Publications

@article{10.1145/3629170,
author = {Bauer, Christine and Zangerle, Eva and Said, Alan},
title = {Exploring the Landscape of Recommender Systems Evaluation: Practices and Perspectives},
year = {2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3629170},
doi = {10.1145/3629170},
abstract = {Recommender systems research and practice are fast-developing topics with growing adoption in a wide variety of information access scenarios. In this paper, we present an overview of research specifically focused on the emphevaluation of recommender systems. We perform a systematic literature review, in which we analyze 57 papers spanning six years (2017–2022). Focusing on the processes surrounding evaluation, we dial in on the methods applied, the datasets utilized, and the metrics used. Our study shows that the predominant experiment type in research on the evaluation of recommender systems is offline experimentation and that online evaluations are primarily used in combination with other experimentation methods, e.g., an offline experiment. Furthermore, we find that only a few datasets (MovieLens, Amazon review dataset) are widely used, while many datasets are used in only a few papers each. We observe a similar scenario when analyzing the employed performance metrics—a few metrics are widely used (precision, nDCG, and Recall), while many others are used in only a few papers. Overall, our review indicates that beyond-accuracy qualities are rarely assessed. Our analysis shows that the research community working on evaluation has focused on the development of evaluation in a rather narrow scope, with the majority of experiments focusing on a few metrics, datasets, and methods.},
note = {Just Accepted},
journal = {ACM Trans. Recomm. Syst.},
month = {oct},
keywords = {survey, systematic literature review, recommender systems, evaluation}
}

@inproceedings{DBLP:conf/clef/ZangerleMP023,
  author       = {Eva Zangerle and
                  Maximilian Mayerl and
                  Martin Potthast and
                  Benno Stein},
  editor       = {Mohammad Aliannejadi and
                  Guglielmo Faggioli and
                  Nicola Ferro and
                  Michalis Vlachos},
  title        = {Overview of the Multi-Author Writing Style Analysis Task at {PAN}
                  2023},
  booktitle    = {Working Notes of the Conference and Labs of the Evaluation Forum {(CLEF}
                  2023), Thessaloniki, Greece, September 18th to 21st, 2023},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3497},
  pages        = {2513--2522},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  url          = {https://ceur-ws.org/Vol-3497/paper-201.pdf},
  timestamp    = {Wed, 25 Oct 2023 17:24:28 +0200},
  biburl       = {https://dblp.org/rec/conf/clef/ZangerleMP023.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@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}
}

@inproceedings{bevendorff:clef:2023,
author="Bevendorff, Janek
and Borrego-Obrador, Ian
and Chinea-R{\'i}os, Mara
and Franco-Salvador, Marc
and Fr{\"o}be, Maik
and Heini, Annina
and Kredens, Krzysztof
and Mayerl, Maximilian
and P{\k{e}}zik, Piotr
and Potthast, Martin
and Rangel, Francisco
and Rosso, Paolo
and Stamatatos, Efstathios
and Stein, Benno
and Wiegmann, Matti
and Wolska, Magdalena
and Zangerle, Eva",
title="Overview of PAN 2023: Authorship Verification, Multi-Author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection",
booktitle="Experimental IR Meets Multilinguality, Multimodality, and Interaction",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="459--481",
abstract="The paper gives a brief overview of three shared tasks which have been organized at the PAN 2023 lab on digital text forensics and stylometry hosted at the CLEF 2023 conference. The tasks include authorship verification across discourse types, multi-author writing style analysis, profiling cryptocurrency influencers with few-shot learning, and trigger detection. Authorship verification and multi-author analysis continue and advance from past editions of PAN and influencer profiling and trigger detection are new tasks with novel research questions and evaluation resources. All four tasks alilgn with the goals of all shared tasks at PAN: to advance the state of the art in text forensics and stylometry while ensuring objective evaluation on newly developed benchmark datasets.",
isbn="978-3-031-42448-9",
doi="10.1007/978-3-031-42448-9_29"
}

@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}
}

@inproceedings{voetter@songpopordinal,
        author =       {V\"{o}tter, Michael and Mayerl, Maximilian and Zangerle, Eva and Specht, G\"{u}nther},
	title        = {Song Popularity Prediction using Ordinal Classification},
	year         = 2023,
	booktitle    = {Proceedings of the 20th Sound and Music Computing Conference. June 15-17, 2023. Stockholm, Sweden},
	publisher    = {Royal College of Music and KTH Royal Institute of Technology},
	editor       = {Roberto Bresin and Kjetil Falkenberg},
	organization = {Royal College of Music and KTH Royal Institute of Technology}
}

@inproceedings{mci/Moosleitner2023,
author = {Moosleitner, Manfred and Specht, Günther and Zangerle, Eva},
title = {Detection of Generated Text Reviews by Leveraging Methods from Authorship Attribution: Predictive Performance vs. Resourcefulness},
booktitle = {BTW 2023},
year = {2023},
editor = {König-Ries, Birgitta AND Scherzinger, Stefanie AND Lehner, Wolfgang AND Vossen, Gottfried} ,
doi = { 10.18420/BTW2023-11 },
publisher = {Gesellschaft für Informatik e.V.},
address = {}
}

@inproceedings{mci/Mayerl2023,
author = {Mayerl, Maximilian AND Vötter, Michael AND Specht, Günther AND Zangerle, Eva},
title = {Pairwise Learning to Rank for Hit Song Prediction},
booktitle = {BTW 2023},
year = {2023},
editor = {König-Ries, Birgitta AND Scherzinger, Stefanie AND Lehner, Wolfgang AND Vossen, Gottfried} ,
doi = { 10.18420/BTW2023-26 },
publisher = {Gesellschaft für Informatik e.V.},
address = {}
}

@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}
}

@InProceedings{10.1007/978-3-031-28241-6_60,
author="Bevendorff, Janek
and Chinea-Rios, Mara
and Franco-Salvador, Marc
and Heini, Annina
and K{\"o}rner, Erik
and Kredens, Krzysztof
and Mayerl, Maximilian
and Pkezik, Piotr
and Potthast, Martin
and Rangel, Francisco
and Rosso, Paolo
and Stamatatos, Efstathios
and Stein, Benno
and Wiegmann, Matti
and Wolska, Magdalena
and Zangerle, Eva",
title="Overview of PAN 2023: Authorship Verification, Multi-author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection",
booktitle="Advances in Information Retrieval (ECIR 2023)",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="518--526",
isbn="978-3-031-28241-6"
}

@InProceedings{10.1007/978-3-031-23793-5_26,
author="Murauer, Benjamin
and Tschuggnall, Michael
and Specht, G{\"u}nther",
editor="Gelbukh, Alexander",
title="On the Influence of Machine Translation on Language Origin Obfuscation",
booktitle="Computational Linguistics and Intelligent Text Processing",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="320--330",
abstract="In the last decade, machine translation has become a popular means to deal with multilingual digital content. By providing higher quality translations, obfuscating the source language of a text becomes more attractive. In this paper, we analyze the ability to detect the source language from the translated output of two widely used commercial machine translation systems by utilizing machine-learning algorithms with basic textual features like n-grams. Evaluations show that the source language can be reconstructed with high accuracy for documents that contain a sufficient amount of translated text. In addition, we analyze how the document size influences the performance of the prediction, as well as how limiting the set of possible source languages improves the classification accuracy.",
isbn="978-3-031-23793-5"
}

@InProceedings{10.1007/978-3-031-23804-8_23,
author="Murauer, Benjamin
and Tschuggnall, Michael
and Specht, G{\"u}nther
and Brandl, Julia",
editor="Gelbukh, Alexander",
title="Algorithmic Segmentation of Job Ads Using Textual Analysis",
booktitle="Computational Linguistics and Intelligent Text Processing",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="287--300",
abstract="As job ads are getting more prevalent online, an automated analysis is becoming increasingly important, especially in the field of human resource management. In this paper, we propose an approach to automatically segment job ads by predefined categories like the description of a job or the offering company, which is needed to categorize and quantify different aspects of job ads. Using a manually annotated data set, textual features are extracted for each segment type in a first step and utilized to train state-of-the-art machine learning classification methods. Subsequently, these models are used by iterative algorithms to detect the individual segments. Using several optimization techniques like detecting typical segment start phrases, comprehensive evaluations show promising results.",
isbn="978-3-031-23804-8"
}

@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}
 }

@phdthesis{murauer2022dissertation,
  title={{Universal Grammar Features for Cross-Language Authorship Attribution}},
  school = {University of Innsbruck, Department of Computer Science},
  author={Benjamin Murauer},
  year={2022},
}

@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{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}
}

@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}
}

@inproceedings{peintner:cars:2022,
 author = {Peintner, Andreas and Moscati, Marta and Parada-Cabaleiro, Emilia and Schedl, Markus and Zangerle, Eva},
 booktitle = {CARS: Workshop on Context-Aware Recommender Systems (RecSys ’22)},
 title = {Unsupervised Graph Embeddings for Session-based Recommendation with Item Features},
 year = {2022}
}

@inproceedings{DBLP:conf/clef/BevendorffCFHKK22,
  author    = {Janek Bevendorff and
               Berta Chulvi and
               Elisabetta Fersini and
               Annina Heini and
               Mike Kestemont and
               Krzysztof Kredens and
               Maximilian Mayerl and
               Reynier Ortega{-}Bueno and
               Piotr Pezik and
               Martin Potthast and
               Francisco Rangel and
               Paolo Rosso and
               Efstathios Stamatatos and
               Benno Stein and
               Matti Wiegmann and
               Magdalena Wolska and
               Eva Zangerle},
  title     = {Overview of {PAN} 2022: Authorship Verification, Profiling Irony and
               Stereotype Spreaders, and Style Change Detection},
  booktitle = {Experimental {IR} Meets Multilinguality, Multimodality, and Interaction
               - 13th International Conference of the {CLEF} Association, {CLEF}
               2022, Bologna, Italy, September 5-8, 2022, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {13390},
  pages     = {382--394},
  publisher = {Springer},
  year      = {2022},
  url       = {https://doi.org/10.1007/978-3-031-13643-6\_24},
  doi       = {10.1007/978-3-031-13643-6\_24},
}

@inproceedings{DBLP:conf/clef/ZangerleMP022,
  author    = {Eva Zangerle and
               Maximilian Mayerl and
               Martin Potthast and
               Benno Stein},
  editor    = {Guglielmo Faggioli and
               Nicola Ferro and
               Allan Hanbury and
               Martin Potthast},
  title     = {Overview of the Style Change Detection Task at {PAN} 2022},
  booktitle = {Working Notes of {CLEF} 2022 - Conference and Labs
               of the Evaluation Forum},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {3180},
  pages     = {2344--2356},
  publisher = {CEUR-WS.org},
  year      = {2022},
  url       = {http://ceur-ws.org/Vol-3180/paper-186.pdf},
  timestamp = {Wed, 10 Aug 2022 16:26:45 +0200},
  biburl    = {https://dblp.org/rec/conf/clef/ZangerleMP022.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{voetter_ijsc_2022,
	title = {{HSP} {Datasets}: {Insights} on {Song} {Popularity} {Prediction}},
	issn = {1793-351X},
	url = {https://www.worldscientific.com/doi/abs/10.1142/S1793351X22400104},
	doi = {10.1142/S1793351X22400104},
	journal = {International Journal of Semantic Computing},
	author = {Vötter, Michael and Mayerl, Maximilian and Specht, Günther and Zangerle, Eva},
	month = may,
	year = {2022},
	note = {Publisher: World Scientific Publishing Co.},
	pages = {1--23},
}

@article{10.1145/3506692,
author = {Binna, Robert and Zangerle, Eva and Pichl, Martin and Specht, G\"{u}nther and Leis, Viktor},
title = {Height Optimized Tries},
year = {2022},
issue_date = {March 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {47},
number = {1},
issn = {0362-5915},
url = {https://doi.org/10.1145/3506692},
doi = {10.1145/3506692},
abstract = {We present the Height Optimized Trie (HOT), a fast and space-efficient in-memory index structure. The core algorithmic idea of HOT is to dynamically vary the number of bits considered at each node, which enables a consistently high fanout and thereby good cache efficiency. For a fixed maximum node fanout, the overall tree height is minimal and its structure is deterministically defined. Multiple carefully engineered node implementations using SIMD instructions or lightweight compression schemes provide compactness and fast search and optimize HOT structures for different usage scenarios. Our experiments, which use a wide variety of workloads and data sets, show that HOT outperforms other state-of-the-art index structures for string keys both in terms of search performance and memory footprint, while being competitive for integer keys.},
journal = {ACM Trans. Database Syst.},
month = {apr},
articleno = {3},
numpages = {46},
keywords = {main memory, SIMD, Height optimized trie, index structure}
}