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Dominik Kowald
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- affiliation: Graz University of Technology, Austria
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2020 – today
- 2024
- [j16]Tomislav Duricic, Dominik Kowald, Emanuel Lacic, Elisabeth Lex:
Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks. Frontiers Big Data 6 (2024) - [j15]Peter Müllner, Elisabeth Lex, Markus Schedl, Dominik Kowald:
Differential privacy in collaborative filtering recommender systems: a review. Frontiers Big Data 6 (2024) - [j14]Dominik Kowald
, Deqing Yang, Emanuel Lacic:
Editorial: Reviews in recommender systems: 2022. Frontiers Big Data 7 (2024) - [c49]Tomislav Duricic, Peter Müllner, Nicole Weidinger, Neven A. M. ElSayed, Dominik Kowald, Eduardo E. Veas:
AI-Powered Immersive Assistance for Interactive Task Execution in Industrial Environments. ECAI 2024: 4491-4494 - [c48]Peter Müllner
The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias. ECIR (4) 2024: 466-482 - [c47]Florian Koenigstorfer, Armin Haberl, Dominik Kowald
Black Box or Open Science? Assessing Reproducibility-Related Documentation in AI Research. HICSS 2024: 682-691 - [c46]Gustavo Escobedo
Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models. ECML/PKDD (7) 2024: 349-365 - [c45]Oleg Lesota
Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems. RecSys 2024: 1022-1027 - [i53]Peter Müllner, Elisabeth Lex, Markus Schedl, Dominik Kowald:
The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias. CoRR abs/2401.03883 (2024) - [i52]Dominik Kowald:
Transparency, Privacy, and Fairness in Recommender Systems. CoRR abs/2406.11323 (2024) - [i51]Gustavo Escobedo, Marta Moscati, Peter Muellner, Simone Kopeinik, Dominik Kowald, Elisabeth Lex, Markus Schedl:
Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models. CoRR abs/2406.11505 (2024) - [i50]Harald Semmelrock, Tony Ross-Hellauer, Simone Kopeinik, Dieter Theiler, Armin Haberl, Stefan Thalmann, Dominik Kowald:
Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers. CoRR abs/2406.14325 (2024) - [i49]Tomislav Duricic, Peter Müllner, Nicole Weidinger, Neven A. M. ElSayed, Dominik Kowald
AI-Powered Immersive Assistance for Interactive Task Execution in Industrial Environments. CoRR abs/2407.09147 (2024) - [i48]Oleg Lesota, Jonas Geiger, Max Walder, Dominik Kowald
Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems. CoRR abs/2408.11565 (2024) - [i47]Florian Atzenhofer-Baumgartner, Bernhard C. Geiger, Christoph Trattner, Georg Vogeler, Dominik Kowald:
Challenges in Implementing a Recommender System for Historical Research in the Humanities. CoRR abs/2410.20909 (2024) - 2023
- [j13]Peter Müllner
ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations. ACM Trans. Intell. Syst. Technol. 14(5): 80:1-80:29 (2023) - [c44]Dominik Kowald, Gregor Mayr, Markus Schedl, Elisabeth Lex:
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations. BIAS 2023: 1-16 - [c43]Emanuel Lacic, Tomislav Duricic, Leon Fadljevic, Dieter Theiler, Dominik Kowald
Uptrendz: API-Centric Real-Time Recommendations in Multi-domain Settings. ECIR (3) 2023: 255-261 - [c42]Marta Moscati
Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation. RecSys 2023: 840-847 - [d1]Marta Moscati
Files for Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation. Zenodo, 2023 - [i46]Emanuel Lacic, Tomislav Duricic, Leon Fadljevic, Dieter Theiler
Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings. CoRR abs/2301.01037 (2023) - [i45]Sebastian Scher, Bernhard C. Geiger, Simone Kopeinik, Andreas Trügler, Dominik Kowald
A conceptual model for leaving the data-centric approach in machine learning. CoRR abs/2302.03361 (2023) - [i44]Dominik Kowald
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations. CoRR abs/2303.00400 (2023) - [i43]Harald Semmelrock, Simone Kopeinik, Dieter Theiler
Reproducibility in Machine Learning-Driven Research. CoRR abs/2307.10320 (2023) - [i42]Tomislav Duricic, Dominik Kowald, Emanuel Lacic, Elisabeth Lex:
Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks. CoRR abs/2310.02294 (2023) - [i41]Armin Haberl, Jürgen Fleiß, Dominik Kowald, Stefan Thalmann:
Take the aTrain. Introducing an Interface for the Accessible Transcription of Interviews. CoRR abs/2310.11967 (2023) - 2022
- [c41]Dominik Kowald
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems. BIAS 2022: 1-11 - [c40]Peter Müllner, Stefan Schmerda, Dieter Theiler, Stefanie N. Lindstaedt, Dominik Kowald
Towards employing recommender systems for supporting data and algorithm sharing. DE@CoNEXT 2022: 8-14 - [c39]Emanuel Lacic
What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations. ECIR (2) 2022: 172-179 - [i40]Dominik Kowald, Emanuel Lacic:
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems. CoRR abs/2203.00376 (2022) - [i39]Emanuel Lacic, Dominik Kowald:
Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance. CoRR abs/2203.01256 (2022) - [i38]Peter Müllner, Markus Schedl, Elisabeth Lex, Dominik Kowald:
ReuseKNN: Neighborhood Reuse for Privacy-Aware Recommendations. CoRR abs/2206.11561 (2022) - [i37]Sebastian Scher, Simone Kopeinik, Andreas Trügler, Dominik Kowald:
Long-term dynamics of fairness: understanding the impact of data-driven targeted help on job seekers. CoRR abs/2208.08881 (2022) - [i36]Peter Müllner, Stefan Schmerda, Dieter Theiler
Towards Employing Recommender Systems for Supporting Data and Algorithm Sharing. CoRR abs/2210.11828 (2022) - 2021
- [j12]Dominik Kowald
Support the underground: characteristics of beyond-mainstream music listeners. EPJ Data Sci. 10(1): 14 (2021) - [j11]Elisabeth Lex, Dominik Kowald
Psychology-informed Recommender Systems. Found. Trends Inf. Retr. 15(2): 134-242 (2021) - [c38]Tomislav Duricic, Dominik Kowald
My friends also prefer diverse music: homophily and link prediction with user preferences for mainstream, novelty, and diversity in music. ASONAM 2021: 447-454 - [c37]Peter Müllner
Robustness of Meta Matrix Factorization Against Strict Privacy Constraints. ECIR (2) 2021: 107-119 - [c36]Oleg Lesota, Alessandro B. Melchiorre
Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected? RecSys 2021: 601-606 - [i35]Peter Müllner, Dominik Kowald, Elisabeth Lex:
Robustness of Meta Matrix Factorization Against Strict Privacy Constraints. CoRR abs/2101.06927 (2021) - [i34]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) - [i33]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) - [i32]Peter Müllner, Elisabeth Lex, Dominik Kowald:
Position Paper on Simulating Privacy Dynamics in Recommender Systems. CoRR abs/2109.06473 (2021) - [i31]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) - [i30]Emanuel Lacic, Leon Fadljevic, Franz Weissenboeck, Stefanie N. Lindstädt, Dominik Kowald:
What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations. CoRR abs/2111.14467 (2021) - 2020
- [j10]Markus Schedl, Christine Bauer
Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes. Frontiers Artif. Intell. 3: 508725 (2020) - [j9]Elisabeth Lex, Dominik Kowald
Modeling Popularity and Temporal Drift of Music Genre Preferences. Trans. Int. Soc. Music. Inf. Retr. 3(1): 17-30 (2020) - [j8]Emanuel Lacic, Markus Reiter-Haas
Using autoencoders for session-based job recommendations. User Model. User Adapt. Interact. 30(4): 617-658 (2020) - [c35]Dominik Kowald
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study. ECIR (2) 2020: 35-42 - [c34]Tomislav Duricic, Hussain Hussain, Emanuel Lacic, Dominik Kowald
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering. ISMIS 2020: 181-191 - [c33]Leon Fadljevic, Katharina Maitz
Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy. LAK 2020: 112-117 - [i29]Dominik Kowald, Elisabeth Lex, Markus Schedl:
Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations. CoRR abs/2003.10699 (2020) - [i28]Tomislav Duricic, Hussain Hussain, Emanuel Lacic, Dominik Kowald, Denis Helic, Elisabeth Lex:
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering. CoRR abs/2003.13345 (2020) - [i27]Markus Schedl, Christine Bauer
Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes. CoRR abs/2009.09935 (2020)
2010 – 2019
- 2019
- [j7]Adolfo Ruiz-Calleja, Sebastian Dennerlein
An Infrastructure for Workplace Learning Analytics: Tracing Knowledge Creation with the Social Semantic Server. J. Learn. Anal. 6(2) (2019) - [c32]Simone Kopeinik
A Real-Life School Study of Confirmation Bias and Polarisation in Information Behaviour. EC-TEL 2019: 409-422 - [c31]Elisabeth Lex, Dominik Kowald
The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach. GI-Jahrestagung 2019: 285-286 - [i26]Dominik Kowald, Simone Kopeinik, Elisabeth Lex:
The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems. CoRR abs/1901.00306 (2019) - [i25]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) - [i24]Tomislav Duricic, Emanuel Lacic, Dominik Kowald, Elisabeth Lex:
Exploiting weak ties in trust-based recommender systems using regular equivalence. CoRR abs/1907.11620 (2019) - [i23]Elisabeth Lex, Dominik Kowald:
The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach. CoRR abs/1908.00977 (2019) - [i22]Dominik Kowald, Matthias Traub, Dieter Theiler, Heimo Gursch, Emanuel Lacic, Stefanie N. Lindstaedt, Roman Kern, Elisabeth Lex:
Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets. CoRR abs/1908.04017 (2019) - [i21]Emanuel Lacic, Dominik Kowald, Dieter Theiler, Matthias Traub, Lucky Kuffer, Stefanie N. Lindstaedt, Elisabeth Lex:
Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric. CoRR abs/1908.04042 (2019) - [i20]Dominik Kowald, Markus Schedl, Elisabeth Lex:
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study. CoRR abs/1912.04696 (2019) - 2018
- [j6]Paul Seitlinger, Tobias Ley
Balancing the Fluency-Consistency Tradeoff in Collaborative Information Search with a Recommender Approach. Int. J. Hum. Comput. Interact. 34(6): 557-575 (2018) - [c30]Dominik Kowald, Emanuel Lacic, Dieter Theiler, Elisabeth Lex:
AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments. CIKM Workshops 2018 - [c29]Emanuel Lacic, Dominik Kowald, Elisabeth Lex:
Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations. CIKM Workshops 2018 - [c28]Sebastian Dennerlein, Dominik Kowald, Viktoria Pammer-Schindler, Elisabeth Lex, Tobias Ley:
Simulation-based Co-Creation of Algorithms. CC-TEL/TACKLE@EC-TEL 2018 - [c27]Angela Fessl, Dominik Kowald, Susana López-Sola, Ana Moreno, Ricardo Alonso Maturana, Stefan Thalmann:
Analytics for Everyday Learning from two Perspectives: Knowledge Workers and Teachers. AFEL@EC-TEL 2018 - [c26]Tomislav Duricic, Emanuel Lacic
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence. RecSys 2018: 446-450 - [c25]Dominik Kowald
The Impact of Semantic Context Cues on the User Acceptance of Tag Recommendations: An Online Study. WWW (Companion Volume) 2018: 1-2 - [c24]Mathieu d'Aquin, Dominik Kowald
AFEL - Analytics for Everyday Learning. WWW (Companion Volume) 2018: 439-440 - [i19]Dominik Kowald, Paul Seitlinger, Tobias Ley, Elisabeth Lex:
The Impact of Semantic Context Cues on the User Acceptance of Tag Recommendations: An Online Study. CoRR abs/1803.02179 (2018) - [i18]Dominik Kowald:
Modeling Activation Processes in Human Memory to Improve Tag Recommendations. CoRR abs/1803.03176 (2018) - [i17]Dominik Kowald, Elisabeth Lex:
Overcoming the Imbalance Between Tag Recommendation Approaches and Real-World Folksonomy Structures with Cognitive-Inspired Algorithms. CoRR abs/1805.03067 (2018) - [i16]Dominik Kowald:
Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations. CoRR abs/1805.11878 (2018) - [i15]Tomislav Duricic, Emanuel Lacic, Dominik Kowald, Elisabeth Lex:
Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence. CoRR abs/1807.06839 (2018) - [i14]Dominik Kowald, Emanuel Lacic, Dieter Theiler, Elisabeth Lex:
AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments. CoRR abs/1808.04603 (2018) - [i13]Emanuel Lacic, Dominik Kowald, Elisabeth Lex:
Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations. CoRR abs/1808.06417 (2018) - [i12]Dominik Kowald, Elisabeth Lex:
Studying Confirmation Bias in Hashtag Usage on Twitter. CoRR abs/1809.03203 (2018) - [i11]Elisabeth Lex, Mario Wagner, Dominik Kowald:
Mitigating Confirmation Bias on Twitter by Recommending Opposing Views. CoRR abs/1809.03901 (2018) - 2017
- [j5]Simone Kopeinik
Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning. J. Web Sci. 2(4): 45-61 (2017) - [j4]Dominik Kowald:
Modeling Activation Processes in Human Memory to Improve Tag Recommendations. SIGIR Forum 51(3): 166 (2017) - [c23]Mathieu d'Aquin, Alessandro Adamou, Stefan Dietze, Besnik Fetahu, Ujwal Gadiraju, Ilire Hasani-Mavriqi, Peter Holtz, Joachim Kimmerle, Dominik Kowald, Elisabeth Lex, Susana López-Sola, Ricardo Alonso Maturana, Vedran Sabol, Pinelopi Troullinou, Eduardo E. Veas:
AFEL: Towards Measuring Online Activities Contributions to Self-directed Learning. ARTEL@EC-TEL 2017 - [c22]Emanuel Lacic, Dominik Kowald, Elisabeth Lex:
Tailoring Recommendations for a Multi-Domain Environment. RecSysKTL 2017: 42-45 - [c21]Dominik Kowald, Simone Kopeinik, Elisabeth Lex:
The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems. UMAP (Adjunct Publication) 2017: 23-28 - [c20]Dominik Kowald
Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach. WWW 2017: 1401-1410 - [i10]Dominik Kowald, Subhash Chandra Pujari, Elisabeth Lex:
Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach. CoRR abs/1701.01276 (2017) - [i9]Emanuel Lacic, Dominik Kowald, Markus Reiter-Haas
Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendations. CoRR abs/1711.07762 (2017) - 2016
- [j3]Patricia Santos, Elisabeth Lex, Sebastian Dennerlein, Dieter Theiler, John Cook, Tamsin Treasure-Jones, Debbie Holley, Micky Kerr, Graham Attwell, Dominik Kowald:
Going beyond your Personal Learning Network, Using Recommendations and Trust through a Multimedia Question-Answering Service for Decision-support: a Case Study in the Healthcare. J. Univers. Comput. Sci. 22(3): 340-359 (2016) - [j2]Christoph Trattner, Dominik Kowald, Paul Seitlinger, Tobias Ley
Modeling Activation Processes in Human Memory to Predict the Use of Tags in Social Bookmarking Systems. J. Web Sci. 2(1): 1-16 (2016) - [c19]Simone Kopeinik
Which Algorithms Suit Which Learning Environments? A Comparative Study of Recommender Systems in TEL. EC-TEL 2016: 124-138 - [c18]Dominik Kowald
The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems. HT 2016: 237-242 - [c17]Emanuel Lacic
High Enough?: Explaining and Predicting Traveler Satisfaction Using Airline Reviews. HT 2016: 249-254 - [i8]Dominik Kowald, Elisabeth Lex:
The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems. CoRR abs/1604.00837 (2016) - [i7]Emanuel Lacic, Dominik Kowald, Elisabeth Lex:
High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Review. CoRR abs/1604.00942 (2016) - 2015
- [j1]Christoph Trattner, Dominik Kowald, Emanuel Lacic:
TagRec: towards a toolkit for reproducible evaluation and development of tag-based recommender algorithms. SIGWEB Newsl. 2015(Winter): 3:1-3:10 (2015) - [c16]Sebastian Dennerlein
The social semantic server: a flexible framework to support informal learning at the workplace. I-KNOW 2015: 26:1-26:8 - [c15]Matthias Traub, Dominik Kowald
Smart booking without looking: providing hotel recommendations in the TripRebel portal. I-KNOW 2015: 50:1-50:4 - [c14]Dominik Kowald
Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study. RecSys 2015: 265-268 - [c13]Emanuel Lacic, Dominik Kowald, Matthias Traub, Granit Luzhnica, Jörg Simon, Elisabeth Lex:
Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems. RecSys Posters 2015 - [c12]Paul Seitlinger
Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics. WWW (Companion Volume) 2015: 339-345 - [c11]Dominik Kowald
Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations. WWW (Companion Volume) 2015: 505-509 - [i6]Paul Seitlinger, Dominik Kowald
Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics. CoRR abs/1501.07716 (2015) - 2014
- [c10]Dominik Kowald
TagRec: towards a standardized tag recommender benchmarking framework. HT 2014: 305-307 - [c9]Emanuel Lacic, Dominik Kowald, Paul Seitlinger, Christoph Trattner, Denis Parra:
Recommending Items in Social Tagging Systems Using Tag and Time Informations. HT (Doctoral Consortium / Late-breaking Results / Workshops) 2014 - [c8]Emanuel Lacic
SocRecM: a scalable social recommender engine for online marketplaces. HT 2014: 308-310 - [c7]Dominik Kowald
Long time no see: the probability of reusing tags as a function of frequency and recency. WWW (Companion Volume) 2014: 463-468 - [c6]Emanuel Lacic
Towards a scalable social recommender engine for online marketplaces: the case of apache solr. WWW (Companion Volume) 2014: 817-822 - [i5]Dominik Kowald, Paul Seitlinger, Christoph Trattner, Tobias Ley:
Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender. CoRR abs/1402.0728 (2014) - [i4]Emanuel Lacic, Dominik Kowald, Lukas Eberhard, Christoph Trattner, Denis Parra, Leandro Balby Marinho:
Utilizing Online Social Network and Location-Based Data to Recommend Items in an Online Marketplace. CoRR abs/1405.1837 (2014) - [i3]Emanuel Lacic, Dominik Kowald, Christoph Trattner:
SocRecM: A Scalable Social Recommender Engine for Online Marketplaces. CoRR abs/1405.1842 (2014) - [i2]Emanuel Lacic, Dominik Kowald, Paul Seitlinger, Christoph Trattner, Denis Parra:
Recommending Items in Social Tagging Systems Using Tag and Time Information. CoRR abs/1406.7727 (2014) - 2013
- [c5]Paul Seitlinger
Recommending tags with a model of human categorization. CIKM 2013: 2381-2386 - [c4]Dominik Kowald, Sebastian Dennerlein, Dieter Theiler, Simon Walk, Christoph Trattner:
The Social Semantic Server - A Framework to Provide Services on Social Semantic Network Data. I-SEMANTICS (Posters & Demos) 2013: 50-54 - [c3]Dominik Kowald
Refining Frequency-Based Tag Reuse Predictions by Means of Time and Semantic Context. MSM/MUSE 2013: 55-74 - [c2]Dominik Kowald
Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender. MSM/MUSE 2013: 75-95 - [c1]Emanuel Lacic
Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces. MSM/MUSE 2013: 96-115 - [i1]Dominik Kowald, Paul Seitlinger, Christoph Trattner, Tobias Ley:
Long Time No See: The Probability of Reusing Tags as a Function of Frequency and Recency. CoRR abs/1312.5111 (2013)
Coauthor Index
[j16] [j15] [c48] [c46] [i53] [i51] [j13] [c44] [c42] [d1] [i44] [i42] [i38] [j12] [j11] [c38] [c37] [c36] [i35] [i34] [i33] [i32] [i31] [j10] [j9] [j8] [c35] [c34] [i29] [i28] [i27] [j7] [c32] [c31] [i26] [i25] [i24] [i23] [i22] [i21] [i20] [j6] [c30] [c29] [c28] [c26] [c25] [c24] [i19] [i17] [i15] [i14] [i13] [i12] [i11] [j5] [c23] [c22] [c21] [c20] [i10] [i9] [j3] [c19] [c18] [c17] [i8] [i7] [c16] [c15] [c14] [c13] [c12] [i6]
Peter Müllner
aka: Peter Muellner
aka: Peter Muellner
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