Short Bio

I got my Ph.D. degree from the Department of Informatics of Aristotle University of Thessaloniki in 2011. I have worked as a postdoctoral research fellow at ITI-CERTH from June 2011 to December 2013. In 2014, I was awarded an excellence grant for postdoctoral researchers in «Multimedia Big Data and Cloud Computing» from the Aristotle University. Also, in 2015 I was awarded an excellence grant for new researchers from UCY. Then, I became a postdoctoral research fellow at the Computer Science Department of University of Mons in Belgium. Currently, I am an assistant professor at DKE & IDS at Maastricht University. My main research interests are Machine Learning, Information Retrieval, Recommender Systems and Social Media Mining.

Profiles: [Google Scholar] [DBLP] [ResearchGate]

Contact: dimitrios (dot) rafailidis (at) maastrichtuniversity (dot) nl

Selected Publications

  • D. Rafailidis, F. Crestani, “GeoDCF: Deep Collaborative Filtering with Multifaceted Contextual Information in Location-based Social Networks”. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD’18), Dublin, Ireland, 2018.
  • D. Rafailidis, F. Crestani, “Friend Recommendation in Location-Based Social Networks via Deep Pairwise Learning”. Proceedings of the IEEE/ACM 2018 International Conference on Advances in Social Networks Analysis and Mining (ASONAM’18), pp. 421-428, Barcelona, Spain, 2018. [pdf]
  • D. Rafailidis, F. Crestani, “A Collaborative Ranking Model for Cross-Domain Recommendations”. Proceedings of the 26th ACM International Conference on International Conference on Information and Knowledge Management (CIKM’17), pp. 2263-2266, Singapore, 2017. [pdf]
  • D. Rafailidis, F. Crestani, “A Regularization Method with Inference of Trust and Distrust in Recommender Systems”. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD’17), pp. 253-268, Skopje, FYROM, 2017. [pdf]
  • D. Rafailidis, F. Crestani, “Learning to Rank with Trust and Distrust in Recommender Systems”. Proceedings of the 11th ACM Conference on Recommender Systems (RecSys’17), pp. 5-13, Como, Italy, 2017. [pdf]
  • D. Rafailidis, F. Crestani, “Joint Collaborative Ranking with Social Relationships in Top-N Recommendation”. Proceedings of the 25th ACM International Conference on International Conference on Information and Knowledge Management (CIKM’16), pp. 1393-1402, Indianapolis, IN, USA, 2016. [pdf]
  • D. Rafailidis, F. Crestani, “Network Completion via Joint Node Clustering and Similarity Learning”. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’16), pp. 63-68, San Francisco, CA, USA, 2016. [pdf]
  • D. Rafailidis, F. Crestani, “Top-N Recommendation via Joint Cross-Domain User Clustering and Similarity Learning”. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD’16), Part II, pp. 426-441, Riva del Garda, Italy, 2016. [pdf]
  • D. Rafailidis, F. Crestani, “Cluster-based Joint Matrix Factorization Hashing for Cross-Modal Retrieval”. Proceedings of the 39th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’16), pp. 781-784, Pisa, Italy, 2016. [pdf]
  • D. Rafailidis, F. Crestani, “Collaborative Ranking with Social Relationships for Top-N Recommendations”. Proceedings of the 39th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’16), pp. 785-788, Pisa, Italy, 2016. [pdf]
  • D. Rafailidis, A. Nanopoulos, “Repeat Consumption Recommendation based on Users Preference Dynamics and Side Information”. Proceedings of the 24th ACM International Conference on World Wide Web, Companion Volume (WWW’15), pp. 99-100, Florence, Italy, 2015. [pdf]
  • D. Rafailidis, A. Nanopoulos, “Crossing the Boundaries of Communities via Limited Link Injection for Information Diffusion in Social Networks”. Proceedings of the 24th ACM International Conference on World Wide Web, Companion Volume (WWW’15), pp. 97-98, Florence, Italy, 2015. [pdf]
  • D. Rafailidis, A. Nanopoulos, “Modeling the Dynamics of User Preferences in Coupled Tensor Factorization”. Proceedings of the 8th ACM Conference on Recommender Systems (RecSys’14), pp. 321-324, Silicon Valley, CA, USA, 2014. [pdf]
  • S. Antaris, D. Rafailidis, “In-memory Stream Indexing of Massive and Fast Incoming Multimedia Content“, IEEE Transactions on Big Data (TBD), Vol.4, N.1, pp. 40-54, 2018. [pdf]
  • A. Axenopoulos, D. Rafailidis, G. Papadopoulos, E. Houstis, P. Daras, “Similarity Search of Flexible 3D Molecules Combining Local and Global Shape Descriptors“, IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Vol.13, N.5, pp. 954-970, 2016. [pdf]
  • D. Rafailidis, A. Nanopoulos, “Modeling Users Preference Dynamics and Side Information in Recommender Systems”, IEEE Transactions on Systems, Man and Cybernetics: Systems (TSMC), Vol.46, N.6, pp. 782-792, 2016. [pdf]
  • S. Antaris, D. Rafailidis, “Similarity Search over the Cloud based on Image Descriptors′ Dimensions Value Cardinalities”, ACM Transactions on Multimedia Computing, Communications and Applications (TOMCCAP), Vol.11, N.4, Article Number 51, 2015. [pdf]
  • D. Rafailidis, A. Axenopoulos, J. Etzold, S. Manolopoulou, P. Daras, “Content-based Tag Propagation and Tensor Factorization for Personalized Item Recommendation based on Social Tagging”, ACM Transactions on Interactive Intelligent Systems (TiiS), Vol.3, N.4, Article Number 26, 2014. [pdf]
  • E. Tiakas, D. Rafailidis, A. Dimou, P. Daras, “MSIDX: Multi-Sort Indexing for Efficient Content-based Image Search and Retrieval”, IEEE Transactions on Multimedia (TMM), Vol.15, N.6, pp. 1415-1430, 2013. [pdf]
  • D. Rafailidis, P. Daras, “The TFC Model: Tensor Factorization and Tag Clustering for Item Recommendation in Social Tagging Systems”, IEEE Transactions on Systems, Man and Cybernetics: Systems (TSMC), Vol.43, N.3, pp. 673-688, 2013. [pdf]
  • A. Nanopoulos, D. Rafailidis, P. Symeonidis, Y. Manolopoulos, “MusicBox: Personalized Music Recommendation based on Cubic Analysis of Social Tags”, IEEE Transactions on Audio, Speech and Language Processing (TASLP), Vol.18, N.2, pp. 407-412, 2010. [pdf]