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2020 – today
- 2022
[j39]Tommaso Di Noia, Nava Tintarev, Panagiota Fatourou, Markus Schedl:
Recommender systems under European AI regulations. Commun. ACM 65(4): 69-73 (2022)- [j38]Mihai Gabriel Constantin
, Liviu-Daniel Stefan, Bogdan Ionescu, Claire-Hélène Demarty, Mats Sjöberg, Markus Schedl, Guillaume Gravier:
Affect in Multimedia: Benchmarking Violent Scenes Detection. IEEE Trans. Affect. Comput. 13(1): 347-366 (2022) - [c180]Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, Navid Rekabsaz:
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements? BIAS 2022: 104-116 - [c179]Markus Schedl, Stefan Brandl, Oleg Lesota, Emilia Parada-Cabaleiro, David Penz, Navid Rekabsaz:
LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis. CHIIR 2022: 337-341 - [c178]Elisabeth Lex, Markus Schedl:
Psychology-informed Recommender Systems: A Human-Centric Perspective on Recommender Systems. CHIIR 2022: 367-368 - [c177]Alessandro B. Melchiorre, David Penz, Christian Ganhör, Oleg Lesota, Vasco Fragoso, Florian Friztl, Emilia Parada-Cabaleiro, Franz Schubert, Markus Schedl:
EmoMTB: Emotion-aware Music Tower Blocks. ICMR 2022: 206-210 - [i27]Klara Krieg, Emilia Parada-Cabaleiro, Gertraud Medicus, Oleg Lesota, Markus Schedl, Navid Rekabsaz:
Grep-BiasIR: A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results. CoRR abs/2201.07754 (2022) - [i26]Darius Afchar, Alessandro B. Melchiorre, Markus Schedl, Romain Hennequin, Elena V. Epure, Manuel Moussallam:
Explainability in Music Recommender Systems. CoRR abs/2201.10528 (2022) - [i25]Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, Navid Rekabsaz:
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements? CoRR abs/2203.01731 (2022) - [i24]Christian Ganhör, David Penz, Navid Rekabsaz, Oleg Lesota, Markus Schedl:
Unlearning Protected User Attributes in Recommendations with Adversarial Training. CoRR abs/2206.04500 (2022) - [i23]Peter Müllner, Markus Schedl, Elisabeth Lex, Dominik Kowald:
ReuseKNN: Neighborhood Reuse for Privacy-Aware Recommendations. CoRR abs/2206.11561 (2022) - 2021
- [j37]Dominik Kowald, Peter Müllner, Eva Zangerle, Christine Bauer, Markus Schedl, Elisabeth Lex:
Support the underground: characteristics of beyond-mainstream music listeners. EPJ Data Sci. 10(1): 14 (2021) - [j36]Elisabeth Lex, Dominik Kowald, Paul Seitlinger, Thi Ngoc Trang Tran, Alexander Felfernig, Markus Schedl:
Psychology-informed Recommender Systems. Found. Trends Inf. Retr. 15(2): 134-242 (2021) - [j35]Alessandro B. Melchiorre, Navid Rekabsaz, Emilia Parada-Cabaleiro, Stefan Brandl, Oleg Lesota, Markus Schedl:
Investigating gender fairness of recommendation algorithms in the music domain. Inf. Process. Manag. 58(5): 102666 (2021) - [c176]Tomislav Duricic, Dominik Kowald, Markus Schedl, Elisabeth Lex:
My friends also prefer diverse music: homophily and link prediction with user preferences for mainstream, novelty, and diversity in music. ASONAM 2021: 447-454 - [c175]Alessandro B. Melchiorre, Verena Haunschmid, Markus Schedl, Gerhard Widmer:
LEMONS: Listenable Explanations for Music recOmmeNder Systems. ECIR (2) 2021: 531-536 - [c174]Oleg Lesota, Navid Rekabsaz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, Markus Schedl:
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models. ICTIR 2021: 185-195 - [c173]Emilia Parada-Cabaleiro, Maximilian Schmitt, Anton Batliner, Björn W. Schuller, Markus Schedl:
Automatic Recognition of Texture in Renaissance Music. ISMIR 2021: 509-516 - [c172]Harald Victor Schweiger, Emilia Parada-Cabaleiro, Markus Schedl:
Does Track Sequence in User-generated Playlists Matter?. ISMIR 2021: 618-625 - [c171]Alexander Krauck, David Penz, Markus Schedl:
Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features. RecSys Challenge 2021: 39-43 - [c170]Oleg Lesota, Alessandro B. Melchiorre, Navid Rekabsaz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl:
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected? RecSys 2021: 601-606 - [c169]Markus Reiter-Haas, Emilia Parada-Cabaleiro, Markus Schedl, Elham Motamedi, Marko Tkalcic, Elisabeth Lex:
Predicting Music Relistening Behavior Using the ACT-R Framework. RecSys 2021: 702-707 - [c168]Navid Rekabsaz, Simone Kopeinik, Markus Schedl:
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation of BERT Rankers. SIGIR 2021: 306-316 - [c167]Rosie Jones, Hamed Zamani, Markus Schedl, Ching-Wei Chen, Sravana Reddy, Ann Clifton, Jussi Karlgren, Helia Hashemi, Aasish Pappu, Zahra Nazari, Longqi Yang, Oguz Semerci, Hugues Bouchard, Ben Carterette:
Current Challenges and Future Directions in Podcast Information Access. SIGIR 2021: 1554-1565 - [c166]Navid Rekabsaz, Oleg Lesota, Markus Schedl, Jon Brassey, Carsten Eickhoff:
TripClick: The Log Files of a Large Health Web Search Engine. SIGIR 2021: 2507-2513 - [c165]Christopher Stelzmüller, Sebastian Tanzer, Markus Schedl:
Cross-city Analysis of Location-based Sentiment in User-generated Text. WWW (Companion Volume) 2021: 339-346 - [i22]Dominik Kowald, Peter Müllner, Eva Zangerle, Christine Bauer, Markus Schedl, Elisabeth Lex:
Support the Underground: Characteristics of Beyond-Mainstream Music Listeners. CoRR abs/2102.12188 (2021) - [i21]Navid Rekabsaz, Oleg Lesota, Markus Schedl, Jon Brassey, Carsten Eickhoff:
TripClick: The Log Files of a Large Health Web Search Engine. CoRR abs/2103.07901 (2021) - [i20]Navid Rekabsaz, Simone Kopeinik, Markus Schedl:
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation for BERT Rankers. CoRR abs/2104.13640 (2021) - [i19]Rosie Jones, Hamed Zamani, Markus Schedl, Ching-Wei Chen, Sravana Reddy, Ann Clifton, Jussi Karlgren, Helia Hashemi, Aasish Pappu, Zahra Nazari, Longqi Yang, Oguz Semerci, Hugues Bouchard, Ben Carterette:
Current Challenges and Future Directions in Podcast Information Access. CoRR abs/2106.09227 (2021) - [i18]Oleg Lesota, Navid Rekabsaz, Daniel Cohen, Klaus Antonius Grasserbauer, Carsten Eickhoff, Markus Schedl:
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models. CoRR abs/2106.13618 (2021) - [i17]Yashar Deldjoo, Markus Schedl, Peter Knees:
Content-driven Music Recommendation: Evolution, State of the Art, and Challenges. CoRR abs/2107.11803 (2021) - [i16]Markus Reiter-Haas, Emilia Parada-Cabaleiro, Markus Schedl, Elham Motamedi, Marko Tkalcic, Elisabeth Lex:
Predicting Music Relistening Behavior Using the ACT-R Framework. CoRR abs/2108.02138 (2021) - [i15]Oleg Lesota, Alessandro B. Melchiorre, Navid Rekabsaz, Stefan Brandl, Dominik Kowald, Elisabeth Lex, Markus Schedl:
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected? CoRR abs/2108.06973 (2021) - [i14]Tomislav Duricic, Dominik Kowald, Markus Schedl, Elisabeth Lex:
My friends also prefer diverse music: homophily and link prediction with user preferences for mainstream, novelty, and diversity in music. CoRR abs/2111.00562 (2021) - 2020
- [j34]Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, Gabriella Pasi:
Recommender Systems Leveraging Multimedia Content. ACM Comput. Surv. 53(5): 106:1-106:38 (2020) - [j33]Markus Schedl, Christine Bauer, Wolfgang Reisinger, Dominik Kowald, Elisabeth Lex:
Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes. Frontiers Artif. Intell. 3: 508725 (2020) - [j32]Eva Zangerle, Martin Pichl, Markus Schedl:
User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues. Trans. Int. Soc. Music. Inf. Retr. 3(1): 1-16 (2020) - [j31]Elisabeth Lex, Dominik Kowald, Markus Schedl:
Modeling Popularity and Temporal Drift of Music Genre Preferences. Trans. Int. Soc. Music. Inf. Retr. 3(1): 17-30 (2020) - [j30]Peter Knees, Markus Schedl, Masataka Goto:
Intelligent User Interfaces for Music Discovery. Trans. Int. Soc. Music. Inf. Retr. 3(1): 165-179 (2020) - [j29]Marko Tkalcic, Markus Schedl, Peter Knees:
Preface to the Special Issue on user modeling for personalized interaction with music. User Model. User Adapt. Interact. 30(2): 195-198 (2020) - [c164]Dominik Kowald, Markus Schedl, Elisabeth Lex:
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study. ECIR (2) 2020: 35-42 - [c163]Meijun Liu, Eva Zangerle, Xiao Hu, Alessandro B. Melchiorre, Markus Schedl:
Pandemics, music, and collective sentiment: evidence from the outbreak of COVID-19. ISMIR 2020: 157-165 - [c162]Kyle Robinson, Dan Brown, Markus Schedl:
User Insights on Diversity in Music Recommendation Lists. ISMIR 2020: 446-453 - [c161]Markus Schedl, Michael Mayr, Peter Knees:
Music Tower Blocks: Multi-Faceted Exploration Interface for Web-Scale Music Access. ICMR 2020: 388-392 - [c160]Alessandro B. Melchiorre, Eva Zangerle, Markus Schedl:
Personality Bias of Music Recommendation Algorithms. RecSys 2020: 533-538 - [c159]Navid Rekabsaz, Markus Schedl:
Do Neural Ranking Models Intensify Gender Bias? SIGIR 2020: 2065-2068 - [c158]Alessandro B. Melchiorre, Markus Schedl:
Personality Correlates of Music Audio Preferences for Modelling Music Listeners. UMAP 2020: 313-317 - [e8]Julie Cumming, Jin Ha Lee, Brian McFee, Markus Schedl, Johanna Devaney, Cory McKay, Eva Zangerle, Timothy de Reuse:
Proceedings of the 21th International Society for Music Information Retrieval Conference, ISMIR 2020, Montreal, Canada, October 11-16, 2020. 2020, ISBN 978-0-9813537-1-5 [contents] - [i13]Dominik Kowald, Elisabeth Lex, Markus Schedl:
Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations. CoRR abs/2003.10699 (2020) - [i12]Navid Rekabsaz, Markus Schedl:
Do Neural Ranking Models Intensify Gender Bias? CoRR abs/2005.00372 (2020) - [i11]Markus Schedl, Christine Bauer, Wolfgang Reisinger, Dominik Kowald, Elisabeth Lex:
Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes. CoRR abs/2009.09935 (2020)
2010 – 2019
- 2019
- [j28]Markus Schedl:
Deep Learning in Music Recommendation Systems. Frontiers Appl. Math. Stat. 5: 44 (2019) - [j27]Andreu Vall, Massimo Quadrana, Markus Schedl, Gerhard Widmer:
Order, context and popularity bias in next-song recommendations. Int. J. Multim. Inf. Retr. 8(2): 101-113 (2019) - [j26]Thomas Krismayer, Markus Schedl, Peter Knees, Rick Rabiser:
Predicting user demographics from music listening information. Multim. Tools Appl. 78(3): 2897-2920 (2019) - [j25]Bruce Ferwerda, Emily Yang, Markus Schedl, Marko Tkalcic:
Personality and taxonomy preferences, and the influence of category choice on the user experience for music streaming services. Multim. Tools Appl. 78(14): 20157-20190 (2019) - [j24]Hamed Zamani, Markus Schedl, Paul Lamere, Ching-Wei Chen:
An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation. ACM Trans. Intell. Syst. Technol. 10(5): 57:1-57:21 (2019) - [j23]Yashar Deldjoo, Maurizio Ferrari Dacrema, Mihai Gabriel Constantin, Hamid Eghbal-zadeh, Stefano Cereda, Markus Schedl, Bogdan Ionescu, Paolo Cremonesi:
Movie genome: alleviating new item cold start in movie recommendation. User Model. User Adapt. Interact. 29(2): 291-343 (2019) - [j22]Andreu Vall, Matthias Dorfer, Hamid Eghbal-zadeh, Markus Schedl, Keki Burjorjee, Gerhard Widmer:
Feature-combination hybrid recommender systems for automated music playlist continuation. User Model. User Adapt. Interact. 29(2): 527-572 (2019) - [c157]Yashar Deldjoo, Markus Schedl:
Retrieving Relevant and Diverse Movie Clips Using the MFVCD-7K Multifaceted Video Clip Dataset. CBMI 2019: 1-4 - [c156]Yashar Deldjoo, Markus Schedl, Mehdi Elahi:
Movie Genome Recommender: A Novel Recommender System Based on Multimedia Content. CBMI 2019: 1-4 - [c155]Christine Bauer, Markus Schedl:
Cross-country User Connections in an Online Social Network for Music. CHI Extended Abstracts 2019 - [c154]Christine Bauer, Markus Schedl:
A Cross-Country Investigation of User Connection Patterns in Online Social Networks. HICSS 2019: 1-10 - [c153]Peter Knees, Markus Schedl, Masataka Goto:
Intelligent User Interfaces for Music Discovery: The Past 20 Years and What's to Come. ISMIR 2019: 44-53 - [c152]Peter Knees, Markus Schedl, Rebecca Fiebrink:
Intelligent music interfaces for listening and creation. IUI Companion 2019: 135-136 - [c151]Yashar Deldjoo, Benny Kille, Markus Schedl, Andreas Lommatzsch, Jialie Shen:
The 2019 Multimedia for Recommender System Task: MovieREC and NewsREEL at MediaEval. MediaEval 2019 - [c150]Hossein A. Rahmani, Yashar Deldjoo, Markus Schedl:
A Regression Approach to Movie Rating Prediction Using Multimedia Content and Metadata. MediaEval 2019 - [c149]Christine Bauer, Markus Schedl, Vera Angerer, Stefan Wegenkittl:
Tastalyzer: Audiovisual Exploration of Urban and Rural Variations in Music Taste. ACM Multimedia 2019: 1044-1046 - [c148]Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema, Markus Schedl, Paolo Cremonesi:
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features. IntRS@RecSys 2019: 72-76 - [c147]Markus Schedl:
Genre Differences of Song Lyrics and Artist Wikis: An Analysis of Popularity, Length, Repetitiveness, and Readability. WWW 2019: 3201-3207 - [i10]Dominik Kowald, Elisabeth Lex, Markus Schedl:
Modeling Artist Preferences of Users with Different Music Consumption Patterns for Fair Music Recommendations. CoRR abs/1907.09781 (2019) - [i9]Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema, Markus Schedl, Paolo Cremonesi:
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features. CoRR abs/1908.11055 (2019) - [i8]Dominik Kowald, Markus Schedl, Elisabeth Lex:
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study. CoRR abs/1912.04696 (2019) - [i7]Christine Bauer, Markus Schedl:
Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems. CoRR abs/1912.06933 (2019) - [i6]Markus Schedl, Christine Bauer:
Online Music Listening Culture of Kids and Adolescents: Listening Analysis and Music Recommendation Tailored to the Young. CoRR abs/1912.11564 (2019) - 2018
- [j21]Markus Schedl, Hamed Zamani, Ching-Wei Chen, Yashar Deldjoo, Mehdi Elahi:
Current challenges and visions in music recommender systems research. Int. J. Multim. Inf. Retr. 7(2): 95-116 (2018) - [j20]Markus Schedl, Christine Bauer:
An Analysis of Global and RegionalMainstreaminess for Personalized MusicRecommender Systems. J. Mobile Multimedia 14(1): 95-112 (2018) - [j19]Markus Schedl, Emilia Gómez, Erika S. Trent, Marko Tkalcic, Hamid Eghbal-Zadeh, Agustín Martorell:
On the Interrelation Between Listener Characteristics and the Perception of Emotions in Classical Orchestra Music. IEEE Trans. Affect. Comput. 9(4): 507-525 (2018) - [c146]Christine Bauer, Markus Schedl:
On the Importance of Considering Country-specific Aspects on the Online-Market: An Example of Music Recommendation Considering Country-Specific Mainstream. HICSS 2018: 1-10 - [c145]Mhd Mousa Hamad, Marcin Skowron, Markus Schedl:
Regressing Controversy of Music Artists from Microblogs. ICTAI 2018: 548-555 - [c144]Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, Gabriella Pasi:
Content-Based Multimedia Recommendation Systems: Definition and Application Domains. IIR 2018 - [c143]Christine Bauer, Markus Schedl:
Investigating Cross-Country Relationship between Users' Social Ties and Music Mainstreaminess. ISMIR 2018: 678-686 - [c142]Christian Esswein, Markus Schedl, Eva Zangerle:
geMsearch: Personalized Explorative Music Search. IUI Workshops 2018 - [c141]Yashar Deldjoo, Mihai Gabriel Constantin, Athanasios Dritsas, Bogdan Ionescu, Markus Schedl:
The MediaEval 2018 Movie Recommendation Task: Recommending Movies Using Content. MediaEval 2018 - [c140]Stevan Rudinac, Tat-Seng Chua, Nicolás E. Díaz Ferreyra, Gerald Friedland, Tatjana Gornostaja, Benoit Huet, Rianne Kaptein, Krister Lindén, Marie-Francine Moens, Jaakko Peltonen, Miriam Redi, Markus Schedl, David A. Shamma, Alan F. Smeaton, Lexing Xie:
Rethinking Summarization and Storytelling for Modern Social Multimedia. MMM (1) 2018: 632-644 - [c139]Yashar Deldjoo, Mihai Gabriel Constantin, Bogdan Ionescu, Markus Schedl, Paolo Cremonesi:
MMTF-14K: a multifaceted movie trailer feature dataset for recommendation and retrieval. MMSys 2018: 450-455 - [c138]Yashar Deldjoo, Mihai Gabriel Constantin, Hamid Eghbal-Zadeh, Bogdan Ionescu, Markus Schedl, Paolo Cremonesi:
Audio-visual encoding of multimedia content for enhancing movie recommendations. RecSys 2018: 455-459 - [c137]Ching-Wei Chen, Paul Lamere, Markus Schedl, Hamed Zamani:
Recsys challenge 2018: automatic music playlist continuation. RecSys 2018: 527-528 - [c136]Yashar Deldjoo, Markus Schedl, Balázs Hidasi, Peter Knees:
Multimedia recommender systems. RecSys 2018: 537-538 - [c135]Andreu Vall, Matthias Dorfer, Markus Schedl, Gerhard Widmer:
A hybrid approach to music playlist continuation based on playlist-song membership. SAC 2018: 1374-1382 - [c134]Eva Zangerle, Martin Pichl, Markus Schedl:
Culture-Aware Music Recommendation. UMAP 2018: 357-358 - [c133]Markus Schedl, Eelco Wiechert, Christine Bauer:
The Effects of Real-world Events on Music Listening Behavior: An Intervention Time Series Analysis. WWW (Companion Volume) 2018: 75-76 - [i5]Andreu Vall, Matthias Dorfer, Markus Schedl, Gerhard Widmer:
A Hybrid Approach to Music Playlist Continuation Based on Playlist-Song Membership. CoRR abs/1805.09557 (2018) - [i4]Andreu Vall, Massimo Quadrana, Markus Schedl, Gerhard Widmer:
The Importance of Song Context and Song Order in Automated Music Playlist Generation. CoRR abs/1807.04690 (2018) - [i3]Hamed Zamani, Markus Schedl, Paul Lamere, Ching-Wei Chen:
An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation. CoRR abs/1810.01520 (2018) - 2017
- [j18]Markus Schedl:
Investigating country-specific music preferences and music recommendation algorithms with the LFM-1b dataset. Int. J. Multim. Inf. Retr. 6(1): 71-84 (2017) - [j17]Markus Schedl, Yi-Hsuan Yang, Perfecto Herrera-Boyer:
Introduction to Intelligent Music Systems and Applications. ACM Trans. Intell. Syst. Technol. 8(2): 17:1-17:8 (2017) - [c132]Thomas Krismayer, Markus Schedl, Peter Knees, Rick Rabiser:
Prediction of User Demographics from Music Listening Habits. CBMI 2017: 8:1-8:7 - [c131]Yashar Deldjoo, Paolo Cremonesi, Markus Schedl, Massimo Quadrana:
The effect of different video summarization models on the quality of video recommendation based on low-level visual features. CBMI 2017: 20:1-20:6 - [c130]Haukur Pálmason, Björn Þór Jónsson, Markus Schedl, Peter Knees:
Music Genre Classification Revisited: An In-Depth Examination Guided by Music Experts. CMMR 2017: 49-62 - [c129]Marcin Skowron, Florian Lemmerich, Bruce Ferwerda, Markus Schedl:
Predicting Genre Preferences from Cultural and Socio-Economic Factors for Music Retrieval. ECIR 2017: 561-567 - [c128]Martin Pichl, Eva Zangerle, Günther Specht, Markus Schedl:
Mining Culture-Specific Music Listening Behavior from Social Media Data. ISM 2017: 208-215 - [c127]Markus Schedl, Florian Lemmerich, Bruce Ferwerda, Marcin Skowron, Peter Knees:
Indicators of Country Similarity in Terms of Music Taste, Cultural, and Socio-economic Factors. ISM 2017: 308-311 - [c126]Markus Schedl, Bruce Ferwerda:
Large-Scale Analysis of Group-Specific Music Genre Taste from Collaborative Tags. ISM 2017: 479-482 - [c125]Meijun Liu, Xiao Hu, Markus Schedl:
Artist Preferences and Cultural, Socio-Economic Distances Across Countries: A Big Data Perspective. ISMIR 2017: 103-111 - [c124]Markus Schedl:
Intelligent User Interfaces for Social Music Discovery and Exploration of Large-scale Music Repositories. HUMANIZE@IUI 2017: 7-11 - [c123]Mark P. Graus, Bruce Ferwerda, Markus Schedl, Marko Tkalcic, Martijn C. Willemsen, Panagiotis Germanakos:
IUI'17 Companion-Workshop Summary for HUMANIZE'17. IUI Companion 2017: 13-15 - [c122]Khaled Koutini, Alina Imenina, Matthias Dorfer, Alexander Gruber, Markus Schedl:
MediaEval 2017 AcousticBrainz Genre Task: Multilayer Perceptron Approach. MediaEval 2017 - [c121]Markus Schedl, Christine Bauer:
Introducing Global and Regional Mainstreaminess for Improving Personalized Music Recommendation. MoMM 2017: 74-81 - [c120]Bruce Ferwerda, Marko Tkalcic, Markus Schedl:
Personality Traits and Music Genre Preferences: How Music Taste Varies Over Age Groups. RecTemp@RecSys 2017: 16-20 - [c119]Andreu Vall, Hamid Eghbal-zadeh, Matthias Dorfer, Markus Schedl, Gerhard Widmer:
Music Playlist Continuation by Learning from Hand-Curated Examples and Song Features: Alleviating the Cold-Start Problem for Rare and Out-of-Set Songs. DLRS@RecSys 2017: 46-54 - [c118]Markus Schedl, Peter Knees, Fabien Gouyon:
New Paths in Music Recommender Systems Research. RecSys 2017: 392-393 - [c117]Andreu Vall, Massimo Quadrana, Markus Schedl, Gerhard Widmer, Paolo Cremonesi:
The Importance of Song Context in Music Playlists. RecSys Posters 2017 - [c116]Bruce Ferwerda, Mark P. Graus, Andreu Vall, Marko Tkalcic, Markus Schedl:
How item discovery enabled by diversity leads to increased recommendation list attractiveness. SAC 2017: 1693-1696 - [c115]Haukur Pálmason, Björn Þór Jónsson, Laurent Amsaleg, Markus Schedl, Peter Knees:
On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique. SISAP 2017: 275-283 - [c114]Bruce Ferwerda, Marko Tkalcic, Markus Schedl:
Personality Traits and Music Genres: What Do People Prefer to Listen To? UMAP 2017: 285-288 - [c113]Christine Bauer, Markus Schedl:
Introducing Surprise and Opposition by Design in Recommender Systems. UMAP (Adjunct Publication) 2017: 350-353 - [c112]