Martin Pichl, PhD

@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}
}

@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{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{8516495,
title = {Latent Feature Combination for Multi-Context Music Recommendation},
author = {Martin Pichl and Eva Zangerle},
doi = {10.1109/CBMI.2018.8516495},
year = {2018},
date = {2018-09-01},
booktitle = {2018 International Conference on Content-Based Multimedia Indexing (CBMI)},
pages = {1-6},
abstract = {In recent years, music aficionados have increasingly been consuming music via public music streaming platforms. Due to the size of the collections provided, music recommender systems have become a vital component as these aim to provide recommendations that match the user's current context as, throughout the day, users listen to music in numerous different contexts and situations. In this paper, we propose a multi-context-aware track recommender system that jointly exploits information about the current situation and musical preferences of users. To jointly model users by their situational and musical preferences, we cluster users based on their situational features and similarly, cluster music tracks based on their content features. Our experiments show that by relying on Factorization Machines for the computation of recommendations, the proposed approach allows to successfully leverage interaction effects between listening histories, situational and track content information, substantially outperforming a set of baseline recommenders.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{zangerle_ismir18,
title = {Content-based User Models: Modeling the Many Faces of Musical Preference},
author = {Eva Zangerle and Martin Pichl},
editor = {Emilia Gomez and Xiao Hu and Eric Humphrey and Emmanouil Benetos},
year = {2018},
date = {2018-09-22},
booktitle = {Proceedings of the 19th International Society for Music Information Retrieval Conference 2018 (ISMIR 2018)},
pages = {709--716}
}

@inproceedings{DBLP:conf/sigir/PichlPZ18,
  author    = {Martin Pichl and
               Bernward Pichl and
               Eva Zangerle},
  title     = {Carl: Sports Award Recommender},
  booktitle = {The {SIGIR} 2018 Workshop On eCommerce co-located with the 41st International
               {ACM} {SIGIR} Conference on Research and Development in Information
               Retrieval {(SIGIR} 2018), Ann Arbor, Michigan, USA, July 12, 2018.},
  year      = {2018},
  url       = {http://ceur-ws.org/Vol-2319/paper11.pdf},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2319},
  urn       = {urn:nbn:de:0074-2319-2},
  publisher = {CEUR-WS.org},
}

@inproceedings{Zangerle:2018:CMR:3209219.3209258,
 author = {Zangerle, Eva and Pichl, Martin and Schedl, Markus},
 title = {Culture-Aware Music Recommendation},
 booktitle = {Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization},
 series = {UMAP '18},
 year = {2018},
 isbn = {978-1-4503-5589-6},
 location = {Singapore, Singapore},
 pages = {357--358},
 numpages = {2},
 url = {http://doi.acm.org/10.1145/3209219.3209258},
 doi = {10.1145/3209219.3209258},
 acmid = {3209258},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {music information retrieval, recommender systems, user modeling},
} 

@inproceedings{Binna:2018:HHO:3183713.3196896,
 author = {Binna, Robert and Zangerle, Eva and Pichl, Martin and Specht, G\"{u}nther and Leis, Viktor},
 title = {HOT: A Height Optimized Trie Index for Main-Memory Database Systems},
 booktitle = {Proceedings of the 2018 International Conference on Management of Data},
 series = {SIGMOD '18},
 year = {2018},
 isbn = {978-1-4503-4703-7},
 location = {Houston, TX, USA},
 pages = {521--534},
 numpages = {14},
 url = {http://doi.acm.org/10.1145/3183713.3196896},
 doi = {10.1145/3183713.3196896},
 acmid = {3196896},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {height optimized trie, index, main memory, simd},
} 

@PHDTHESIS {pichl2018-diss,
    author = {Martin Pichl},
    title  = {{Multi-Context-Aware Recommender Systems: A Study on Music Recommendation}},
    school = {University of Innsbruck, Department of Computer Science},
    year   = {2018},
    month  = {feb}
}

@inproceedings{sympmm,
title = {Mining Culture-Specific Music Listening Behavior from Social Media Data},
author = {Pichl, Martin and Zangerle, Eva and Specht, G\"unther and Markus Schedl},
doi = {10.1109/ISM.2017.35},
isbn = {978-1-5386-2937-6/17},
year = {2017},
booktitle = {IEEE International Symposium on Multimedia, ISM 2017, Taichung, Taiwan, December 11-13, 2017},
pages = {208--215},
publisher = {IEEE Computer Society}
}

@article{Murauer2017Genre,
  title={Hierarchical Multilabel Classification and Voting for Genre Classification},
  author={Murauer, Benjamin and Mayerl, Maximilian and Tschuggnall, Michael and Zangerle, Eva and Pichl, Martin and Specht, G{\"u}nther},
  booktitle={CEURS Working Notes Proceedings of the MediaEval 2017 Workshop},
  publisher={CEUR-WS.org},
  city={Dublin, Ireland},
  year={2017},
  url={http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_41.pdf},
}

@inproceedings{Pichl:2017:ICM:3078971.3078980,
 author = {Pichl, Martin and Zangerle, Eva and Specht, G\"{u}nther},
 title = {Improving Context-Aware Music Recommender Systems: Beyond the Pre-filtering Approach},
 booktitle = {Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval},
 series = {ICMR '17},
 year = {2017},
 isbn = {978-1-4503-4701-3},
 location = {Bucharest, Romania},
 pages = {201--208},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/3078971.3078980},
 doi = {10.1145/3078971.3078980},
 acmid = {3078980},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {context, personalization, recommender systems, user modeling},
} 

@article{ijmdem17,
	author = {Martin Pichl and
	Eva Zangerle and
	G{\"{u}}nther Specht},
	title = {Understanding User-curated Playlists on Spotify: A Machine Learning Approach},
	journal = {International Journal of Multimedia Data Engineering and Management {IJMDEM}},
	volume = {8},
	number = {4}, 
	year = {2017}
}

@inProceedings{sympmm16,
title = {{Understanding Playlist Creation on Music Streaming Platforms}},
booktitle = {Proceedings of the IEEE Symposium on Multimedia (ISM)},
publisher = {IEEE},
year = {2016},
author = {Pichl, Martin and Zangerle, Eva and Specht, G\"unther},
pages = {475--480}
}

@inproceedings{DBLP:conf/ismir/ZangerlePHS16,
  author    = {Eva Zangerle and
               Martin Pichl and
               Benedikt Hupfauf and
               G{\"{u}}nther Specht},
  editor    = {Michael I. Mandel and
               Johanna Devaney and
               Douglas Turnbull and
               George Tzanetakis},
  title     = {Can Microblogs Predict Music Charts? An Analysis of the Relationship
               Between {\#}Nowplaying Tweets and Music Charts},
  booktitle = {Proceedings of the 17th International Society for Music Information
               Retrieval Conference, {ISMIR} 2016, New York City, United States,
               August 7-11, 2016},
  pages     = {365--371},
  year      = {2016},
  url       = {https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/07/039_Paper.pdf},
  timestamp = {Thu, 08 Sep 2016 13:32:51 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/ismir/ZangerlePHS16},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  publisher = {ISMIR}
}

@inproceedings{DBLP:conf/wikis/ZangerleGPSS16,
  author    = {Eva Zangerle and
               Wolfgang Gassler and
               Martin Pichl and
               Stefan Steinhauser and
               G{\"{u}}nther Specht},
  editor    = {Anthony I. Wasserman},
  title     = {An Empirical Evaluation of Property Recommender Systems for Wikidata
               and Collaborative Knowledge Bases},
  booktitle = {Proceedings of the 12th International Symposium on Open Collaboration,
               OpenSym 2016, Berlin, Germany, August 17-19, 2016},
  pages     = {18:1--18:8},
  publisher = {{ACM}},
  year      = {2016},
  url       = {http://doi.acm.org/10.1145/2957792.2957804},
  doi       = {10.1145/2957792.2957804},
  timestamp = {Mon, 31 Oct 2016 08:29:49 +0100},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/wikis/ZangerleGPSS16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@inProceedings{somera15,
author = {Pichl, Martin and Zangerle, Eva and Specht, G\"{u}nther},
title = {Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?},
booktitle = {15th IEEE International Conference on Data Mining Workshops (ICDM 2015)},
series = {ICDM 15},
year = {2015},
location = {Atlantic City},
pages = {1360--1365},
numpages = {6},
month = nov,
publisher = {IEEE},
address = {Piscataway, NJ, USA},
}

@inProceedings{sowemine15,
booktitle = {{Current Trends in Web Engineering, 15th International Conference, ICWE 2015 Workshops (Revised Selected Papers)}},
title = {{#nowplaying on #Spotify: Leveraging Spotify Information on Twitter for Artist Recommendations}},
publisher = {Springer},
year = {2015},
pages = {163--174},
author={Pichl, Martin and Zangerle, Eva and Specht, G\"unther}
}

@inproceedings{gvdb14,
author = {Pichl, Martin and Zangerle, Eva and Specht, G\"unther},
booktitle = {Proceedings of the 26nd Workshop Grundlagen von Datenbanken (GvDB 2014), Ritten, Italy},
publisher = {CEUR-WS.org},
title = {{Combining Spotify and Twitter Data for Generating a Recent and Public Dataset for Music Recommendation}},
year = {2014},
volume = {1313},
month = oct,
pages = {35--40},
howpublished = {online},
}

@InProceedings{ismm14,
title={#nowplaying Music Dataset: Extracting Listening Behavior from Twitter},
author={Zangerle, Eva and Pichl, Martin and Gassler, Wolfgang and Specht, G\"unther},
year={2014},
pages={21--26},
location = {Orlando, Florida, USA},
publisher = {ACM},
address = {New York, NY, USA},
month = jun,
booktitle = {Proceedings of the 1st ACM International Workshop on Internet-Scale Multimedia Management},
series = {ISMM '14},
}