p>
Supplementary material 1 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Hackathon Topic 1
recommender systems
<p>A collection of datasets for recommender systems.</p>
Supplementary material 1 from: Agosti D, Benichou L, Addink W, Arvanitidis C, Catapano T, Cochrane G, Dillen M, Döring M, Georgiev T, Gérard I, Groom Q, Kishor P, Kroh A, Kvaček J, Mergen P, Mietchen D, Pauperio J, Sautter G, Penev L (2022) Recommendations for use of annotations and persistent identifiers in taxonomy and biodiversity publishing. Research Ideas and Outcomes 8: e97374. https://doi.org/10.3897/rio.8.e97374
Overview table of recommendations
Supplementary material 2 from: Baricevic A, Chardon C, Kahlert M, Karjalainen SM, Pfannkuchen DM, Pfannkuchen M, Rimet F, Tankovic MS, Trobajo R, Vasselon V, Zimmermann J, Bouchez A (2022) Recommendations for the preservation of environmental samples in diatom metabarcoding studies. Metabarcoding and Metagenomics 6: e85844. https://doi.org/10.3897/mbmg.6.85844
Data 2
Supplementary material 3 from: Baricevic A, Chardon C, Kahlert M, Karjalainen SM, Pfannkuchen DM, Pfannkuchen M, Rimet F, Tankovic MS, Trobajo R, Vasselon V, Zimmermann J, Bouchez A (2022) Recommendations for the preservation of environmental samples in diatom metabarcoding studies. Metabarcoding and Metagenomics 6: e85844. https://doi.org/10.3897/mbmg.6.85844
Data 3
Supplementary material 1 from: Baricevic A, Chardon C, Kahlert M, Karjalainen SM, Pfannkuchen DM, Pfannkuchen M, Rimet F, Tankovic MS, Trobajo R, Vasselon V, Zimmermann J, Bouchez A (2022) Recommendations for the preservation of environmental samples in diatom metabarcoding studies. Metabarcoding and Metagenomics 6: e85844. https://doi.org/10.3897/mbmg.6.85844
Data 1
Supplementary material 1 from: Demetriou J, Radea C, Peyton JM, Groom Q, Roques A, Rabitsch W, Seraphides N, Arianoutsou M, Roy HE, Martinou AF (2023) The Alien to Cyprus Entomofauna (ACE) database: a review of the current status of alien insects (Arthropoda, Insecta) including an updated species checklist, discussion on impacts and recommendations for informing management. NeoBiota 83: 11-42. https://doi.org/10.3897/neobiota.83.96823
Checklist of alien insects of Cyprus
Supplementary material 2 from: Demetriou J, Radea C, Peyton JM, Groom Q, Roques A, Rabitsch W, Seraphides N, Arianoutsou M, Roy HE, Martinou AF (2023) The Alien to Cyprus Entomofauna (ACE) database: a review of the current status of alien insects (Arthropoda, Insecta) including an updated species checklist, discussion on impacts and recommendations for informing management. NeoBiota 83: 11-42. https://doi.org/10.3897/neobiota.83.96823
Alien biological control agents intentionally introduced to Cyprus
Exploring the Automatic Recommendation of Composite Refactorings
<p>Dataset of the survey with developers to explore a recommender of refactoring. </p>
Usage Requirements on Recommender Systems for a Web Platform for Continuing Education in Public Transport in Germany
<p>The dataset contains usage requirements on recommender systems for a web platform for continuing education in public transport in Germany. The underlying data was collected in 28 stakeholder interviews. </p>
Enhancing MovieLens Dataset: Enriching Recommendations with Audio Information, Transcriptions, and Metadata
<p>Nowadays, there are lots of datasets available for training and experimentation in the field of recommender systems. Specifically, in the recommendation of audiovisual content, the MovieLens dataset is a prominent example. It is focused on the user-item relationship, providing actual interaction data between users and movies. However, although movies can be described with several characteristics, this dataset only offers limited information about the movie genres. </p> <p>In this work, we propose enriching the MovieLens dataset by incorporating metadata available on the web (such as cast, description, keywords, etc.) and movie trailers. By leveraging the trailers, we extract audio information and generate transcriptions for each trailer, introducing a crucial textual dimension to the dataset. The audio information was extracted by the waveform and frequency analysis, followed by the application of dimensionality reduction techniques. For the transcription generation, the deep learning model Whisper was used. Finally, metadata was obtained from TMDB, and the BERT model was applied to extract embeddings.</p> <p>These additional attributes enrich the original dataset, providing deeper and more precise analysis. Then, the use of this extended and enhanced dataset could drive significant advancements in recommendation systems, enhancing user experiences by providing more relevant and tailored movie recommendations based on their tastes and preferences. </p>
E-Course Recommendation System
<p>Students data with all attributes for course recommendation system</p>
Files for Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation
<p>This are the files needed for running the experiments of "Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation".</p> <ul> <li> <p>listening_events.tsv.bz2 : Dataset excerpt from <a href="http://www.cp.jku.at/datasets/LFM-2b/">LFM-2b</a>, before filtering (see submission for details)</p> </li> <li> <p>BPR_item_embeddings.tsv.bz2 : Item embeddings obtained from the pre-trained BPR instance</p> </li> <li> <p>user_split.tar.bz2 : csv file of the listening history of each user</p> </li> <li> <p>2023_recsys_actr_poster.pdf ; poster presented at RecSys 2023</p> </li> </ul> <p>The code for running the experiments is available on <a href="https://github.com/hcai-mms/actr">GitHub</a><br> <br> If you use these files, please cite<br> </p> <blockquote> <p>@inproceedings{10.1145/3604915.3608838,<br> author = {Moscati, Marta and Wallmann, Christian and Reiter-Haas, Markus and Kowald, Dominik and Lex, Elisabeth and Schedl, Markus},<br> title = {Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation},<br> year = {2023},<br> isbn = {9798400702419},<br> publisher = {Association for Computing Machinery},<br> address = {New York, NY, USA},<br> url = {https://doi.org/10.1145/3604915.3608838},<br> doi = {10.1145/3604915.3608838},<br> abstract = {Music listening sessions often consist of sequences including repeating tracks. Modeling such relistening behavior with models of human memory has been proven effective in predicting the next track of a session. However, these models intrinsically lack the capability of recommending novel tracks that the target user has not listened to in the past. Collaborative filtering strategies, on the contrary, provide novel recommendations by leveraging past collective behaviors but are often limited in their ability to provide explanations. To narrow this gap, we propose four hybrid algorithms that integrate collaborative filtering with the cognitive architecture ACT-R. We compare their performance in terms of accuracy, novelty, diversity, and popularity bias, to baselines of different types, including pure ACT-R, kNN-based, and neural-networks-based approaches. We show that the proposed algorithms are able to achieve the best performances in terms of novelty and diversity, and simultaneously achieve a higher accuracy of recommendation with respect to pure ACT-R models. Furthermore, we illustrate how the proposed models can provide explainable recommendations.},<br> booktitle = {Proceedings of the 17th ACM Conference on Recommender Systems},<br> pages = {840–847},<br> numpages = {8},<br> keywords = {Music Recommender Systems, Psychology-Informed Recommender Systems, Collaborative Filtering, Adaptive Control Thought-Rational (ACT-R), Sequential Recommendation, Explainability},<br> location = {Singapore, Singapore},<br> series = {RecSys '23}<br> }</p> </blockquote> <p>This research was funded in whole, or in part, by the Austrian Science Funds (FWF): P33526 and DFH-23, and by the State of Upper Austria and the Federal Ministry of Education, Science, and Research, through grant LIT-2020-9-SEE-113.</p>
A Learning Algorithm for MDI Individuals With Type 1 Diabetes to Adjust Recommendations for High Fat Meals and Exercise Management
ClinicalTrials.gov study NCT05041621. IPD Sharing: YES. Countries: 1. Publications: 0.
A Re-licensing Study to Assess the Efficacy of Inflexal V Formulated With WHO Recommended 2008/2009 Influenza Virus Strains for the Northern Hemisphere
ClinicalTrials.gov study NCT01303510. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Implementation of Red Blood Cell Transfusion Recommendations in the Pediatric Intensive Care Unit
ClinicalTrials.gov study NCT07108374. IPD Sharing: NO. Countries: 1. Publications: 0.
Developing Recommendations to Support Therapeutic Alliance in Eating Disorders Inpatient Treatment
ClinicalTrials.gov study NCT06961032. IPD Sharing: NO. Countries: 1. Publications: 0.
Barriers to Adherence to Recommended Follow-up in Women With a History of Gestational Diabetes
ClinicalTrials.gov study NCT01681147. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.