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Mehdi Elahi
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2020 – today
- 2024
- [j20]Elham Motamedi
, Danial Khosh Kholgh
Predicting movies' eudaimonic and hedonic scores: A machine learning approach using metadata, audio and visual features. Inf. Process. Manag. 61(2): 103610 (2024) - [j19]Fatemeh Elahi, Mahmood Fazlali
Parallel Fractional Stochastic Gradient Descent With Adaptive Learning for Recommender Systems. IEEE Trans. Parallel Distributed Syst. 35(3): 470-483 (2024) - [c59]Mahsa Dehghani, Mehdi Elahi, Mahdi Fazeli, Ahmad Patooghy:
Advancing IoT Security Through Run-time Monitoring & Post-Execution Verification. ISVLSI 2024: 825-829 - [c58]Bilal Mahmood
Incorporating Editorial Feedback in the Evaluation of News Recommender Systems. UMAP (Adjunct Publication) 2024 - [i7]Mohamed R. Elshamy, Mehdi Elahi, Ahmad Patooghy, Abdel-Hameed A. Badawy:
Cluster-BPI: Efficient Fine-Grain Blind Power Identification for Defending against Hardware Thermal Trojans in Multicore SoCs. CoRR abs/2409.18921 (2024) - [i6]Nasim Sonboli, Sipei Li, Mehdi Elahi, Asia Biega:
The trade-off between data minimization and fairness in collaborative filtering. CoRR abs/2410.07182 (2024) - 2023
- [j18]Ahmad Patooghy
Addressing Benign and Malicious Crosstalk in Modern System-on-Chips. IEEE Access 11: 142263-142275 (2023) - [j17]Mehdi Elahi, Danial Khosh Kholgh
Hybrid recommendation by incorporating the sentiment of product reviews. Inf. Sci. 625: 738-756 (2023) - [j16]Stefano Savian
Benchmarking equivariance for Deep Learning based optical flow estimators. Signal Process. Image Commun. 111: 116892 (2023) - [c57]Ayoub El Majjodi, Alain D. Starke, Mehdi Elahi, Christoph Trattner:
The Interplay between Food Knowledge, Nudges, and Preference Elicitation Methods Determines the Evaluation of a Recipe Recommender System. IntRS@RecSys 2023: 1-18 - [c56]Andreas Lommatzsch, Benjamin Kille, Özlem Özgöbek, Mehdi Elahi, Duc-Tien Dang-Nguyen:
The Relation between Texts and Images in News: News Images in MediaEval 2023. MediaEval 2023 - [c55]Anastasiia Klimashevskaia
Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study. RecSys 2023: 1084-1089 - [c54]Anastasiia Klimashevskaia
Addressing Popularity Bias in Recommender Systems: An Exploration of Self-Supervised Learning Models. UMAP (Adjunct Publication) 2023: 7-11 - [i5]Anastasiia Klimashevskaia, Dietmar Jannach, Mehdi Elahi, Christoph Trattner:
A Survey on Popularity Bias in Recommender Systems. CoRR abs/2308.01118 (2023) - 2022
- [j15]Mehdi Elahi
Towards responsible media recommendation. AI Ethics 2(1): 103-114 (2022) - [j14]Christoph Trattner
Responsible media technology and AI: challenges and research directions. AI Ethics 2(4): 585-594 (2022) - [j13]Shahpar Yakhchi
A Convolutional Attention Network for Unifying General and Sequential Recommenders. Inf. Process. Manag. 59(1): 102755 (2022) - [c53]Anastasiia Klimashevskaia
Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches. BIAS 2022: 82-90 - [c52]Van Thanh Le
Capacity-Based Trust System in Untrusted MEC Environments. IOTSMS 2022: 1-8 - [c51]Benjamin Kille, Andreas Lommatzsch, Özlem Özgöbek, Mehdi Elahi, Duc-Tien Dang-Nguyen:
News Images in MediaEval 2022. MediaEval 2022 - [c50]Himan Abdollahpouri, Shaghayegh Sahebi, Mehdi Elahi, Masoud Mansoury, Babak Loni, Zahra Nazari, Maria Dimakopoulou:
MORS 2022: The Second Workshop on Multi-Objective Recommender Systems. RecSys 2022: 658-660 - [e2]Himan Abdollahpouri, Shaghayegh Sahebi, Mehdi Elahi, Masoud Mansoury, Babak Loni, Zahra Nazari, Maria Dimakopoulou:
Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems co-located with 16th ACM Conference on Recommender Systems (RecSys 2022), Seattle, WA, USA, 18th-23rd September 2022. CEUR Workshop Proceedings 3268, CEUR-WS.org 2022 [contents] - 2021
- [j12]Naieme Hazrati, Mehdi Elahi
Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines. Expert Syst. J. Knowl. Eng. 38(3) (2021) - [j11]Mehdi Elahi, Alain Starke
Developing and Evaluating a University Recommender System. Frontiers Artif. Intell. 4: 796268 (2021) - [j10]Mehdi Elahi
Investigating the impact of recommender systems on user-based and item-based popularity bias. Inf. Process. Manag. 58(5): 102655 (2021) - [j9]Claudio A. Ardagna
From Trustworthy Data to Trustworthy IoT: A Data Collection Methodology Based on Blockchain. ACM Trans. Cyber Phys. Syst. 5(1): 11:1-11:26 (2021) - [c49]Tord Kvifte, Mehdi Elahi, Christoph Trattner:
Hybrid Recommendation of Movies Based on Deep Content Features. ICSOC Workshops 2021: 32-45 - [c48]Benjamin Kille, Andreas Lommatzsch, Özlem Özgöbek, Mehdi Elahi, Duc-Tien Dang-Nguyen:
News Images in MediaEval 2021. MediaEval 2021 - [c47]Himan Abdollahpouri, Mehdi Elahi, Masoud Mansoury, Shaghayegh Sahebi, Zahra Nazari, Allison Chaney, Babak Loni:
MORS 2021: 1st Workshop on Multi-Objective Recommender Systems. RecSys 2021: 787-788 - [c46]Himan Abdollahpouri, Mehdi Elahi, Masoud Mansoury, Shaghayegh Sahebi, Zahra Nazari, Allison Chaney, Babak Loni:
MORS 2021 - 1st Workshop on Multi-Objective Recommender Systems. MORS@RecSys 2021 - [c45]Mehdi Elahi, Himan Abdollahpouri, Masoud Mansoury, Helma Torkamaan:
Beyond Algorithmic Fairness in Recommender Systems. UMAP (Adjunct Publication) 2021: 41-46 - [c44]Mehdi Elahi, Farshad Bakhshandegan Moghaddam, Reza Hosseini, Mohammad Hossein Rimaz, Nabil El Ioini, Marko Tkalcic, Christoph Trattner, Tammam Tillo:
Recommending Videos in Cold Start With Automatic Visual Tags. UMAP (Adjunct Publication) 2021: 54-60 - [e1]Himan Abdollahpouri, Mehdi Elahi, Masoud Mansoury, Shaghayegh Sahebi, Zahra Nazari, Allison Chaney, Babak Loni:
Proceedings of the 1st Workshop on Multi-Objective Recommender Systems (MORS 2021) co-located with 15th ACM Conference on Recommender Systems (RecSys 2021), Amsterdam, The Netherlands, September 25, 2021. CEUR Workshop Proceedings 2959, CEUR-WS.org 2021 [contents] - 2020
- [c43]Naieme Hazrati, Mehdi Elahi, Francesco Ricci:
Simulating the Impact of Recommender Systems on the Evolution of Collective Users' Choices. HT 2020: 207-212 - [c42]Mehdi Elahi, Reza Hosseini, Mohammad Hossein Rimaz, Farshad Bakhshandegan Moghaddam, Christoph Trattner:
Visually-Aware Video Recommendation in the Cold Start. HT 2020: 225-229 - [c41]Mohammad Hossein Rimaz
AudioLens: Audio-Aware Video Recommendation for Mitigating New Item Problem. ICSOC Workshops 2020: 365-378 - [c40]Mehdi Elahi, Nabil El Ioini, Anna Alexander Lambrix, Mouzhi Ge:
Exploring Personalized University Ranking and Recommendation. UMAP (Adjunct Publication) 2020: 6-10 - [c39]Ayoub El Majjodi, Mehdi Elahi, Nabil El Ioini, Christoph Trattner:
Towards Generating Personalized Country Recommendation. UMAP (Adjunct Publication) 2020: 71-76 - [p2]Stefano Savian, Mehdi Elahi, Tammam Tillo:
Optical Flow Estimation with Deep Learning, a Survey on Recent Advances. Deep Biometrics 2020: 257-287
2010 – 2019
- 2019
- [c38]Yashar Deldjoo
Movie Genome Recommender: A Novel Recommender System Based on Multimedia Content. CBMI 2019: 1-4 - [c37]Marko Tkalcic
Prediction of music pairwise preferences from facial expressions. IUI 2019: 150-159 - [c36]Ahmad Patooghy, Maral Filvan Torkaman, Mehdi Elahi:
Your hardware is all wired up!: attacking network-on-chips via crosstalk channel. NoCArc@MICRO 2019: 7:1-7:6 - [c35]Naieme Hazrati, Mehdi Elahi, Francesco Ricci:
Analysing Recommender Systems Impact on Users' Choices. ImpactRS@RecSys 2019 - [c34]Stefano Savian, Mehdi Elahi, Tammam Tillo:
Benchmarking The Imbalanced Behavior of Deep Learning Based Optical Flow Estimators. SITIS 2019: 151-158 - [c33]Farshad Bakhshandegan Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic
Predicting Movie Popularity and Ratings with Visual Features. SMAP 2019: 1-6 - [c32]Mohammad Hossein Rimaz, Mehdi Elahi, Farshad Bakhshandegan Moghaddam, Christoph Trattner, Reza Hosseini, Marko Tkalcic
Exploring the Power of Visual Features for the Recommendation of Movies. UMAP 2019: 303-308 - 2018
- [j8]Markus Schedl
Current challenges and visions in music recommender systems research. Int. J. Multim. Inf. Retr. 7(2): 95-116 (2018) - [j7]Yashar Deldjoo
Using visual features based on MPEG-7 and deep learning for movie recommendation. Int. J. Multim. Inf. Retr. 7(4): 207-219 (2018) - [c31]Dario Cavada, Mehdi Elahi, David Massimo, Stefano Maule, Elena Not
Tangible Tourism with the Internet of Things. ENTER 2018: 349-361 - [c30]Mehdi Elahi, Rosella Gennari, Alessandra Melonio, Francesco Ricci:
It Takes Two, Baby: Style and Tangibles for Recommending and Interacting with Videos. IIR 2018 - [p1]Mehdi Elahi, Matthias Braunhofer, Tural Gurbanov
User Preference Elicitation, Rating Sparsity and Cold Start. Collaborative Recommendations 2018: 253-294 - [i4]Paolo Cremonesi, Chiara Francalanci, Alessandro Poli, Roberto Pagano, Luca Mazzoni, Alberto Maggioni, Mehdi Elahi:
Social Network based Short-Term Stock Trading System. CoRR abs/1801.05295 (2018) - 2017
- [j6]Paolo Cremonesi
User interface patterns in recommendation-empowered content intensive multimedia applications. Multim. Tools Appl. 76(4): 5275-5309 (2017) - [c29]Mehdi Elahi
Exploring the Semantic Gap for Movie Recommendations. RecSys 2017: 326-330 - [c28]Marko Tkalcic
A Research Tool for User Preferences Elicitation with Facial Expressions. RecSys 2017: 353-354 - [c27]Fabian Abel, Yashar Deldjoo
RecSys Challenge 2017: Offline and Online Evaluation. RecSys 2017: 372-373 - [c26]David Massimo, Mehdi Elahi
Learning User Preferences by Observing User-Items Interactions in an IoT Augmented Space. UMAP (Adjunct Publication) 2017: 35-40 - [c25]Hanna Schäfer
User Nutrition Modelling and Recommendation: Balancing Simplicity and Complexity. UMAP (Adjunct Publication) 2017: 93-96 - [c24]David Massimo, Mehdi Elahi
Item Contents Good, User Tags Better: Empirical Evaluation of a Food Recommender System. UMAP 2017: 373-374 - [i3]Roberto Pagano, Massimo Quadrana, Mehdi Elahi
Toward Active Learning in Cross-domain Recommender Systems. CoRR abs/1701.02021 (2017) - [i2]Yashar Deldjoo, Massimo Quadrana, Mehdi Elahi, Paolo Cremonesi:
Using Mise-En-Scène Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation. CoRR abs/1704.06109 (2017) - [i1]Markus Schedl, Hamed Zamani, Ching-Wei Chen, Yashar Deldjoo, Mehdi Elahi:
Current Challenges and Visions in Music Recommender Systems Research. CoRR abs/1710.03208 (2017) - 2016
- [j5]Mehdi Elahi
A survey of active learning in collaborative filtering recommender systems. Comput. Sci. Rev. 20: 29-50 (2016) - [j4]Yashar Deldjoo
Content-Based Video Recommendation System Based on Stylistic Visual Features. J. Data Semant. 5(2): 99-113 (2016) - [j3]Ignacio Fernández-Tobías, Matthias Braunhofer, Mehdi Elahi
Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model. User Adapt. Interact. 26(2-3): 221-255 (2016) - [c23]Yashar Deldjoo
Recommending Movies Based on Mise-en-Scene Design. CHI Extended Abstracts 2016: 1540-1547 - [c22]Yashar Deldjoo
How to Combine Visual Features with Tags to Improve Movie Recommendation Accuracy? EC-Web 2016: 34-45 - [c21]Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi:
Using Visual Features and Latent Factors for Movie Recommendation. CBRecSys@RecSys 2016: 15-18 - 2015
- [c20]Paolo Cremonesi
Interaction Design Patterns in Recommender Systems. CHItaly 2015: 66-73 - [c19]Yashar Deldjoo
Toward Effective Movie Recommendations Based on Mise-en-Scène Film Styles. CHItaly 2015: 162-165 - [c18]Mona Naseri, Mehdi Elahi, Paolo Cremonesi:
Investigating the Decision Making Process of Users based on the PoliMovie Dataset. DMRS 2015: 41-44 - [c17]Yashar Deldjoo
Toward Building a Content-Based Video Recommendation System Based on Low-Level Features. EC-Web 2015: 45-56 - [c16]Mouzhi Ge, Mehdi Elahi
Using Tags and Latent Factors in a Food Recommender System. Digital Health 2015: 105-112 - [c15]Matthias Braunhofer, Mehdi Elahi, Francesco Ricci:
User Personality and the New User Problem in a Context-Aware Point of Interest Recommender System. ENTER 2015: 537-549 - [c14]Mehdi Elahi, Mouzhi Ge, Francesco Ricci, Ignacio Fernández-Tobías, Shlomo Berkovsky, David Massimo:
Interaction Design in a Mobile Food Recommender System. IntRS@RecSys 2015: 49-52 - [r1]Neil Rubens, Mehdi Elahi
Active Learning in Recommender Systems. Recommender Systems Handbook 2015: 809-846 - 2014
- [j2]Matthias Braunhofer, Mehdi Elahi, Francesco Ricci:
Techniques for cold-starting context-aware mobile recommender systems for tourism. Intelligenza Artificiale 8(2): 129-143 (2014) - [c13]Matthias Braunhofer, Mehdi Elahi, Francesco Ricci:
Usability Assessment of a Context-Aware and Personality-Based Mobile Recommender System. EC-Web 2014: 77-88 - [c12]Mehdi Elahi, Francesco Ricci, Neil Rubens:
Active Learning in Collaborative Filtering Recommender Systems. EC-Web 2014: 113-124 - [c11]Matthias Braunhofer, Mehdi Elahi, Francesco Ricci, Thomas Schievenin:
Context-Aware Points of Interest Suggestion with Dynamic Weather Data Management. ENTER 2014: 87-100 - [c10]Matthias Braunhofer, Mehdi Elahi
Context Dependent Preference Acquisition with Personality-Based Active Learning in Mobile Recommender Systems. HCI (15) 2014: 105-116 - [c9]Mehdi Elahi, Mouzhi Ge, Francesco Ricci, David Massimo, Shlomo Berkovsky:
Interactive Food Recommendation for Groups. RecSys Posters 2014 - [c8]Matthias Braunhofer, Mehdi Elahi, Francesco Ricci:
STS: A Context-Aware Mobile Recommender System for Places of Interest. UMAP Workshops 2014 - 2013
- [j1]Mehdi Elahi
Active learning strategies for rating elicitation in collaborative filtering: A system-wide perspective. ACM Trans. Intell. Syst. Technol. 5(1): 13:1-13:33 (2013) - [c7]Matthias Braunhofer, Mehdi Elahi, Mouzhi Ge, Francesco Ricci, Thomas Schievenin:
STS: Design of Weather-Aware Mobile Recommender Systems in Tourism. AI*HCI@AI*IA 2013 - [c6]Mehdi Elahi
Personality-Based Active Learning for Collaborative Filtering Recommender Systems. AI*IA 2013: 360-371 - 2012
- [c5]Mehdi Elahi
Adapting to Natural Rating Acquisition with Combined Active Learning Strategies. ISMIS 2012: 254-263 - 2011
- [c4]Mehdi Elahi
Rating Elicitation Strategies for Collaborative Filtering. EC-Web 2011: 160-171 - [c3]Mehdi Elahi
Adaptive Active Learning in Recommender Systems. UMAP 2011: 414-417 - 2010
- [c2]Mehdi Elahi
Context-aware intelligent recommender system. IUI 2010: 407-408
2000 – 2009
- 2009
- [c1]Magnus Jändel, Mehdi Elahi
Tribal taste: mobile multiagent recommender system. IUI 2009: 489-490
Coauthor Index
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last updated on 2024-11-19 21:42 CET by the dblp team
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