About me
I am an assistant professor at Polytechnic University of Bari (Politecnico di Bari), Italy affiliated with the Information Systems Laboratory (SisInf Lab), working with Prof. Tommaso di Noia, the head of the group. My main areas of research span a range of topics focusing mainly on recommender systems, multimedia processing, and machine learning. Currently, I am working with a group of Ph.D. students at SisInf Lab on several topics around secuirty, privacy and fairness of recommender systems (see current research topics).
I defended my Ph.D. dissertation, with "con lode" (highest distinction in Italy) in July 2018! I have received a B.Sc. degree in Electrical Engineering from the University of Guilan, a B.A. degree in English, Linguistics from the University of Gothenburg, an M.Sc. degree in Electrical Engineering from the Chalmers University of Technology, Sweden and a Ph.D. degree in Computer Science from Polytechnic University of Milan (Politecnico di Milano), Italy. During my Ph.D., I was a visiting researcher at Johannes Kepler University (JKU) Linz, Austria for 6 months.
I have published more than 40 papers at top-tier conferences and journals, including ACM Computing Surveys, TKDE, TIST, UMUAI, SIGIR, RecSys, WSDM, CHI, MMSys, AIIA and hold a U.S. Patent. I have been involved in organizing ACM Recommender System Challenges as part of the RecSys conference through the years 2017-2020 (as a chair or advisor). I have also organized two tasks at the MediaEval benchmarking event in 2018 and 2019.
NEW: Our new work "A Flexible Framework for Evaluating User and Item Fairness in Recommender Systems" accepted to UMUAI, the main journal for personalization research. It extends our RMSE@RecSys'19 paper in several dimensions: large-scale experiment, theoretical support and extensive literature review! PrePrint is now availible https://bit.ly/33KSYYr
NEW: Our comprehensive literature review "Recommender Systems Leveraging Multimedia Content" accepted to ACM Computing Surveys has now a prePrint version online. Domains studied in this work include: fashion, audio (music, sounds), video (movie, user-generated videos), news, social media, food, e-commerce, tourism, among others: https://t.co/Ek819d0Af1?amp=1
NEW: Our recent extensive literature review "Adversarial Machine Learning in Recommender Systems: State of the art and Challenges" has now a prePrint version available online (under review). https://bit.ly/2A3TlAo
NEW: A new dataset "Session-Based Hotel Recommendations Dataset" co-authered with Trivago has been accepted to ACM TIST. The link to dataset is provided within the paper. https://bit.ly/2OcguVm
news
July-2020 Our paper on recommender systems fairness evaluation is accepted to UMUAI!July-2020 Our tutorial on adversarial machine learning in RecSys is accepted to RecSys'20.July-2020 Our paper session-based hotel recommendations dataset done in collobration with Trivago is accepted at ACM TIST.July-2020 Our paper federated learning is accepted to Italian journal of Intelligenza Artificiale.June-2020 Our comprehensive literature review about recommender systems leveraging multimedia content is accepted to ACM Computing Surveys.May-2020 Our literature review on adversarial machine learning in recommender systems has a preprint version.Jan-2020 One full paper accepted to SIGIR 2020.Dec-2019 One full paper accepted to ESWC 2020.Oct-2019 Our tutorial on adversarial machine learning in RecSys accepted to WSDM'20.Oct-2019 A US patent accepted.July-2019 Our paper on federated learning accepted to AIIA'19.Jan-2019 Our paper accepted to UMUAI.
Current Research Topics
- Recommender systems and personalization (RecSys)
- Multimedia Recommender Systems (MM-RecSys)
- Fairness in Recommender Systems (Fair-RecSys)
- Advarsarial Machine Learning in Recommender Systems (Security-RecSys)
- Federated and Privacy-Aware Recommender Systems (Privacy-RecSys)
Seleted Publications
- (MM-RecSys) Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, Gabriella Pasi. "Recommender Systems Leveraging Multimedia Content.", ACM Computing Surveys 2020.
- (Fair-RecSys) Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogin, Tommaso Di Noia. "A Flexible Framework for Evaluating User and Item Fairness in Recommender Systems", UMUAI 2020.
- (Security-RecSys) Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra. "How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models", SIGIR 2020.
- (MM-RecSys) Yashar Deldjoo, Maurizio Ferrari Dacrema, Mihai Gabriel Constantin, Hamid Eghbal-Zadeh, Stefano Cereda, Markus Schedl, Bogdan Ionescu, Paolo Cremonesi. "Movie Genome: Alleviating New Item Cold Start in Movie Recommendation", UMUAI 2019.
- (MM-RecSys) Yashar Deldjoo, Mihai Gabriel Constantin, Hamid Eghbal-zadeh, Markus Schedl, Bogdan Ionescu, Paolo Cremonesi."Audio-Visual Encoding of Multimedia Content to Enhance Movie Recommendations", RecSys 2018.
- (Privacy-RecSys) Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara. "Towards Effective Device-Aware Federated Learning", AIIA 2019.
Academic Services
- Organizer: ACM RecSys Challenge 2019 (session-based hotel recommendation/Trivago), ACM RecSys Challenge 2017 (Job Recommendation/Xing), MediaEval 2019 (MovieRec and NEWSReel), MediaEval 2018 (MovieRec)
- PC Member: SIGIR'20, ACM MM'20, ECIR'20, UMAP'20, RecSys'19, ACM MM'19, UMAP'19, ECIR'19, MMSys'19
- Journal Reviewer: ACM Computing Surveys (CSUR), Journal of User Modeling and User-Adapted Interaction (UMUAI), Elsevier Expert Systems with Applications, IEEE Access