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2020 – today
- 2023
[j42]Alessandro B. Melchiorre
, David Penz, Christian Ganhör, Oleg Lesota, Vasco Fragoso, Florian Fritzl, Emilia Parada-Cabaleiro, Franz Schubert, Markus Schedl:
Emotion-aware music tower blocks (EmoMTB ): an intelligent audiovisual interface for music discovery and recommendation. Int. J. Multim. Inf. Retr. 12(1): 13 (2023)- [j41]Tommaso Di Noia, In-Young Ko, Markus Schedl:
Introduction to the ICWE 2022 Special Issue. J. Web Eng. 22(1): v-viii (2023) - [c202]Lukas Hauzenberger, Shahed Masoudian, Deepak Kumar, Markus Schedl, Navid Rekabsaz:
Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks. ACL (Findings) 2023: 6192-6214 - [c201]Dominik Kowald, Gregor Mayr, Markus Schedl, Elisabeth Lex:
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations. BIAS 2023: 1-16 - [c200]Simone Kopeinik, Martina Mara, Linda Ratz, Klara Krieg, Markus Schedl, Navid Rekabsaz:
Show me a "Male Nurse"! How Gender Bias is Reflected in the Query Formulation of Search Engine Users. CHI 2023: 137:1-137:15 - [c199]Bruce Ferwerda, Eveline Ingesson, Michaela Berndl, Markus Schedl:
I Don't Care How Popular You Are! Investigating Popularity Bias in Music Recommendations from a User's Perspective. CHIIR 2023: 357-361 - [c198]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. CHIIR 2023: 444-448 - [c197]Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz:
Parameter-efficient Modularised Bias Mitigation via AdapterFusion. EACL 2023: 2730-2743 - [c196]Marta Moscati, Yashar Deldjoo, Giulio Davide Carparelli, Markus Schedl:
Multiobjective Hyperparameter Optimization of Recommender Systems. Perspectives@RecSys 2023 - [c195]Marta Moscati, Christian Wallmann, Markus Reiter-Haas, Dominik Kowald, Elisabeth Lex, Markus Schedl:
Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation. RecSys 2023: 840-847 - [c194]Markus Schedl, Vito Walter Anelli, Elisabeth Lex:
Trustworthy Recommender Systems: Technical, Ethical, Legal, and Regulatory Perspectives. RecSys 2023: 1288-1290 - [c193]Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, Bruce Ferwerda, Simone Kopeinik, Elisabeth Lex, Navid Rekabsaz, Markus Schedl:
Computational Versus Perceived Popularity Miscalibration in Recommender Systems. SIGIR 2023: 1889-1893 - [c192]Veronika Arefieva, Roman Egger, Michael Schrefl, Markus Schedl:
Travel Bird: A Personalized Destination Recommender with TourBERT and Airbnb Experiences. WSDM 2023: 1164-1167 - [c191]Markus Schedl, Emilia Gómez, Elisabeth Lex:
Trustworthy Algorithmic Ranking Systems. WSDM 2023: 1240-1243 - [i30]Deepak Kumar, Oleg Lesota, George Zerveas, Daniel Cohen, Carsten Eickhoff, Markus Schedl, Navid Rekabsaz:
Parameter-efficient Modularised Bias Mitigation via AdapterFusion. CoRR abs/2302.06321 (2023) - [i29]Dominik Kowald, Gregor Mayr, Markus Schedl, Elisabeth Lex:
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations. CoRR abs/2303.00400 (2023) - [i28]Shahed Masoudian, Khaled Koutini, Markus Schedl, Gerhard Widmer, Navid Rekabsaz:
Domain Information Control at Inference Time for Acoustic Scene Classification. CoRR abs/2306.08010 (2023) - 2022
- [j40]Darius Afchar, Alessandro B. Melchiorre, Markus Schedl, Romain Hennequin, Elena V. Epure, Manuel Moussallam:
Explainability in Music Recommender Systems. AI Mag. 43(2): 190-208 (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) - [c190]Klara Krieg, Emilia Parada-Cabaleiro, Markus Schedl, Navid Rekabsaz:
Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements? BIAS 2022: 104-116 - [c189]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 - [c188]Elisabeth Lex, Markus Schedl:
Psychology-informed Recommender Systems: A Human-Centric Perspective on Recommender Systems. CHIIR 2022: 367-368 - [c187]Marta Moscati, Emilia Parada-Cabaleiro, Yashar Deldjoo, Eva Zangerle, Markus Schedl:
Music4All-Onion - A Large-Scale Multi-faceted Content-Centric Music Recommendation Dataset. CIKM 2022: 4339-4343 - [c186]Oleg Lesota, Emilia Parada-Cabaleiro, Stefan Brandl, Elisabeth Lex, Navid Rekabsaz, Markus Schedl:
Traces of Globalization in Online Music Consumption Patterns and Results of Recommendation Algorithms. ISMIR 2022: 291-297 - [c185]Maximilian Mayerl, Stefan Brandl, Günther Specht, Markus Schedl, Eva Zangerle:
Verse versus Chorus: Structure-aware Feature Extraction for Lyrics-based Genre Recognition. ISMIR 2022: 884-890 - [c184]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 - [c183]Oleg Lesota, Stefan Brandl, Matthias Wenzel, Alessandro B. Melchiorre, Elisabeth Lex, Navid Rekabsaz, Markus Schedl:
Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization. MORS@RecSys 2022 - [c182]Alessandro B. Melchiorre, Navid Rekabsaz, Christian Ganhör, Markus Schedl:
ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations. RecSys 2022: 246-256 - [c181]Elisabeth Lex, Markus Schedl:
Psychology-informed Recommender Systems Tutorial. RecSys 2022: 714-717 - [c180]Christian Ganhör, David Penz, Navid Rekabsaz, Oleg Lesota, Markus Schedl:
Unlearning Protected User Attributes in Recommendations with Adversarial Training. SIGIR 2022: 2142-2147 - [c179]Markus Schedl, Emilia Gómez, Elisabeth Lex:
Retrieval and Recommendation Systems at the Crossroads of Artificial Intelligence, Ethics, and Regulation. SIGIR 2022: 3420-3424 - [c178]Markus Schedl, Navid Rekabsaz, Elisabeth Lex, Tessa Grosz, Elisabeth Greif:
Multiperspective and Multidisciplinary Treatment of Fairness in Recommender Systems Research. UMAP (Adjunct Publication) 2022: 90-94 - [c177]Riccardo Tommasini, Senjuti Basu Roy, Xuan Wang, Hongwei Wang, Heng Ji, Jiawei Han, Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Markus Schedl, Elisabeth Lex, Akash Bharadwaj, Graham Cormode, Milan Dojchinovski, Jan Forberg, Johannes Frey, Pieter Bonte, Marco Balduini, Matteo Belcao, Emanuele Della Valle, Junliang Yu, Hongzhi Yin, Tong Chen, Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Jamell Dacon, Lingjuan Lye, Jiliang Tang, Aristides Gionis, Stefan Neumann, Bruno Ordozgoiti, Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian M. Suchanek, Lingfei Wu, Yu Chen, Yunyao Li, Bang Liu, Filip Ilievski, Daniel Garijo, Hans Chalupsky, Pedro A. Szekely, Ilias Kanellos, Dimitris Sacharidis, Thanasis Vergoulis, Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan H. Sengamedu, Chandan K. Reddy, Friedhelm Victor, Bernhard Haslhofer, George Katsogiannis-Meimarakis, Georgia Koutrika, Shengmin Jin, Danai Koutra, Reza Zafarani, Yulia Tsvetkov, Vidhisha Balachandran, Sachin Kumar, Xiangyu Zhao, Bo Chen, Huifeng Guo, Yejing Wang, Ruiming Tang, Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, Xiangnan He:
Accepted Tutorials at The Web Conference 2022. WWW (Companion Volume) 2022: 391-399 - [e9]Tommaso Di Noia, In-Young Ko, Markus Schedl, Carmelo Ardito:
Web Engineering - 22nd International Conference, ICWE 2022, Bari, Italy, July 5-8, 2022, Proceedings. Lecture Notes in Computer Science 13362, Springer 2022, ISBN 978-3-031-09916-8 [contents] - [r3]Markus Schedl, Peter Knees, Brian McFee, Dmitry Bogdanov:
Music Recommendation Systems: Techniques, Use Cases, and Challenges. Recommender Systems Handbook 2022: 927-971 - [r2]Yashar Deldjoo, Markus Schedl, Balázs Hidasi, Yinwei Wei, Xiangnan He:
Multimedia Recommender Systems: Algorithms and Challenges. Recommender Systems Handbook 2022: 973-1014 - [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]Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, Gabriella Pasi:
Recommender Systems Leveraging Multimedia Content. ACM Comput. Surv. 53(5): 106:1-106:38 (2021) - [j36]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) - [j35]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) - [j34]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
- [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