Recommender Systems and User Modeling

@inproceedings{DBLP:conf/recsys/PeintnerMZ23,
  author       = {Andreas Peintner and
                  Amir Reza Mohammadi and
                  Eva Zangerle},
  title        = {{SPARE:} Shortest Path Global Item Relations for Efficient Session-based
                  Recommendation},
  booktitle    = {Proceedings of the 17th {ACM} Conference on Recommender Systems, RecSys
                  2023, Singapore, Singapore, September 18-22, 2023},
  pages        = {58--69},
  publisher    = {{ACM}},
  year         = {2023},
  url          = {https://doi.org/10.1145/3604915.3608768},
  doi          = {10.1145/3604915.3608768}
}

@proceedings{DBLP:conf/recsys/2023perspectives,
  editor       = {Alan Said and
                  Eva Zangerle and
                  Christine Bauer},
  title        = {Proceedings of the 3rd Workshop Perspectives on the Evaluation of Recommender Systems 2023 co-located with the 17th {ACM} Conference on Recommender Systems (RecSys 2023), Singapore, Singapore, September
                  19, 2023},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3476},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  url          = {https://ceur-ws.org/Vol-3476},
  urn          = {urn:nbn:de:0074-3476-4},
}

@inproceedings{DBLP:conf/recsys/SaidZ023,
  author       = {Alan Said and
                  Eva Zangerle and
                  Christine Bauer},
  title        = {Third Workshop: Perspectives on the Evaluation of Recommender Systems
                  {(PERSPECTIVES} 2023)},
  booktitle    = {Proceedings of the 17th {ACM} Conference on Recommender Systems, RecSys
                  2023, Singapore, Singapore, September 18-22, 2023},
  pages        = {1221--1222},
  publisher    = {{ACM}},
  year         = {2023},
  url          = {https://doi.org/10.1145/3604915.3608748},
  doi          = {10.1145/3604915.3608748},
}

@article{10.1145/3556536,
author = {Zangerle, Eva and Bauer, Christine},
title = {Evaluating Recommender Systems: Survey and Framework},
year = {2023},
month = {August},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {0360-0300},
url = {https://doi.org/10.1145/3556536},
doi = {10.1145/3556536},
abstract = {The comprehensive evaluation of the performance of a recommender system is a complex endeavor: many facets need to be considered in configuring an adequate and effective evaluation setting. Such facets include, for instance, defining the specific goals of the evaluation, choosing an evaluation method, underlying data, and suitable evaluation metrics. In this paper, we consolidate and systematically organize this dispersed knowledge on recommender systems evaluation. We introduce the “Framework for EValuating Recommender systems” (FEVR) that we derive from the discourse on recommender systems evaluation. In FEVR, we categorize the evaluation space of recommender systems evaluation. We postulate that the comprehensive evaluation of a recommender system frequently requires considering multiple facets and perspectives in the evaluation. The FEVR framework provides a structured foundation to adopt adequate evaluation configurations that encompass this required multi-facettedness and provides the basis to advance in the field. We outline and discuss the challenges of a comprehensive evaluation of recommender systems, and provide an outlook on what we need to embrace and do to move forward as a research community.},
journal = {ACM Computing Surveys},
volume       = 55,
number       = 8,
articleno = {170},
numpages = {38},
keywords = {Framework for EValuating Recommender systems, survey, FEVR}
}

@article{10.1145/3582900.3582919,
author = {Zangerle, Eva and Bauer, Christine and Said, Alan},
title = {Report on the 2nd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022) at RecSys 2022},
year = {2023},
issue_date = {December 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {56},
number = {2},
issn = {0163-5840},
url = {https://doi.org/10.1145/3582900.3582919},
doi = {10.1145/3582900.3582919},
journal = {SIGIR Forum},
month = {jan},
articleno = {15},
numpages = {4}
}

@phdthesis{zangerle:habil:2022,
 author = {Eva Zangerle},
 school = {University of Innsbruck},
 title = {Recommender Systems for Music Retrieval Tasks},
 type = {Habilitation},
 year = {2022}
}

@inproceedings{mayerl:ismir:2022,
   title        = {Verse versus Chorus: Structure-aware Feature Extraction for Lyrics-based Genre Recognition},
   author       = {Mayerl, Maximilian and Brandl, Stefan and Specht, G{\"u}nther and Schedl, Markus and Zangerle, Eva},
   year         = 2022,
   booktitle    = {Proceedings of the 23rd International Society for Music Information Retrieval Conference 2022},
   publisher    = {ISMIR},
   series       = {ISMIR '22},
   pages        = {884--890},
   doi          = {10.5281/zenodo.7316806}
 }

@inproceedings{10.1145/3511808.3557656,
author = {Moscati, Marta and Parada-Cabaleiro, Emilia and Deldjoo, Yashar and Zangerle, Eva and Schedl, Markus},
title = {Music4All-Onion -- A Large-Scale Multi-Faceted Content-Centric Music Recommendation Dataset},
year = {2022},
isbn = {9781450392365},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3511808.3557656},
doi = {10.1145/3511808.3557656},
abstract = {When 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.},
booktitle = {Proceedings of the 31st ACM International Conference on Information & Knowledge Management},
pages = {4339–4343},
numpages = {5},
keywords = {music information retrieval, image, video, audio features, lyrics, music recommender systems, audio signal, multimedia content analysis, onion model, natural language processing},
location = {Atlanta, GA, USA},
series = {CIKM '22}
}

@proceedings{DBLP:conf/recsys/2022perspectives,
  editor    = {Eva Zangerle and
               Christine Bauer and
               Alan Said},
  title     = {Proceedings of the Perspectives on the Evaluation of Recommender Systems
               Workshop 2022 co-located with the 16th {ACM} Conference on Recommender
               Systems (RecSys 2022)},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {3228},
  publisher = {CEUR-WS.org},
  year      = {2022},
  url       = {http://ceur-ws.org/Vol-3228},
}

@inproceedings{zangerle-recsys-2022,
author = {Zangerle, Eva and Bauer, Christine and Said, Alan},
title = {Second Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022)},
year = {2022},
isbn = {9781450392785},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3523227.3547408},
doi = {10.1145/3523227.3547408},
booktitle = {Proceedings of the 16th ACM Conference on Recommender Systems},
pages = {652–653},
numpages = {2},
keywords = {evaluation, recommender systems, personalized systems, methods, information retrieval},
location = {Seattle, WA, USA},
series = {RecSys '22}
}

@article{zangerle:sigirforum:2021,
author = {Zangerle, Eva and Bauer, Christine and Said, Alan},
title = {Report on the 1st Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2021) at RecSys 2021},
year = {2022},
issue_date = {December 2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {55},
number = {2},
issn = {0163-5840},
url = {https://doi.org/10.1145/3527546.3527565},
doi = {10.1145/3527546.3527565},
abstract = {Evaluation is a central step when it comes to developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2021 workshop at the 15th ACM Conference on Recommender Systems brought together academia and industry to critically reflect on the evaluation of recommender systems. The primary goal of the workshop was to capture the current state of evaluation from different, and maybe even diverging or contradictory perspectives.Date: 25 and 30 September 2021.Website: https://perspectives-ws.github.io/2021/.},
journal = {SIGIR Forum},
month = {mar},
articleno = {16},
numpages = {5}
}

@proceedings{zangerle:perspectives:2021,
  editor    = {Eva Zangerle and 
               Christine Bauer and 
              Alan Said},
  title     = {Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2021
co-located with the 15th ACM Conference on Recommender Systems (RecSys 2021)},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2955},
  publisher = {CEUR-WS.org},
  year      = {2021},
  url       = {http://ceur-ws.org/Vol-2955},
  urn       = {urn:nbn:de:0074-2955-1},
}

@inproceedings{10.1145/3460231.3470929,
author = {Zangerle, Eva and Bauer, Christine and Said, Alan},
title = {Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES)},
year = {2021},
isbn = {9781450384582},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3460231.3470929},
doi = {10.1145/3460231.3470929},
booktitle = {Fifteenth ACM Conference on Recommender Systems},
pages = {794–795},
numpages = {2},
keywords = {recommender systems, evaluation, methods},
location = {Amsterdam, Netherlands},
series = {RecSys '21}
}

@article{pichl2020user,
  title={User models for multi-context-aware music recommendation},
  author={Pichl, Martin and Zangerle, Eva},
  journal={Multimedia Tools and Applications},
  pages={22509--22531},
  year={2021},
  publisher={Springer},
  doi={10.1007/s11042-020-09890-7},
  volume=80,
  number={15}
}

@ARTICLE{8382228,
  author={Eva {Zangerle} and Chih-Ming {Chen} and Ming-Feng {Tsai} and Yi-Hsuan {Yang}},
  journal={IEEE Transactions on Affective Computing}, 
  title={Leveraging Affective Hashtags for Ranking Music Recommendations}, 
  year={2021},
  volume={12},
  number={1},
  pages={78-91},
  doi={10.1109/TAFFC.2018.2846596}}

@article{kowald2021support,
  title={Support the underground: characteristics of beyond-mainstream music listeners},
  author={Kowald, Dominik and Muellner, Peter and Zangerle, Eva and Bauer, Christine and Schedl, Markus and Lex, Elisabeth},
  journal={EPJ Data Science},
  volume={10},
  number={1},
  pages={1--26},
  year={2021},
  publisher={Springer}
}

@article{zangerle2020user,
  title={User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues},
  author={Zangerle, Eva and Pichl, Martin and Schedl, Markus},
  journal={Transactions of the International Society for Music Information Retrieval},
  volume={3},
  number={1},
  year={2020},
  publisher={Ubiquity Press},
  doi={10.5334/tismir.37}
}

@inproceedings{10.1145/3383313.3412223,
 address = {New York, NY, USA},
 author = {Melchiorre, Alessandro B. and Zangerle, Eva and Schedl, Markus},
 booktitle = {14th ACM Conference on Recommender Systems (RecSys 2020)},
 doi = {10.1145/3383313.3412223},
 isbn = {9781450375832},
 keywords = {bias, dataset, music recommender systems, neural networks, personality},
 location = {Virtual Event, Brazil},
 numpages = {6},
 pages = {533–538},
 publisher = {ACM},
 series = {RecSys '20},
 title = {Personality Bias of Music Recommendation Algorithms},
 url = {https://doi.org/10.1145/3383313.3412223},
 year = {2020}
}

@inproceedings{bauer2019leveraging,
  title={Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings},
  author={Bauer, Christine and Zangerle, Eva},
  year={2019},
  booktitle = {{Proceedings of the 1st Workshop on the Impact of Recommender Systems
co-located with 13th ACM Conference on Recommender Systems (ACM RecSys 2019)}},
  publisher = {ceur-ws.org},
}

@inproceedings{gvdb1019_1,
  author    = {Maximilian Mayerl and
               Michael V{\"{o}}tter and
               Eva Zangerle and
               G{\"{u}}nther Specht},
  title     = {Language Models for Next-Track Music Recommendation},
  booktitle = {Proceedings of the 31st GI-Workshop Grundlagen von Datenbanken, Saarburg,
               Germany, June 11-14, 2019.},
  pages     = {15--19},
  year      = {2019},
  url       = {http://ceur-ws.org/Vol-2367/paper\_1.pdf},
}