Recommender Systems and User Modeling

@Article{bauer:dagstuhl:2024,
  author =	{Bauer, Christine and Said, Alan and Zangerle, Eva},
  title =	{{Evaluation Perspectives of Recommender Systems: Driving Research and Education (Dagstuhl Seminar 24211)}},
  pages =	{58--172},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{14},
  number =	{5},
  editor =	{Bauer, Christine and Said, Alan and Zangerle, Eva},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.5.58},
  URN =		{urn:nbn:de:0030-drops-222655},
  doi =		{10.4230/DagRep.14.5.58},
  annote =	{Keywords: Recommender Systems, Evaluation, Information Retrieval, User Interaction, Intelligent Systems}
}

@article{10.1145/3701764,
author = {Peintner, Andreas and Mohammadi, Amir Reza and Zangerle, Eva},
title = {Efficient Session-based Recommendation with Contrastive Graph-based Shortest Path Search},
year = {2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3701764},
doi = {10.1145/3701764},
abstract = {Session-based recommendation aims to predict the next item based on a set of anonymous sessions. Capturing user intent from a short interaction sequence imposes a variety of challenges since no user profiles are available and interaction data is naturally sparse. Recent approaches relying on graph neural networks (GNNs) for session-based recommendation use global item relations to explore collaborative information from different sessions. These methods capture the topological structure of the graph and rely on multi-hop information aggregation in GNNs to exchange information along edges. Consequently, graph-based models suffer from noisy item relations in the training data and introduce high complexity for large item catalogs. We propose to explicitly model the multi-hop information aggregation mechanism over multiple layers via shortest-path edges based on knowledge from the sequential recommendation domain. Our approach does not require multiple layers to exchange information and ignores unreliable item-item relations. Furthermore, to address inherent data sparsity, we are the first to apply supervised contrastive learning by mining data-driven positive and hard negative item samples from the training data. Extensive experiments on four different datasets show that the proposed approach outperforms almost all of the state-of-the-art methods.},
note = {Just Accepted},
journal = {ACM Trans. Recomm. Syst.},
month = oct,
keywords = {Recommender Systems, Session-based Recommendation, Graph Neural Network, Supervised Contrastive Learning}
}

@inproceedings{10.1145/3640457.3687101,
author = {Said, Alan and Bauer, Christine and Zangerle, Eva},
title = {Reflections on Recommender Systems: Past, Present, and Future (INTROSPECTIVES)},
year = {2024},
isbn = {9798400705052},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3640457.3687101},
doi = {10.1145/3640457.3687101},
abstract = {With the RecSys conference now turning 18 years old, the recommender systems (RS) discipline ventures into adulthood. This workshop serves as a platform for introspection, examining the evolution of RS from its origins in CHI to its current state heavily influenced by and focusing on machine learning. The INTROSPECTIVES workshop aims to foster discussions on the past, present, and future of the RS discipline, inviting the community to reflect on key questions such as the maturation of RS, shifts in research focus, and the impact and success of RS in practice. Topics include the changing landscape of RS problems, the evolving role of RS in addressing choice overload to the current motivations driving RS adoption.},
booktitle = {Proceedings of the 18th ACM Conference on Recommender Systems},
pages = {1237–1238},
numpages = {2},
keywords = {information retrieval, introspection, personalized systems, recommender systems, reflection},
location = {Bari, Italy},
series = {RecSys '24}
}

@article{jacobsen:musiscaescientiae:2024,
	title        = {Assessing aesthetic music-evoked emotions in a minute or less: A comparison of the GEMS-45 and the GEMS-9},
	author       = {Peer-Ole Jacobsen and Hannah Strauss and Julia Vigl and Eva Zangerle and Marcel Zentner},
	year         = {0},
	journal      = {Musicae Scientiae},
	pages        = 10298649241256252,
	doi          = {10.1177/10298649241256252},
	eprint       = {https://doi.org/10.1177/10298649241256252}
}

@inproceedings{10.1145/3627043.3659540,
author = {Moscati, Marta and Strau\ss{}, Hannah and Jacobsen, Peer-Ole and Peintner, Andreas and Zangerle, Eva and Zentner, Marcel and Schedl, Markus},
title = {Emotion-Based Music Recommendation from Quality Annotations and Large-Scale User-Generated Tags},
year = {2024},
isbn = {9798400704338},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3627043.3659540},
doi = {10.1145/3627043.3659540},
abstract = {Emotions constitute an important aspect when listening to music. While manual annotations from user studies grounded in psychological research on music and emotions provide a well-defined and fine-grained description of the emotions evoked when listening to a music track, user-generated tags provide an alternative view stemming from large-scale data. In this work, we examine the relationship between these two emotional characterizations of music and analyze their impact on the performance of emotion-based music recommender systems individually and jointly. Our analysis shows that (i) the agreement between the two characterizations, as measured with Cohen’s κ coefficient and Kendall rank correlation, is often low, (ii) Leveraging the emotion profile based on the intensity of evoked emotions from high-quality annotations leads to performances that are stable across different recommendation algorithms; (iii) Simultaneously leveraging the emotion profiles based on high-quality and large-scale annotations allows to provide recommendations that are less exposed to the low accuracy that algorithms might reach when leveraging one type of data, only.},
booktitle = {Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {159–164},
numpages = {6},
keywords = {Annotation Study, Emotion-based Recommender Systems, Emotions, Music, Music Recommender Systems},
location = {Cagliari, Italy},
series = {UMAP '24}
}

@article{strauss:2024:behaviorresearchmethods,
	title        = {The Emotion-to-Music Mapping Atlas (EMMA): A systematically organized online database of emotionally evocative music excerpts},
	author       = {Strauss, Hannah and Vigl, Julia and Jacobsen, Peer-Ole and Bayer, Martin and Talamini, Francesca and Vigl, Wolfgang and Zangerle, Eva and Zentner, Marcel},
	year         = 2024,
	month        = {jun},
	journal      = {Behavior Research Methods},
	publisher    = {Springer},
	pages        = {3560--3577},
        issue_date   = {June 2024},
	volume	     = 56,
        number       = 4,
	doi          = {https://doi.org/10.3758/s13428-024-02336-0}
}

@proceedings{soto:iuiworkshops:2024,
  editor       = {Axel Soto and
                  Eva Zangerle},
  title        = {Joint Proceedings of the {ACM} {IUI} 2024 Workshops co-located with
                  the 29th Annual {ACM} Conference on Intelligent User Interfaces {(IUI}
                  2024), Greenville, South Carolina, USA, March 18, 2024},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3660},
  publisher    = {CEUR-WS.org},
  year         = {2024},
  url          = {https://ceur-ws.org/Vol-3660},
  urn          = {urn:nbn:de:0074-3660-6},
  timestamp    = {Fri, 12 Apr 2024 20:58:03 +0200},
  biburl       = {https://dblp.org/rec/conf/iui/2024w.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{10.1145/3648398,
author = {Bauer, Christine and Said, Alan and Zangerle, Eva},
title = {Introduction to the Special Issue on Perspectives on Recommender Systems Evaluation},
year = {2024},
issue_date = {March 2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {2},
number = {1},
url = {https://doi.org/10.1145/3648398},
doi = {10.1145/3648398},
abstract = {Evaluation plays a vital role in recommender systems—in research and practice—whether for confirming algorithmic concepts or assessing the operational validity of designs and applications. It may span the evaluation of early ideas and approaches up to elaborate implementations of systems integrated into everyday product settings; it may target a wide spectrum of different factors being evaluated. In this special issue, we explore recommender systems evaluation—theory and practice—while considering a diverse set of perspectives. These include recommender systems purposes, stakeholders, methodological approaches, and consequences. The collection of articles in this special issue offers insightful analyses of current recommender system evaluation practices, acknowledging their limitations, and setting out future research directions. As recommender systems evolve, the need for adequate evaluation methods and approaches increases. This special issue sheds light on areas undergoing development or requiring added attention from the research and practitioner communities in recommender systems. The compilation serves as a call to the recommender systems research community, motivating continued research and exploration of evaluation metrics, methods, and strategies.},
journal = {ACM Trans. Recomm. Syst.},
month = {mar},
articleno = {1},
numpages = {5},
keywords = {Evaluation, recommender systems, reproducibility, datasets, metrics}
}

@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 = {2024},
issue_date = {March 2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {2},
number = {1},
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 article, we present an overview of research specifically focused on the evaluation 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, normalized Discounted Cumulative Gain, 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.},
journal = {ACM Trans. Recomm. Syst.},
month = {mar},
articleno = {11},
numpages = {31},
keywords = {Evaluation, survey, systematic literature review, recommender systems}
}

@article{10.1145/3642979.3643000,
author = {Said, Alan and Zangerle, Eva and Bauer, Christine},
title = {Report on the 3rd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023) at RecSys 2023},
year = {2024},
issue_date = {December 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {57},
number = {2},
issn = {0163-5840},
url = {https://doi.org/10.1145/3642979.3643000},
doi = {10.1145/3642979.3643000},
journal = {SIGIR Forum},
month = {jan},
articleno = {18},
numpages = {4}
}

@inproceedings{DBLP:conf/recsys/Mohammadi23,
  author       = {Amir Reza Mohammadi},
  title        = {Explainable Graph Neural Network Recommenders; Challenges and Opportunities},
  booktitle    = {Proceedings of the 17th {ACM} Conference on Recommender Systems, RecSys
                  2023, Singapore, Singapore, September 18-22, 2023},
  pages        = {1318--1324},
  publisher    = {{ACM}},
  year         = {2023},
  url          = {https://doi.org/10.1145/3604915.3608875},
  doi          = {10.1145/3604915.3608875},
  timestamp    = {Sun, 24 Sep 2023 15:46:34 +0200},
  biburl       = {https://dblp.org/rec/conf/recsys/Mohammadi23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/recsys/Peintner23,
  author       = {Andreas Peintner},
  title        = {Sequential Recommendation Models: {A} Graph-based Perspective},
  booktitle    = {Proceedings of the 17th {ACM} Conference on Recommender Systems, RecSys
                  2023, Singapore, Singapore, September 18-22, 2023},
  pages        = {1295--1299},
  publisher    = {{ACM}},
  year         = {2023},
  url          = {https://doi.org/10.1145/3604915.3608776},
  doi          = {10.1145/3604915.3608776},
  timestamp    = {Sun, 24 Sep 2023 15:46:34 +0200},
  biburl       = {https://dblp.org/rec/conf/recsys/Peintner23.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}
}

@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}
}