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Yashar Deldjoo
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2020 – today
- 2024
- [j24]Yashar Deldjoo
, Markus Schedl, Peter Knees:
Content-driven music recommendation: Evolution, state of the art, and challenges. Comput. Sci. Rev. 51: 100618 (2024) - [j23]Yashar Deldjoo
A Review of Modern Fashion Recommender Systems. ACM Comput. Surv. 56(4): 87:1-87:37 (2024) - [j22]Giovanni Maria Biancofiore
Interactive Question Answering Systems: Literature Review. ACM Comput. Surv. 56(9): 239:1-239:38 (2024) - [j21]Ali Tourani, Hossein A. Rahmani
CAPRI: Context-aware point-of-interest recommendation framework. Softw. Impacts 19: 100606 (2024) - [j20]Hossein A. Rahmani
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems. Trans. Recomm. Syst. 2(3): 19:1-19:24 (2024) - [j19]Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogín
Fairness in recommender systems: research landscape and future directions. User Model. User Adapt. Interact. 34(1): 59-108 (2024) - [c62]Felice Antonio Merra, Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio:
Empowering Shilling Attacks with Katz and Exclusivity-based Relatedness. KaRS@RecSys 2024: 21-42 - [c61]Yashar Deldjoo
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys). KDD 2024: 6448-6458 - [c60]Yashar Deldjoo
The 1st International Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommendation (ROEGEN). RecSys 2024: 1250-1252 - [i38]Yashar Deldjoo:
Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency. CoRR abs/2401.10545 (2024) - [i37]Hossein A. Rahmani, Mohammadmehdi Naghiaei, Yashar Deldjoo:
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems. CoRR abs/2402.00485 (2024) - [i36]Yashar Deldjoo, Tommaso Di Noia:
CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System. CoRR abs/2403.05668 (2024) - [i35]Yashar Deldjoo, Zhankui He, Julian J. McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, Silvia Milano:
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys). CoRR abs/2404.00579 (2024) - [i34]Lemei Zhang, Peng Liu, Yashar Deldjoo, Yong Zheng, Jon Atle Gulla:
Understanding Language Modeling Paradigm Adaptations in Recommender Systems: Lessons Learned and Open Challenges. CoRR abs/2404.03788 (2024) - [i33]Yashar Deldjoo:
FairEvalLLM. A Comprehensive Framework for Benchmarking Fairness in Large Language Model Recommender Systems. CoRR abs/2405.02219 (2024) - [i32]Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio:
XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced In-Context Learning in Healthcare. CoRR abs/2405.06270 (2024) - [i31]Leila Tavakoli, Giovanni Castiglia, Federica Calò, Yashar Deldjoo, Hamed Zamani, Johanne R. Trippas:
Understanding Modality Preferences in Search Clarification. CoRR abs/2406.19546 (2024) - [i30]Anton Korikov, Scott Sanner, Yashar Deldjoo, Zhankui He, Julian J. McAuley, Arnau Ramisa, René Vidal, Mahesh Sathiamoorthy, Atoosa Kasirzadeh, Silvia Milano, Francesco Ricci:
Large Language Model Driven Recommendation. CoRR abs/2408.10946 (2024) - [i29]Arnau Ramisa, René Vidal, Yashar Deldjoo, Zhankui He, Julian J. McAuley, Anton Korikov, Scott Sanner, Mahesh Sathiamoorthy, Atoosa Kasrizadeh, Silvia Milano, Francesco Ricci:
Multi-modal Generative Models in Recommendation System. CoRR abs/2409.10993 (2024) - [i28]Yashar Deldjoo, Zhankui He, Julian J. McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasrizadeh, Silvia Milano, Francesco Ricci:
Recommendation with Generative Models. CoRR abs/2409.15173 (2024) - 2023
- [j18]Enrique Amigó, Yashar Deldjoo, Stefano Mizzaro
A unifying and general account of fairness measurement in recommender systems. Inf. Process. Manag. 60(1): 103115 (2023) - [c59]Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia:
ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPT. ECAI Workshops (1) 2023: 382-397 - [c58]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo
Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering. ECIR (1) 2023: 33-48 - [c57]Dario Di Palma, Vito Walter Anelli, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo, Yashar Deldjoo, Tommaso Di Noia:
Examining Fairness in Graph-Based Collaborative Filtering: A Consumer and Producer Perspective. IIR 2023: 79-84 - [c56]Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia, Carmelo Ardito, Eugenio Di Sciascio:
Smart Electrical Grids Under the Lens of Adversarial Attacks. Ital-IA 2023: 616-621 - [c55]Marta Moscati, Yashar Deldjoo, Giulio Davide Carparelli, Markus Schedl:
Multiobjective Hyperparameter Optimization of Recommender Systems. Perspectives@RecSys 2023 - [c54]Oleg Lesota
Computational Versus Perceived Popularity Miscalibration in Recommender Systems. SIGIR 2023: 1889-1893 - [i27]Carmelo Ardito, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Fatemeh Nazary, Giovanni Servedio:
Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids. CoRR abs/2303.18136 (2023) - [i26]Ali Tourani, Hossein A. Rahmani, Mohammadmehdi Naghiaei, Yashar Deldjoo:
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework. CoRR abs/2306.11395 (2023) - [i25]Yashar Deldjoo:
Fairness of ChatGPT and the Role Of Explainable-Guided Prompts. CoRR abs/2307.11761 (2023) - [i24]Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia:
ChatGPT-HealthPrompt. Harnessing the Power of XAI in Prompt-Based Healthcare Decision Support using ChatGPT. CoRR abs/2308.09731 (2023) - [i23]Giovanni Pellegrini, Vittorio Maria Faraco, Yashar Deldjoo:
Fairness for All: Investigating Harms to Within-Group Individuals in Producer Fairness Re-ranking Optimization - A Reproducibility Study. CoRR abs/2309.09277 (2023) - 2022
- [j17]Yashar Deldjoo
A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks. ACM Comput. Surv. 54(2): 35:1-35:38 (2022) - [j16]Hossein A. Rahmani
The role of context fusion on accuracy, beyond-accuracy, and fairness of point-of-interest recommendation systems. Expert Syst. Appl. 205: 117700 (2022) - [j15]Carmelo Ardito
Visual inspection of fault type and zone prediction in electrical grids using interpretable spectrogram-based CNN modeling. Expert Syst. Appl. 210: 118368 (2022) - [j14]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara
User-controlled federated matrix factorization for recommender systems. J. Intell. Inf. Syst. 58(2): 287-309 (2022) - [j13]Mohammadmehdi Naghiaei
PyCPFair: A framework for consumer and producer fairness in recommender systems. Softw. Impacts 13: 100382 (2022) - [c53]Hossein A. Rahmani
The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation. BIAS 2022: 56-68 - [c52]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo:
Reshaping Graph Recommendation with Edge Graph Collaborative Filtering and Customer Reviews. DL4SR@CIKM 2022 - [c51]Carmelo Ardito, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Fatemeh Nazary
IEEE13-AdvAttack A Novel Dataset for Benchmarking the Power of Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grid. CIKM 2022: 3817-3821 - [c50]Marta Moscati, Emilia Parada-Cabaleiro, Yashar Deldjoo, Eva Zangerle, Markus Schedl:
Music4All-Onion - A Large-Scale Multi-faceted Content-Centric Music Recommendation Dataset. CIKM 2022: 4339-4343 - [c49]Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra:
Leveraging Content-Style Item Representation for Visual Recommendation. ECIR (2) 2022: 84-92 - [c48]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo:
How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering. MORS@RecSys 2022 - [c47]Giovanni Castiglia, Ayoub El Majjodi, Federica Calò, Yashar Deldjoo, Fedelucio Narducci, Alain Starke, Christoph Trattner:
Nudging Towards Health in a Conversational Food Recommender System Using Multi-Modal Interactions and Nutrition Labels. KaRS@RecSys 2022: 29-35 - [c46]Hossein A. Rahmani, Mohammadmehdi Naghiaei, Ali Tourani
Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation. RecSys 2022: 598-603 - [c45]Mohammadmehdi Naghiaei, Hossein A. Rahmani, Yashar Deldjoo:
CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems. SIGIR 2022: 770-779 - [r2]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra:
Adversarial Recommender Systems: Attack, Defense, and Advances. Recommender Systems Handbook 2022: 335-379 - [r1]Yashar Deldjoo, Markus Schedl, Balázs Hidasi
Multimedia Recommender Systems: Algorithms and Challenges. Recommender Systems Handbook 2022: 973-1014 - [d1]Marta Moscati
Music4All-Onion. Zenodo, 2022 - [i22]Yashar Deldjoo, Fatemeh Nazary, Arnau Ramisa, Julian J. McAuley, Giovanni Pellegrini, Alejandro Bellogín
A Review of Modern Fashion Recommender Systems. CoRR abs/2202.02757 (2022) - [i21]Hossein A. Rahmani, Yashar Deldjoo, Ali Tourani, Mohammadmehdi Naghiaei:
The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation. CoRR abs/2202.13307 (2022) - [i20]Mohammadmehdi Naghiaei, Hossein A. Rahmani, Yashar Deldjoo:
CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems. CoRR abs/2204.08085 (2022) - [i19]Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogín, Alessandro Difonzo, Dario Zanzonelli:
A Survey of Research on Fair Recommender Systems. CoRR abs/2205.11127 (2022) - [i18]Hossein A. Rahmani, Mohammadmehdi Naghiaei, Ali Tourani
Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation. CoRR abs/2207.11609 (2022) - [i17]Giovanni Maria Biancofiore
Interactive Question Answering Systems: Literature Review. CoRR abs/2209.01621 (2022) - 2021
- [j12]Yashar Deldjoo
Recommender Systems Leveraging Multimedia Content. ACM Comput. Surv. 53(5): 106:1-106:38 (2021) - [j11]Yashar Deldjoo
Explaining recommender systems fairness and accuracy through the lens of data characteristics. Inf. Process. Manag. 58(5): 102662 (2021) - [j10]Jens Adamczak, Yashar Deldjoo
Session-based Hotel Recommendations Dataset: As part of the ACM Recommender System Challenge 2019. ACM Trans. Intell. Syst. Technol. 12(1): 1:1-1:20 (2021) - [j9]Yashar Deldjoo
A flexible framework for evaluating user and item fairness in recommender systems. User Model. User Adapt. Interact. 31(3): 457-511 (2021) - [c44]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra:
A Formal Analysis of Recommendation Quality of Adversarially-trained Recommenders. CIKM 2021: 2852-2856 - [c43]Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra:
A Study on the Relative Importance of Convolutional Neural Networks in Visually-Aware Recommender Systems. CVPR Workshops 2021: 3961-3967 - [c42]Vito Walter Anelli, Yashar Deldjoo
FedeRank: User Controlled Feedback with Federated Recommender Systems. ECIR (1) 2021: 32-47 - [c41]Vito Walter Anelli, Alejandro Bellogín, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra:
MSAP: Multi-Step Adversarial Perturbations on Recommender Systems Embeddings. FLAIRS 2021 - [c40]Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Felice Antonio Merra:
A Regression Framework to Interpret the Robustness of Recommender Systems Against Shilling Attacks. IIR 2021 - [c39]Carmelo Ardito
ISCADA: Towards a Framework for Interpretable Fault Prediction in Smart Electrical Grids. INTERACT (5) 2021: 270-274 - [c38]Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Gaetano Pernisco, Vito Renò, Ettore Stella
Towards Improving Car Point-Cloud Tracking Via Detection Updates. IPMV 2021: 30-34 - [c37]Carmelo Ardito, Yashar Deldjoo, Eugenio Di Sciascio, Fatemeh Nazary:
Revisiting Security Threat on Smart Grids: Accurate and Interpretable Fault Location Prediction and Type Classification. ITASEC 2021: 523-533 - [c36]Vito Walter Anelli, Luca Belli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara
Pursuing Privacy in Recommender Systems: the View of Users and Researchers from Regulations to Applications. RecSys 2021: 838-841 - [c35]Vito Walter Anelli, Yashar Deldjoo
How to put users in control of their data in federated top-N recommendation with learning to rank. SAC 2021: 1359-1362 - [c34]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, Fedelucio Narducci:
Federated Recommender Systems with Learning to Rank. SEBD 2021: 71-82 - [c33]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra:
A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images. SIGIR 2021: 1094-1103 - [c32]Yashar Deldjoo, Johanne R. Trippas
Towards Multi-Modal Conversational Information Seeking. SIGIR 2021: 1577-1587 - [i16]Yashar Deldjoo, Markus Schedl, Peter Knees:
Content-driven Music Recommendation: Evolution, State of the Art, and Challenges. CoRR abs/2107.11803 (2021) - [i15]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra:
Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality. CoRR abs/2107.13876 (2021) - [i14]Alejandro Bellogín
Simulations for novel problems in recommendation: analyzing misinformation and data characteristics. CoRR abs/2110.04037 (2021) - 2020
- [j8]Vito Walter Anelli, Yashar Deldjoo
Prioritized multi-criteria federated learning. Intelligenza Artificiale 14(2): 183-200 (2020) - [c31]Carmelo Ardito, Yashar Deldjoo, Eugenio Di Sciascio, Fatemeh Nazary:
Interacting with Features: Visual Inspection of Black-box Fault Type Classification Systems in Electrical Grids. XAI.it@AI*IA 2020: 135-141 - [c30]Vito Walter Anelli, Yashar Deldjoo
Deep Learning-Based Adaptive Image Compression System for a Real-World Scenario. EAIS 2020: 1-8 - [c29]Vito Walter Anelli
SAShA: Semantic-Aware Shilling Attacks on Recommender Systems Exploiting Knowledge Graphs. ESWC 2020: 307-323 - [c28]Vito Walter Anelli, Yashar Deldjoo
Adversarial Learning for Recommendation: Applications for Security and Generative Tasks - Concept to Code. RecSys 2020: 738-741 - [c27]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra, Giuseppe Acciani, Eugenio Di Sciascio:
Knowledge-enhanced Shilling Attacks for Recommendation. SEBD 2020: 310-317 - [c26]Yashar Deldjoo
How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models. SIGIR 2020: 951-960 - [c25]Yashar Deldjoo
Adversarial Machine Learning in Recommender Systems (AML-RecSys). WSDM 2020: 869-872 - [i13]Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra:
Adversarial Machine Learning in Recommender Systems: State of the art and Challenges. CoRR abs/2005.10322 (2020) - [i12]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara:
Prioritized Multi-Criteria Federated Learning. CoRR abs/2007.08893 (2020) - [i11]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, Fedelucio Narducci:
How to Put Users in Control of their Data via Federated Pair-Wise Recommendation. CoRR abs/2008.07192 (2020) - [i10]Vito Walter Anelli, Alejandro Bellogín
Multi-Step Adversarial Perturbations on Recommender Systems Embeddings. CoRR abs/2010.01329 (2020) - [i9]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, Fedelucio Narducci:
FedeRank: User Controlled Feedback with Federated Recommender Systems. CoRR abs/2012.11328 (2020)
2010 – 2019
- 2019
- [j7]Vahid Jalili
Next Generation Indexing for Genomic Intervals. IEEE Trans. Knowl. Data Eng. 31(10): 2008-2021 (2019) - [j6]Yashar Deldjoo
Movie genome: alleviating new item cold start in movie recommendation. User Model. User Adapt. Interact. 29(2): 291-343 (2019) - [c24]Vito Walter Anelli
Towards Effective Device-Aware Federated Learning. AI*IA 2019: 477-491 - [c23]Yashar Deldjoo
Retrieving Relevant and Diverse Movie Clips Using the MFVCD-7K Multifaceted Video Clip Dataset. CBMI 2019: 1-4 - [c22]Yashar Deldjoo
Movie Genome Recommender: A Novel Recommender System Based on Multimedia Content. CBMI 2019: 1-4 - [c21]Yashar Deldjoo, Benny Kille, Markus Schedl, Andreas Lommatzsch, Jialie Shen:
The 2019 Multimedia for Recommender System Task: MovieREC and NewsREEL at MediaEval. MediaEval 2019 - [c20]Hossein A. Rahmani, Yashar Deldjoo, Markus Schedl:
A Regression Approach to Movie Rating Prediction Using Multimedia Content and Metadata. MediaEval 2019 - [c19]Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema, Markus Schedl, Paolo Cremonesi:
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features. IntRS@RecSys 2019: 72-76 - [c18]Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogín Kouki, Tommaso Di Noia:
Recommender Systems Fairness Evaluation via Generalized Cross Entropy. RMSE@RecSys 2019 - [c17]Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra:
Assessing the Impact of a User-Item Collaborative Attack on Class of Users. ImpactRS@RecSys 2019 - [c16]Peter Knees, Yashar Deldjoo
RecSys challenge 2019: session-based hotel recommendations. RecSys 2019: 570-571 - [e1]Peter Knees, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Jens Adamczak, Gerard Paul Leyson, Philipp Monreal:
Proceedings of the Workshop on ACM Recommender Systems Challenge, Copenhagen, Denmark, September 2019. ACM 2019, ISBN 978-1-4503-7667-9 [contents] - [i8]Jens Adamczak, Gerard Paul Leyson, Peter Knees, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Julia Neidhardt, Wolfgang Wörndl, Philipp Monreal:
Session-Based Hotel Recommendations: Challenges and Future Directions. CoRR abs/1908.00071 (2019) - [i7]Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogín
Recommender Systems Fairness Evaluation via Generalized Cross Entropy. CoRR abs/1908.06708 (2019) - [i6]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara
Towards Effective Device-Aware Federated Learning. CoRR abs/1908.07420 (2019) - [i5]Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra:
Assessing the Impact of a User-Item Collaborative Attack on Class of Users. CoRR abs/1908.07968 (2019) - [i4]Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema, Markus Schedl, Paolo Cremonesi:
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features. CoRR abs/1908.11055 (2019) - 2018
- [b1]Yashar Deldjoo:
Video recommendation by exploiting the multimedia content. Polytechnic University of Milan, Italy, 2018 - [j5]Markus Schedl
Current challenges and visions in music recommender systems research. Int. J. Multim. Inf. Retr. 7(2): 95-116 (2018) - [j4]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) - [c15]Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, Gabriella Pasi:
Content-Based Multimedia Recommendation Systems: Definition and Application Domains. IIR 2018 - [c14]Yashar Deldjoo, Mihai Gabriel Constantin, Athanasios Dritsas, Bogdan Ionescu, Markus Schedl:
The MediaEval 2018 Movie Recommendation Task: Recommending Movies Using Content. MediaEval 2018 - [c13]Fatemeh Nazary, Yashar Deldjoo:
Movie Rating Prediction Using Multimedia Content and Modeling as a Classification Problem. MediaEval 2018 - [c12]Yashar Deldjoo
MMTF-14K: a multifaceted movie trailer feature dataset for recommendation and retrieval. MMSys 2018: 450-455 - [c11]Yashar Deldjoo
Audio-visual encoding of multimedia content for enhancing movie recommendations. RecSys 2018: 455-459 - [c10]Yashar Deldjoo
Multimedia recommender systems. RecSys 2018: 537-538 - 2017
- [c9]Yashar Deldjoo
The effect of different video summarization models on the quality of video recommendation based on low-level visual features. CBMI 2017: 20:1-20:6 - [c8]Yashar Deldjoo, Cristina Frà, Massimo Valla, Paolo Cremonesi:
Letting Users Assist What to Watch: An Interactive Query-by-Example Movie Recommendation System. IIR 2017: 63-66 - [c7]Mehdi Elahi
Exploring the Semantic Gap for Movie Recommendations. RecSys 2017: 326-330 - [c6]Fabian Abel, Yashar Deldjoo
RecSys Challenge 2017: Offline and Online Evaluation. RecSys 2017: 372-373 - [i3]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) - [i2]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
- [j3]Yashar Deldjoo
A low-cost infrared-optical head tracking solution for virtual 3D audio environment using the Nintendo Wii-remote. Entertain. Comput. 12: 9-27 (2016) - [j2]Yashar Deldjoo
Content-Based Video Recommendation System Based on Stylistic Visual Features. J. Data Semant. 5(2): 99-113 (2016) - [c5]Yashar Deldjoo
Recommending Movies Based on Mise-en-Scene Design. CHI Extended Abstracts 2016: 1540-1547 - [c4]Yashar Deldjoo
How to Combine Visual Features with Tags to Improve Movie Recommendation Accuracy? EC-Web 2016: 34-45 - [c3]Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi:
Using Visual Features and Latent Factors for Movie Recommendation. CBRecSys@RecSys 2016: 15-18 - [i1]Yashar Deldjoo, Shengping Zhang, Bahman Zanj, Paolo Cremonesi, Matteo Matteucci:
Sparse vs. Non-sparse: Which One Is Better for Practical Visual Tracking? CoRR abs/1608.00168 (2016) - 2015
- [j1]Saman Tahouri, Reza Ebrahimi Atani
Application of connected dominating sets in wildfire detection based on wireless sensor networks. Int. J. Inf. Technol. Commun. Convergence 3(2): 139-160 (2015) - [c2]Yashar Deldjoo
Toward Effective Movie Recommendations Based on Mise-en-Scène Film Styles. CHItaly 2015: 162-165 - [c1]Yashar Deldjoo
Toward Building a Content-Based Video Recommendation System Based on Low-Level Features. EC-Web 2015: 45-56
Coauthor Index
Alejandro Bellogín
aka: Alejandro Bellogín Kouki
aka: Alejandro Bellogín Kouki
[j23] [j22] [c62] [i36] [i32] [c59] [c58] [c57] [c56] [i27] [i24] [j17] [j16] [j15] [j14] [c52] [c51] [c49] [c48] [r2] [i22] [i17] [j11] [j9] [c44] [c43] [c42] [c41] [c40] [c38] [c36] [c35] [c34] [c33] [i15] [j8] [c30] [c29] [c28] [c27] [c26] [c25] [i13] [i12] [i11] [i10] [i9] [c24] [c18] [c17] [i7] [i6] [i5]
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