5,790 research outputs found
#nowplaying
No full text<p>This dataset contains a dump of the #nowplaying dataset which contains so-called listening events of users who publish the music they are currently listening to on Twitter. In particular, this dataset includes tracks which have been tweeted using the hashtags #nowplaying, #listento or #listeningto. In this dataset, we provide the track and artist of a listening event and metadata on the tweet (date sent, user, source). Furthermore, we provide a mapping of tracks to its respective Musicbrainz identifiers. The dataset features a total of 126 mio listening events.</p>
<p>This archive contains the nowplaying.csv file, the main file which contains the following fields:</p>
<ul>
<li>user id (each user is identified by a unique hash value)</li>
<li>source of the tweet (how it was sent; as provided by the Twitter API)</li>
<li>timestamp of the time the tweet underlying the listening event was sent</li>
<li>track title</li>
<li>artist name</li>
<li>musicbrainz identifier of the recording (cf. https://musicbrainz.org/)</li>
</ul>
<p>In case you make use of our dataset in a scientific setting, we kindly ask you to cite the following paper: </p>
<p><br>
Eva Zangerle, Martin Pichl, Wolfgang Gassler, and Günther Specht. 2014. #nowplaying Music Dataset: Extracting Listening Behavior from Twitter. In Proceedings of the First International Workshop on Internet-Scale Multimedia Management (WISMM '14). ACM, New York, NY, USA, 21-26.</p>
<p>If you have any questions or suggestions regarding the dataset, please do not hesitate to contact Eva Zangerle ([email protected]).</p>
Hit Song Prediction (Million Song Dataset and Audio Features)
No full text<p><strong>Hit Song Prediction Dataset</strong></p>
<p>This dataset is based on the Million Song Dataset (MSD), which contains one million songs that are representative for western commercial music released between 1922 and 2011. The dataset contains release year information for 515,576 of the MSD songs. Please refer to http://millionsongdataset.com/ for further information on the million song dataset.</p>
<p>For our hit song prediction experiments, we extract high- and low-level audio features using the Essentia toolkit (cf. https://essentia.upf.edu/). For the high-level features, we make use of the pre-trained classifiers as provided by Essentia. For a detailed description of the features, please visit the Essentia documentation.</p>
<p><br>
The dataset hence contains:</p>
<ul>
<li><strong>Audio features</strong>: the compressed msd_audio_features.tar.gz file contains the low- and high-level features for each track, stored as json files. Please note that we organize all MSD audio feature files based on the track's identifier with one folder holding all tracks with the same first letter of the track identifier to keep the files manageable. For each track, we provide two files: one containing the high-level and one containing the low-level features extracted by Essentia.</li>
<li><strong>Billboard data:</strong> the folder billboard_data contains two files: msd_bb_matches.csv contains information about the MSD tracks that were also featured in the Billboard Hot 100 charts. Here, we provide the MSD id, Echo Nest id, artist name, track title, release year, peak position in Billboard charts and the number of weeks in the charts. The second file, msd_bb_non_matches.csv contains meta-information about the tracks of the MSD that were not featured in the Billboard Hot 100 and hence were used as negative samples. Here, we provide the MSD id, Echo Nest id, artist name, track title and the release year.</li>
</ul>
<p><br>
If you make use of the dataset, please kindly cite the following paper:</p>
<p>Eva Zangerle, Michael Vötter, Ramona Huber, and Yi-Hsuan Yang. Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In Proceedings of the 20th International Society for Music Information Retrieval Conference 2019 (ISMIR 2019), 2019.</p>
<p><br>
@inproceedings{zangerle_ismir19,<br>
title = {{Hit Song Prediction: Leveraging Low- and High-Level Audio Features}},<br>
author = {Eva Zangerle and Ramona Huber and Michael V\"{o}tter and Yi-Hsuan Yang},<br>
year = {2019},<br>
booktitle = {{Proceedings of the 20th International Society for Music Information Retrieval Conference 2019 (ISMIR 2019)}},<br>
}</p>
Spotify Playlists Dataset
No full text<p><br>
This dataset is based on the subset of users in the #nowplaying dataset who publish their #nowplaying tweets via Spotify. In principle, the dataset holds users, their playlists and the tracks contained in these playlists.</p>
<p>The csv-file holding the dataset contains the following columns: "user_id", "artistname", "trackname", "playlistname", where</p>
<ul>
<li>user_id is a hash of the user's Spotify user name</li>
<li>artistname is the name of the artist</li>
<li>trackname is the title of the track and</li>
<li>playlistname is the name of the playlist that contains this track.</li>
</ul>
<p>The separator used is , each entry is enclosed by double quotes and the escape character used is \.</p>
<p>A description of the generation of the dataset and the dataset itself can be found in the following paper:</p>
<p>Pichl, Martin; Zangerle, Eva; Specht, Günther: "Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?" in 15th IEEE International Conference on Data Mining Workshops (ICDM 2015), pp. 1360-1365, IEEE, Atlantic City, 2015.<br>
</p>
Culture-Aware Music Recommendation Dataset
No full textLFM-1b dataset extended by acoustic track features and cultural cues describing users
This dataset is based on the LFM-1b dataset (cf. http://www.cp.jku.at/datasets/LFM-1b/), however, adds acoustic features describing the tracks to the original dataset as well as cultural aspects describing users (taken from Hofstede's six dimension model and the World Happiness Report) on the country-level.
For the creation of the dataset, we extract all users for which the original dataset contains country information for. We extract the listening events of these users and match the tracks against the Spotify API to subsequently retrieve the acoustic features of these tracks (cf. [Spotify Audio Feature Description](https://developer.spotify.com/documentation/web-api/reference/object-model/#audio-features-object)). The final dataset contains only events of users with country information and tracks with acoustic features, which can be matched with the country-level data of the World Happiness Report and Hofstede's cultural dimensions to add cultural and socio-economic aspects for users.
This new dataset contains
55,190 users
3,471,884 tracks including acoustic features
351,469,333 listening events of those users for tracks we have obtained acoustic features for
Hofstede's cultural dimensions for 47 countries
World Happiness Report (WHR) data for 164 countries
Files
All files are tab-separated, with no quoting of strings. The dataset contains the following files, whose content we describe in more detail in the following parts.
* acoustic_features_lfm_id.tsv: acoustic features for all tracks in the dataset, identified by their LFM track identifier
* events.tsv: listening events for all users
* hofstede.tsv: Hofstede's cultural dimensions
* users.tsv: user metadata
* world_happiness_report_2018.tsv: World Happiness Report data
For further information on the contents of these files, please cf. the Readme file.</p
#nowplaying-rs
No full text<p>The nowplaying-rs dataset features context- and content features of listening events. It contains 11.6 million music listening events of 139K users and 346K tracks collected from Twitter. The dataset comes with a rich set of item content features and user context features, as well as timestamps of the listening events. Moreover, some of the user context features imply the cultural origin of the users, and some others - like hashtags - give clues to the emotional state of a user underlying a listening event.</p>
<p>The dataset contains three files:</p>
<ul>
<li>user_track_hashtag_timestamp.csv contains basic information about each listening event. For each listening event, we provide an id, the user_id, track_id, hashtag, created_at </li>
<li>context_content_features.csv: contains all context and content features. For each listening event, we provide the id of the event, user_id, track_id, artist_id, content features regarding the track mentioned in the event (instrumentalness, liveness, speechiness, danceability, valence, loudness, tempo, acousticness, energy, mode, key) and context features regarding the listening event (coordinates (as geoJSON), place (as geoJSON), geo (as geoJSON), tweet_language, created_at, user_lang, time_zone, entities contained in the tweet).</li>
<li>sentiment_values.csv contains sentiment information for hashtags. It contains the hashtag itself and the sentiment values gathered via four different sentiment dictionaries: AFINN, Opinion Lexicon, Sentistrength Lexicon and vader. For each of these dictionaries we list the minimum, maximum, sum and average of all sentiments of the tokens of the hashtag (if available, else we list empty values). However, as most hashtags only consist of a single token, these values are equal in most cases. Please note that the lexica are rather diverse and therefore, are able to resolve very different terms against a score. Hence, the resulting csv is rather sparse. The file contains the following comma-separated values: <hashtag, vader_min, vader_max, vader_sum,vader_avg, afinn_min, afinn_max, afinn_sum, afinn_avg, ol_min, ol_max, ol_sum, ol_avg, ss_min, ss_max, ss_sum, ss_avg >, where we abbreviate all scores gathered over the Opinion Lexicon with the prefix 'ol'. Similarly, 'ss' stands for SentiStrength. </li>
</ul>
<p>Please note that user_track_hashtag_timestamp.csv and context_content_features.csv partly provide the same features. We deliberately chose to do so to be able to provide useable files that do not have to be matched and joined with each other to perform e.g., simple recommendation tasks.</p>
<p>Please also find the training and test-splits for the dataset in this repo. Also, Asmita provides prototypical implementations of a context-aware recommender system based on the dataset at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM.</p>
<p><br>
If you make use of this dataset, please cite the following paper where we describe and experiment with the dataset:</p>
<p>@inproceedings{smc18,<br>
title = {#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems},<br>
author = {Asmita Poddar and Eva Zangerle and Yi-Hsuan Yang},<br>
url = {http://mac.citi.sinica.edu.tw/~yang/pub/poddar18smc.pdf},<br>
year = {2018},<br>
date = {2018-07-04},<br>
booktitle = {Proceedings of the 15th Sound & Music Computing Conference},<br>
address = {Limassol, Cyprus},<br>
note = {code at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM},<br>
tppubtype = {inproceedings}<br>
}</p>
I Remember column in which author Eva LaPlante writes of her visits to sites a
No full textI Remember column in which author Eva LaPlante writes of her visits to sites associated with E. B. White and his book Charlotte\u27s Web
Evaluation Perspectives of Recommender Systems: Driving Research and Education (Dagstuhl Seminar 24211)
No full textThis report documents the program and the outcomes of Dagstuhl Seminar 24211, "Evaluation Perspectives of Recommender Systems: Driving Research and Education", which brought together 41 participants from 16 countries.
The seminar brought together distinguished researchers and practitioners from the recommender systems community, representing a range of expertise and perspectives. The primary objective was to address current challenges and advance the ongoing discourse on the evaluation of recommender systems. The participants' diverse backgrounds and perspectives on evaluation significantly contributed to the discourse on this subject.
The seminar featured eight presentations on current challenges in the evaluation of recommender systems. These presentations sparked the general discussion and facilitated the formation of groups around these topics. As a result, five working groups were established, each focusing on the following areas: theory of evaluation, fairness evaluation, best-practices for offline evaluations of recommender systems, multistakeholder and multimethod evaluation, and evaluating the long-term impact of recommender systems
Eva Murray, author of Well Out to Sea , has been a resident of Matinicus Island
No full textEva Murray, author of Well Out to Sea , has been a resident of Matinicus Island since she moved there to teach at the island\u27s one-room schoolhouse in 1987. She discusses the differences between writing from an island and writing about an island as well as her efforts to dispel some stereotypes and myths about Matinicus through her writing
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management / Music4All-Onion : a large-scale multi-faceted content-centric music recommendation dataset
No full textWhen we appreciate a piece of music, it is most naturally because of its content, including rhythmic, tonal, and timbral elements as well as its lyrics and semantics. This suggests that the human affinity for music is inherently content-driven. This kind of information is, however, still frequently neglected by mainstream recommendation models based on collaborative filtering that rely solely on user-item interactions to recommend items to users. A major reason for this neglect is the lack of standardized datasets that provide both collaborative and content information. The work at hand addresses this shortcoming by introducing Music4All-Onion, a large-scale, multi-modal music dataset. The dataset expands the Music4All dataset by including 26 additional audio, video, and metadata characteristics for 109,269 music pieces. In addition, it provides a set of 252,984,396 listening records of 119,140 users, extracted from the online music platform Last.fm, which allows leveraging user-item interactions as well. We organize distinct item content features in an onion model according to their semantics, and perform a comprehensive examination of the impact of different layers of this model (e.g., audio features, user-generated content, and derivative content) on content-driven music recommendation, demonstrating how various content features influence accuracy, novelty, and fairness of music recommendation systems. In summary, with Music4All-Onion, we seek to bridge the gap between collaborative filtering music recommender systems and content-centric music recommendation requirements.Fonds zur Förderung der Wissenschaftlichen Forschung P33526 DFH-23Version of recor
Obesity of Older School Age Children Based on Physical Activity and Eating Habits
No full textTITLE: Obesity of Older School Age Children Based on Physical Activity and Eating Habits AUTHOR: Bc. Eva Lamačová DEPARTMENT: Department of Education SUPERVISOR: PaedDr. Eva Marádová, CSc. ABSTRACT: This thesis deals with the obesity of older school age children and their dependence on eating and exercising habits. The theoretical part summarizes knowledge regarding overweight and obesity, disease diagnosis, epidemiological trends and complications related to obesity. Emphasis is put on the evaluation of reasons to why this problem occurs, such as disease prevention, dietary and exercise recommendations, its therapy and inclusion of the issue with the secondary school curriculum. The practical part contains research focused on the nutritional status, eating and exercise habits of pupils in secondary school. The survey results are designed for use in pedagogical practice. Usage is targeted for obesity prevention in this age group during Physical Education and Health Education subjects. KEYWORDS: Obesity, overweight, eating habits, exercising habit
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