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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
- 2023
[j86]Dietmar Jannach
:
Evaluating conversational recommender systems. Artif. Intell. Rev. 56(3): 2365-2400 (2023)- [j85]Adil Mukhtar, Birgit Hofer, Dietmar Jannach, Franz Wotawa:
Explaining software fault predictions to spreadsheet users. J. Syst. Softw. 201: 111676 (2023) - [j84]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) - [j83]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) - [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 - [i43]Pablo Castells, Dietmar Jannach:
Recommender Systems: A Primer. CoRR abs/2302.02579 (2023) - [i42]Koby Bibas, Oren Sar Shalom, Dietmar Jannach:
Semi-supervised Adversarial Learning for Complementary Item Recommendation. CoRR abs/2303.05812 (2023) - [i41]Siyu Wang, Xiaocong Chen, Dietmar Jannach, Lina Yao:
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning. CoRR abs/2304.07920 (2023) - [i40]Anastasiia Klimashevskaia, Dietmar Jannach, Mehdi Elahi, Christoph Trattner:
A Survey on Popularity Bias in Recommender Systems. CoRR abs/2308.01118 (2023) - [i39]Zehui Wang, Wolfram Höpken, Dietmar Jannach:
A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data. CoRR abs/2308.07426 (2023) - [i38]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) - [i37]Alvise De Biasio, Nicolò Navarin, Dietmar Jannach:
Economic Recommender Systems - A Systematic Review. CoRR abs/2308.11998 (2023) - [i36]Jesse Harte, Wouter Zorgdrager, Panos Louridas, Asterios Katsifodimos, Dietmar Jannach, Marios Fragkoulis:
Leveraging Large Language Models for Sequential Recommendation. CoRR abs/2309.09261 (2023) - 2022
- [j82]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) - [j81]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) - [j80]Dietmar Jannach, Pearl Pu, Francesco Ricci, Markus Zanker:
Recommender Systems: Trends and Frontiers. AI Mag. 43(2): 145-150 (2022) - [j79]Dietmar Jannach, Li Chen:
Conversational Recommendation: A Grand AI Challenge. AI Mag. 43(2): 151-163 (2022) - [j78]Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, Li Chen:
A Survey on Conversational Recommender Systems. ACM Comput. Surv. 54(5): 105:1-105:36 (2022) - [j77]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) - [j76]Ahtsham Manzoor, Dietmar Jannach:
Towards retrieval-based conversational recommendation. Inf. Syst. 109: 102083 (2022) - [j75]Sara Latifi, Dietmar Jannach, Andrés Ferraro:
Sequential recommendation: A study on transformers, nearest neighbors and sampled metrics. Inf. Sci. 609: 660-678 (2022) - [j74]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) - [j73]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 - [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
- [j72]Dietmar Jannach, Pearl Pu, Francesco Ricci, Markus Zanker:
Recommender Systems: Past, Present, Future. AI Mag. 42(3): 3-6 (2021) - [j71]Paolo Cremonesi, Dietmar Jannach:
Progress in Recommender Systems Research: Crisis? What Crisis? AI Mag. 42(3): 43-54 (2021) - [j70]Sara Latifi, Noemi Mauro, Dietmar Jannach:
Session-aware recommendation: A surprising quest for the state-of-the-art. Inf. Sci. 573: 291-315 (2021) - [j69]Birgit Hofer, Dietmar Jannach, Patrick W. Koch, Konstantin Schekotihin, Franz Wotawa:
Product metrics for spreadsheets - A systematic review. J. Syst. Softw. 175: 110910 (2021) - [j68]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) - [j67]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) - [j66]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) - [j65]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
- [j64]Dietmar Jannach, Christine Bauer:
Escaping the McNamara Fallacy: Towards more Impactful Recommender Systems Research. AI Mag. 41(4): 79-95 (2020) - [j63]Christoph Trattner, Dietmar Jannach:
Learning to recommend similar items from human judgments. User Model. User Adapt. Interact. 30(1): 1-49 (2020) - [j62]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) - [j61]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) - [j60]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
- [j59]Gabriel de Souza Pereira Moreira, Dietmar Jannach, Adilson Marques da Cunha:
Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks. IEEE Access 7: 169185-169203 (2019) - [j58]Dietmar Jannach, Thomas Schmitz, Birgit Hofer, Konstantin Schekotihin, Patrick W. Koch, Franz Wotawa:
Fragment-based spreadsheet debugging. Autom. Softw. Eng. 26(1): 203-239 (2019) - [j57]Markus Zanker, Laurens Rook, Dietmar Jannach:
Measuring the impact of online personalisation: Past, present and future. Int. J. Hum. Comput. Stud. 131: 160-168 (2019) - [j56]Patrick Rodler, Dietmar Jannach, Konstantin Schekotihin, Philipp Fleiss:
Are query-based ontology debuggers really helping knowledge engineers? Knowl. Based Syst. 179: 92-107 (2019) - [j55]Dietmar Jannach, Michael Jugovac:
Measuring the Business Value of Recommender Systems. ACM Trans. Manag. Inf. Syst. 10(4): 16:1-16:23 (2019) - [j54]Pasquale Lops, Dietmar Jannach, Cataldo Musto, Toine Bogers, Marijn Koolen:
Trends in content-based recommendation - Preface to the special issue on Recommender systems based on rich item descriptions. User Model. User Adapt. Interact. 29(2): 239-249 (2019) - [c123]Dietmar Jannach, Michael Jugovac, Ingrid Nunes:
Explanations and User Control in Recommender Systems. ABIS@HT 2019: 31 - [c122]Dietmar Jannach, Oren Sar Shalom, Joseph A. Konstan:
Towards More Impactful Recommender Systems Research. ImpactRS@RecSys 2019 - [c121]Malte Ludewig, Dietmar Jannach:
Learning to rank hotels for search and recommendation from session-based interaction logs and meta data. RecSys Challenge 2019: 5:1-5:5 - [c120]Gabriel de Souza Pereira Moreira, Dietmar Jannach, Adilson Marques da Cunha:
On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems. INRA@RecSys 2019: 18-23 - [c119]Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach:
Are we really making much progress? A worrying analysis of recent neural recommendation approaches. RecSys 2019: 101-109 - [c118]Malte Ludewig, Noemi Mauro, Sara Latifi, Dietmar Jannach:
Performance comparison of neural and non-neural approaches to session-based recommendation. RecSys 2019: 462-466 - [c117]Malte Ludewig, Dietmar Jannach:
User-centric evaluation of session-based recommendations for an automated radio station. RecSys 2019: 516-520 - [c116]Oren Sar Shalom, Dietmar Jannach, Ido Guy:
First workshop on the impact of recommender systems at ACM RecSys 2019. RecSys 2019: 556-557 - [c115]Dietmar Jannach, Olga C. Santos:
Session details: ACM UMAP 2019 Main Track. UMAP 2019 - [c114]Massimo Quadrana, Dietmar Jannach, Paolo Cremonesi:
Tutorial: Sequence-Aware Recommender Systems. WWW (Companion Volume) 2019: 1316 - [e13]Oren Sar Shalom, Dietmar Jannach, Ido Guy:
Proceedings of the 1st Workshop on the Impact of Recommender Systems co-located with 13th ACM Conference on Recommender Systems, ImpactRS@RecSys 2019), Copenhagen, Denmark, September 19, 2019. CEUR Workshop Proceedings 2462, CEUR-WS.org 2019 [contents] - [e12]George Angelos Papadopoulos, George Samaras, Stephan Weibelzahl, Dietmar Jannach, Olga C. Santos:
Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019, Larnaca, Cyprus, June 9-12, 2019. ACM 2019, ISBN 978-1-4503-6021-0 [contents] - [e11]George Angelos Papadopoulos, George Samaras, Stephan Weibelzahl, Dietmar Jannach, Olga C. Santos:
Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, UMAP 2019, Larnaca, Cyprus, June 09-12, 2019. ACM 2019, ISBN 978-1-4503-6711-0 [contents] - [i14]Patrick Rodler, Dietmar Jannach, Konstantin Schekotihin, Philipp Fleiss:
Are Query-Based Ontology Debuggers Really Helping Knowledge Engineers? CoRR abs/1904.01484 (2019) - [i13]Gabriel de Souza Pereira Moreira, Dietmar Jannach, Adilson Marques da Cunha:
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks. CoRR abs/1904.10367 (2019) - [i12]Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Augusto Pizzato:
Beyond Personalization: Research Directions in Multistakeholder Recommendation. CoRR abs/1905.01986 (2019) - [i11]Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach:
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. CoRR abs/1907.06902 (2019) - [i10]Gabriel de Souza Pereira Moreira, Dietmar Jannach, Adilson Marques da Cunha:
On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems. CoRR abs/1907.07629 (2019) - [i9]Dietmar Jannach, Michael Jugovac:
Measuring the Business Value of Recommender Systems. CoRR abs/1908.08328 (2019) - [i8]Malte Ludewig, Noemi Mauro, Sara Latifi, Dietmar Jannach:
Empirical Analysis of Session-Based Recommendation Algorithms. CoRR abs/1910.12781 (2019) - [i7]Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, Dietmar Jannach:
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. CoRR abs/1911.07698 (2019) - 2018
- [j53]