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ORCIDDietmar Jannach
Person information
- affiliation: University of Klagenfurt, Austria
- affiliation: University of Bergen, Norway
- affiliation (former): Dortmund University of Technology, Germany
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2020 – today
- 2024
[j97]Nicholas Diakopoulos
, Christoph Trattner, Dietmar Jannach, Irene Costera Meijer, Enrico Motta:
Leveraging Professional Ethics for Responsible AI. Commun. ACM 67(2): 19-21 (2024)- [j96]Alvise De Biasio, Nicolò Navarin, Dietmar Jannach:
Economic recommender systems - a systematic review. Electron. Commer. Res. Appl. 63: 101352 (2024) - [j95]Alvise De Biasio, Dietmar Jannach, Nicolò Navarin:
Model-based approaches to profit-aware recommendation. Expert Syst. Appl. 249: 123642 (2024) - [j94]Dietmar Jannach, Himan Abdollahpouri:
A survey on multi-objective recommender systems. Frontiers Big Data 6 (2024) - [j93]Lina Yao, Julian J. McAuley, Xianzhi Wang, Dietmar Jannach:
Special Issue on Responsible Recommender Systems Part 1. ACM Trans. Intell. Syst. Technol. 15(4): 72:1-72:3 (2024) - [j92]Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogín, Alessandro Difonzo, Dario Zanzonelli:
Fairness in recommender systems: research landscape and future directions. User Model. User Adapt. Interact. 34(1): 59-108 (2024) - [j91]Gabrielle Alves, Dietmar Jannach, Rodrigo Ferrari de Souza, Daniela E. Damian, Marcelo Garcia Manzato:
Digitally nudging users to explore off-profile recommendations: here be dragons. User Model. User Adapt. Interact. 34(2): 441-481 (2024) - [j90]Josef Bauer, Dietmar Jannach:
Hybrid session-aware recommendation with feature-based models. User Model. User Adapt. Interact. 34(3): 691-728 (2024) - [c159]Faisal Shehzad, Dietmar Jannach:
Performance Comparison of Session-Based Recommendation Algorithms Based on GNNs. ECIR (4) 2024: 115-131 - [c158]Gabrielle Alves, Dietmar Jannach, Rodrigo Ferrari de Souza, Marcelo Garcia Manzato:
User Perception of Fairness-Calibrated Recommendations. UMAP 2024: 78-88 - [c157]Ahtsham Manzoor, Samuel C. Ziegler, Klaus Maria Pirker Garcia, Dietmar Jannach:
ChatGPT as a Conversational Recommender System: A User-Centric Analysis. UMAP 2024: 267-272 - [c156]Laurens Rook, Markus Zanker, Dietmar Jannach:
Are We Losing Interest in Context-Aware Recommender Systems? UMAP (Adjunct Publication) 2024 - [d3]Adil Mukhtar, Dietmar Jannach, Franz Wotawa:
Investigating Reproducibility in Deep Learning-Based Software Fault Prediction. Version 1. Zenodo, 2024 [all versions]
- [d2]Adil Mukhtar, Dietmar Jannach, Franz Wotawa:
Investigating Reproducibility in Deep Learning-Based Software Fault Prediction. Version 2. Zenodo, 2024 [all versions] - [i50]Artun Boz, Wouter Zorgdrager, Zoe Kotti, Jesse Harte, Panos Louridas, Dietmar Jannach, Marios Fragkoulis:
Improving Sequential Recommendations with LLMs. CoRR abs/2402.01339 (2024) - [i49]Adil Mukhtar, Dietmar Jannach, Franz Wotawa:
Investigating Reproducibility in Deep Learning-Based Software Fault Prediction. CoRR abs/2402.05645 (2024) - [i48]Jin Li, Shoujin Wang, Qi Zhang, Longbing Cao, Fang Chen, Xiuzhen Zhang, Dietmar Jannach, Charu C. Aggarwal:
Causal Learning for Trustworthy Recommender Systems: A Survey. CoRR abs/2402.08241 (2024) - [i47]Dietmar Jannach, Markus Zanker:
A Survey on Intent-aware Recommender Systems. CoRR abs/2406.16350 (2024) - [i46]Matteo Attimonelli, Claudio Pomo, Dietmar Jannach, Tommaso Di Noia:
Fashion Image-to-Image Translation for Complementary Item Retrieval. CoRR abs/2408.09847 (2024) - 2023
- [j89]Dietmar Jannach:
Evaluating conversational recommender systems. Artif. Intell. Rev. 56(3): 2365-2400 (2023) - [j88]Adil Mukhtar, Birgit Hofer, Dietmar Jannach, Franz Wotawa:
Explaining software fault predictions to spreadsheet users. J. Syst. Softw. 201: 111676 (2023) - [j87]Christine Bauer, Ben Carterette, Nicola Ferro, Norbert Fuhr, Joeran Beel, Timo Breuer, Charles L. A. Clarke, Anita Crescenzi, Gianluca Demartini, Giorgio Maria Di Nunzio, Laura Dietz, Guglielmo Faggioli, Bruce Ferwerda, Maik Fröbe, Matthias Hagen, Allan Hanbury, Claudia Hauff, Dietmar Jannach, Noriko Kando, Evangelos Kanoulas, Bart P. Knijnenburg, Udo Kruschwitz, Meijie Li, Maria Maistro, Lien Michiels, Andrea Papenmeier, Martin Potthast, Paolo Rosso, Alan Said, Philipp Schaer, Christin Seifert, Damiano Spina, Benno Stein, Nava Tintarev, Julián Urbano, Henning Wachsmuth, Martijn C. Willemsen, Justin Zobel:
Report on the Dagstuhl Seminar on Frontiers of Information Access Experimentation for Research and Education. SIGIR Forum 57(1): 7:1-7:28 (2023) - [j86]Li Chen, Dietmar Jannach:
ACM Transactions on Recommender Systems: Inaugural Issue Editorial. Trans. Recomm. Syst. 1(1): 1 (2023) - [j85]Veronika Bogina, Tsvi Kuflik, Dietmar Jannach, Mária Bieliková, Michal Kompan, Christoph Trattner:
Considering temporal aspects in recommender systems: a survey. User Model. User Adapt. Interact. 33(1): 81-119 (2023) - [j84]Mathias Jesse, Christine Bauer, Dietmar Jannach:
Intra-list similarity and human diversity perceptions of recommendations: the details matter. User Model. User Adapt. Interact. 33(4): 769-802 (2023) - [c155]Ahtsham Manzoor, Wanling Cai, Dietmar Jannach:
Factors Influencing the Perceived Meaningfulness of System Responses in Conversational Recommendation. IntRS@RecSys 2023: 19-34 - [c154]Dietmar Jannach:
Sequential and Session-based Recommendation: Past, Present, Future. ORSUM@RecSys 2023 - [c153]Faisal Shehzad, Dietmar Jannach:
Everyone's a Winner! On Hyperparameter Tuning of Recommendation Models. RecSys 2023: 652-657 - [c152]Anastasiia Klimashevskaia, Mehdi Elahi, Dietmar Jannach, Lars Skjærven, Astrid Tessem, Christoph Trattner:
Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study. RecSys 2023: 1084-1089 - [c151]Jesse Harte, Wouter Zorgdrager, Panos Louridas, Asterios Katsifodimos, Dietmar Jannach, Marios Fragkoulis:
Leveraging Large Language Models for Sequential Recommendation. RecSys 2023: 1096-1102 - [c150]Siyu Wang, Xiaocong Chen, Dietmar Jannach, Lina Yao:
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning. SIGIR 2023: 1599-1608 - [c149]Koby Bibas, Oren Sar Shalom, Dietmar Jannach:
Semi-supervised Adversarial Learning for Complementary Item Recommendation. WWW 2023: 1804-1812 - [i45]Pablo Castells, Dietmar Jannach:
Recommender Systems: A Primer. CoRR abs/2302.02579 (2023) - [i44]Koby Bibas, Oren Sar Shalom, Dietmar Jannach:
Semi-supervised Adversarial Learning for Complementary Item Recommendation. CoRR abs/2303.05812 (2023) - [i43]Siyu Wang, Xiaocong Chen, Dietmar Jannach, Lina Yao:
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning. CoRR abs/2304.07920 (2023) - [i42]Anastasiia Klimashevskaia, Dietmar Jannach, Mehdi Elahi, Christoph Trattner:
A Survey on Popularity Bias in Recommender Systems. CoRR abs/2308.01118 (2023) - [i41]Zehui Wang, Wolfram Höpken, Dietmar Jannach:
A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data. CoRR abs/2308.07426 (2023) - [i40]Xiaocong Chen, Siyu Wang, Julian J. McAuley, Dietmar Jannach, Lina Yao:
On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems. CoRR abs/2308.11336 (2023) - [i39]Alvise De Biasio, Nicolò Navarin, Dietmar Jannach:
Economic Recommender Systems - A Systematic Review. CoRR abs/2308.11998 (2023) - [i38]Jesse Harte, Wouter Zorgdrager, Panos Louridas, Asterios Katsifodimos, Dietmar Jannach, Marios Fragkoulis:
Leveraging Large Language Models for Sequential Recommendation. CoRR abs/2309.09261 (2023) - [i37]Eduardo Witter dos Santos, Ingrid Nunes, Dietmar Jannach:
Team-related Features in Code Review Prediction Models. CoRR abs/2312.06244 (2023) - [i36]Faisal Shehzad, Dietmar Jannach:
Performance Comparison of Session-based Recommendation Algorithms based on GNNs. CoRR abs/2312.16695 (2023) - 2022
- [j83]Mehdi Elahi, Dietmar Jannach, Lars Skjærven, Erik Knudsen, Helle Sjøvaag, Kristian Tolonen, Øyvind Holmstad, Igor Pipkin, Eivind Throndsen, Agnes Stenbom, Eivind Fiskerud, Adrian Oesch, Loek Vredenberg, Christoph Trattner:
Towards responsible media recommendation. AI Ethics 2(1): 103-114 (2022) - [j82]Christoph Trattner, Dietmar Jannach, Enrico Motta, Irene Costera Meijer, Nicholas Diakopoulos, Mehdi Elahi, Andreas L. Opdahl, Bjørnar Tessem, Njål Borch, Morten Fjeld, Lilja Øvrelid, Koenraad De Smedt, Hallvard Moe:
Responsible media technology and AI: challenges and research directions. AI Ethics 2(4): 585-594 (2022) - [j81]Dietmar Jannach, Pearl Pu, Francesco Ricci, Markus Zanker:
Recommender Systems: Trends and Frontiers. AI Mag. 43(2): 145-150 (2022) - [j80]Dietmar Jannach, Li Chen:
Conversational Recommendation: A Grand AI Challenge. AI Mag. 43(2): 151-163 (2022) - [j79]Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, Li Chen:
A Survey on Conversational Recommender Systems. ACM Comput. Surv. 54(5): 105:1-105:36 (2022) - [j78]Nada Ghanem, Stephan Leitner, Dietmar Jannach:
Balancing consumer and business value of recommender systems: A simulation-based analysis. Electron. Commer. Res. Appl. 55: 101195 (2022) - [j77]Ahtsham Manzoor, Dietmar Jannach:
Towards retrieval-based conversational recommendation. Inf. Syst. 109: 102083 (2022) - [j76]Sara Latifi, Dietmar Jannach, Andrés Ferraro:
Sequential recommendation: A study on transformers, nearest neighbors and sampled metrics. Inf. Sci. 609: 660-678 (2022) - [j75]Tommaso Di Noia, Francesco Maria Donini, Dietmar Jannach, Fedelucio Narducci, Claudio Pomo:
Conversational recommendation: Theoretical model and complexity analysis. Inf. Sci. 614: 325-347 (2022) - [j74]Adil Mukhtar, Birgit Hofer, Dietmar Jannach, Franz Wotawa:
Spreadsheet debugging: The perils of tool over-reliance. J. Syst. Softw. 184: 111119 (2022) - [c148]Anastasiia Klimashevskaia, Mehdi Elahi, Dietmar Jannach, Christoph Trattner, Lars Skjærven:
Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches. BIAS 2022: 82-90 - [c147]Koby Bibas, Oren Sar Shalom, Dietmar Jannach:
Collaborative Image Understanding. CIKM 2022: 77-87 - [c146]Tommaso Di Noia, Francesco Maria Donini, Dietmar Jannach, Fedelucio Narducci, Claudio Pomo:
Towards a theoretical formalization of conversational recommendation. CIKM Workshops 2022 - [c145]Ahtsham Manzoor, Dietmar Jannach:
Revisiting Retrieval-based Approaches for Conversational Recommender Systems. IIR 2022 - [c144]Adil Mukhtar, Birgit Hofer, Dietmar Jannach, Franz Wotawa, Konstantin Schekotihin:
Boosting Spectrum-Based Fault Localization for Spreadsheets with Product Metrics in a Learning Approach. ASE 2022: 175:1-175:5 - [c143]Dietmar Jannach:
Multi-Objective Recommendation: Overview and Challenges. MORS@RecSys 2022 - [c142]Ahtsham Manzoor, Dietmar Jannach:
INFACT: An Online Human Evaluation Framework for Conversational Recommendation. KaRS@RecSys 2022: 6-11 - [c141]Ahtsham Manzoor, Dietmar Jannach:
INSPIRED2: An Improved Dataset for Sociable Conversational Recommendation. KaRS@RecSys 2022: 73-80 - [c140]Sara Latifi, Dietmar Jannach:
Streaming Session-Based Recommendation: When Graph Neural Networks meet the Neighborhood. RecSys 2022: 420-426 - [c139]Sagi Eden, Amit Livne, Oren Sar Shalom, Bracha Shapira, Dietmar Jannach:
Investigating the Value of Subtitles for Improved Movie Recommendations. UMAP 2022: 99-109 - [c138]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Dietmar Jannach, Claudio Pomo:
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art. UMAP 2022: 121-131 - [r6]Dietmar Jannach, Massimo Quadrana, Paolo Cremonesi:
Session-Based Recommender Systems. Recommender Systems Handbook 2022: 301-334 - [r5]Dietmar Jannach, Markus Zanker:
Value and Impact of Recommender Systems. Recommender Systems Handbook 2022: 519-546 - [d1]Nada Ghanem, Stephan Leitner, Dietmar Jannach:
Dataset: "Balancing consumer and business value of recommender systems: A simulation-based analysis". Zenodo, 2022 - [i35]Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Dietmar Jannach, Claudio Pomo:
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art. CoRR abs/2203.01155 (2022) - [i34]Nada Ghanem, Stephan Leitner, Dietmar Jannach:
Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysis. CoRR abs/2203.05952 (2022) - [i33]Dietmar Jannach, Li Chen:
Conversational Recommendation: A Grand AI Challenge. CoRR abs/2203.09126 (2022) - [i32]Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogín, Alessandro Difonzo, Dario Zanzonelli:
A Survey of Research on Fair Recommender Systems. CoRR abs/2205.11127 (2022) - [i31]Ahtsham Manzoor, Dietmar Jannach:
INSPIRED2: An Improved Dataset for Sociable Conversational Recommendation. CoRR abs/2208.04104 (2022) - [i30]Dietmar Jannach:
Evaluating Conversational Recommender Systems. CoRR abs/2208.12061 (2022) - [i29]Ahtsham Manzoor, Dietmar Jannach:
INFACT: An Online Human Evaluation Framework for Conversational Recommendation. CoRR abs/2209.03213 (2022) - [i28]Dietmar Jannach:
Multi-Objective Recommender Systems: Survey and Challenges. CoRR abs/2210.10309 (2022) - [i27]Koby Bibas, Oren Sar Shalom, Dietmar Jannach:
Collaborative Image Understanding. CoRR abs/2210.11907 (2022) - 2021
- [j73]Dietmar Jannach, Pearl Pu, Francesco Ricci, Markus Zanker:
Recommender Systems: Past, Present, Future. AI Mag. 42(3): 3-6 (2021) - [j72]Paolo Cremonesi, Dietmar Jannach:
Progress in Recommender Systems Research: Crisis? What Crisis? AI Mag. 42(3): 43-54 (2021) - [j71]Sara Latifi, Noemi Mauro, Dietmar Jannach:
Session-aware recommendation: A surprising quest for the state-of-the-art. Inf. Sci. 573: 291-315 (2021) - [j70]Birgit Hofer, Dietmar Jannach, Patrick W. Koch, Konstantin Schekotihin, Franz Wotawa:
Product metrics for spreadsheets - A systematic review. J. Syst. Softw. 175: 110910 (2021) - [j69]Josef Bauer, Dietmar Jannach:
Improved Customer Lifetime Value Prediction With Sequence-To-Sequence Learning and Feature-Based Models. ACM Trans. Knowl. Discov. Data 15(5): 80:1-80:37 (2021) - [j68]Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, Dietmar Jannach:
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. ACM Trans. Inf. Syst. 39(2): 20:1-20:49 (2021) - [j67]Patrick W. Koch, Konstantin Schekotihin, Dietmar Jannach, Birgit Hofer, Franz Wotawa:
Metric-Based Fault Prediction for Spreadsheets. IEEE Trans. Software Eng. 47(10): 2195-2207 (2021) - [j66]Malte Ludewig, Noemi Mauro, Sara Latifi, Dietmar Jannach:
Empirical analysis of session-based recommendation algorithms. User Model. User Adapt. Interact. 31(1): 149-181 (2021) - [c137]Karin Hodnigg, Christian Macho, Martin Pinzger, Dietmar Jannach:
Comprehending Spreadsheets: Which Strategies do Users Apply? ICPC 2021: 386-390 - [c136]Patrick Rodler, Erich Teppan, Dietmar Jannach:
Randomized Problem-Relaxation Solving for Over-Constrained Schedules. KR 2021: 696-701 - [c135]Mathias Jesse, Dietmar Jannach:
Explorations in Digital Nudging for Online Food Choices. PACIS 2021: 209 - [c134]Fatih Gedikli, Anne Stockem Novo, Dietmar Jannach:
Semi-Automated Identification of News Story Chains: A New Dataset and Entity-based Labeling Method. INRA@RecSys 2021: 29-42 - [c133]Ahtsham Manzoor, Dietmar Jannach:
Generation-based vs. Retrieval-based Conversational Recommendation: A User-Centric Comparison. RecSys 2021: 515-520 - [c132]Dietmar Jannach, Mathias Jesse, Michael Jugovac, Christoph Trattner:
Exploring Multi-List User Interfaces for Similar-Item Recommendations. UMAP 2021: 224-228 - [c131]Rami Cohen, Oren Sar Shalom, Dietmar Jannach, Amihood Amir:
A Black-Box Attack Model for Visually-Aware Recommender Systems. WSDM 2021: 94-102 - [i26]Gediminas Adomavicius, Dietmar Jannach, Stephan Leitner, Jingjing Zhang:
Understanding Longitudinal Dynamics of Recommender Systems with Agent-Based Modeling and Simulation. CoRR abs/2108.11068 (2021) - [i25]Ahtsham Manzoor, Dietmar Jannach:
Towards Retrieval-based Conversational Recommendation. CoRR abs/2109.02311 (2021) - [i24]Tommaso Di Noia, Francesco M. Donini, Dietmar Jannach, Fedelucio Narducci, Claudio Pomo:
Conversational Recommendation: Theoretical Model and Complexity Analysis. CoRR abs/2111.05578 (2021) - 2020
- [j65]Dietmar Jannach, Christine Bauer:
Escaping the McNamara Fallacy: Towards more Impactful Recommender Systems Research. AI Mag. 41(4): 79-95 (2020) - [j64]Christoph Trattner, Dietmar Jannach:
Learning to recommend similar items from human judgments. User Model. User Adapt. Interact. 30(1): 1-49 (2020) - [j63]Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Augusto Pizzato:
Multistakeholder recommendation: Survey and research directions. User Model. User Adapt. Interact. 30(1): 127-158 (2020) - [j62]Iman Kamehkhosh, Geoffray Bonnin, Dietmar Jannach:
Effects of recommendations on the playlist creation behavior of users. User Model. User Adapt. Interact. 30(2): 285-322 (2020) - [j61]Dietmar Jannach, Bamshad Mobasher, Shlomo Berkovsky:
Research directions in session-based and sequential recommendation. User Model. User Adapt. Interact. 30(4): 609-616 (2020) - [c130]Maurizio Ferrari Dacrema, Federico Parroni, Paolo Cremonesi, Dietmar Jannach:
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems. CIKM 2020: 355-363 - [c129]Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach:
Methodological Issues in Recommender Systems Research (Extended Abstract). IJCAI 2020: 4706-4710 - [c128]Dietmar Jannach, Gabriel de Souza Pereira Moreira, Even Oldridge:
Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?: A Position Paper. RecSys Challenge 2020: 44-49 - [c127]Dietmar Jannach, Ahtsham Manzoor:
End-to-End Learning for Conversational Recommendation: A Long Way to Go? IntRS@RecSys 2020: 72-76 - [c126]Andres Ferraro, Dietmar Jannach, Xavier Serra:
Exploring Longitudinal Effects of Session-based Recommendations. RecSys 2020: 474-479 - [c125]Oren Sar Shalom, Dietmar Jannach, Joseph A. Konstan:
Second Workshop on the Impact of Recommender Systems at ACM RecSys '20. RecSys 2020: 630-631 - [c124]Dietmar Jannach, Surya Kallumadi, Tracy Holloway King, Weihua Luo, Shervin Malmasi:
ECOM'20: The SIGIR 2020 Workshop on eCommerce. SIGIR 2020: 2459-2460 - [e14]Toine Bogers, Marijn Koolen, Casper Petersen, Bamshad Mobasher, Alexander Tuzhilin, Oren Sar Shalom, Dietmar Jannach, Joseph A. Konstan:
Proceedings of the Workshops on Recommendation in Complex Scenarios and the Impact of Recommender Systems co-located with 14th ACM Conference on Recommender Systems (RecSys 2020), Online, September 25, 2020. CEUR Workshop Proceedings 2697, CEUR-WS.org 2020 [contents] - [i23]Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, Li Chen:
A Survey on Conversational Recommender Systems. CoRR abs/2004.00646 (2020) - [i22]Ingrid Nunes, Dietmar Jannach:
A systematic review and taxonomy of explanations in decision support and recommender systems. CoRR abs/2006.08672 (2020) - [i21]Gabriel de Souza Pereira Moreira, Dietmar Jannach, Adilson Marques da Cunha:
Hybrid Session-based News Recommendation using Recurrent Neural Networks. CoRR abs/2006.13063 (2020) - [i20]Maurizio Ferrari Dacrema, Federico Parroni, Paolo Cremonesi, Dietmar Jannach:
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems. CoRR abs/2007.11893 (2020) - [i19]Stephan Leitner, Bartosz Gula, Dietmar Jannach, Ulrike Krieg-Holz, Friederike Wall:
Infodemics: A call to action for interdisciplinary research. CoRR abs/2007.12226 (2020) - [i18]Andres Ferraro, Dietmar Jannach, Xavier Serra:
Exploring Longitudinal Effects of Session-based Recommendations. CoRR abs/2008.07226 (2020) - [i17]Rami Cohen, Oren Sar Shalom, Dietmar Jannach, Amihood Amir:
A Black-Box Attack Model for Visually-Aware Recommender Systems. CoRR abs/2011.02701 (2020) - [i16]Mathias Jesse, Dietmar Jannach:
Digital Nudging with Recommender Systems: Survey and Future Directions. CoRR abs/2011.03413 (2020) - [i15]Sara Latifi, Noemi Mauro, Dietmar Jannach:
Session-aware Recommendation: A Surprising Quest for the State-of-the-art. CoRR abs/2011.03424 (2020)
2010 – 2019
- 2019
- [j60]