C. Wang, S. Zhao, and S. Sahebi, “Learning from Non-assessed Resources: Deep Multi-type Knowledge Tracing,” in The 14th International Conference on Educational Data Mining (EDM-21), 2021.
C. Wang, S. Sahebi, S. Zhao, P. Brusilovsky, and L. Moraes, “Knowledge Tracing for Complex Problem Solving: Granular Rank-based Tensor Factorization,” in The 29th Conference on User Modeling, Adaptation and Personalization (UMAP-21), 2021.
M. Yao, S. Sahebi, R. Feyzi Behnagh, S. Bursali, and S. Zhao, “Temporal Processes Associating withProcrastination Dynamics,” in the Twenty Second International Conference on Artificial Intelligence in Education (AIED-21), 2021. [paper] [slides] [code]
M. Yao, S. Zhao, S. Sahebi, and R. Feyzi Behnagh, “Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling,” in The Thirtieth Web Conference (The Web-21), 2021. [paper] [slides] [code]
M. Yao, S. Zhao,S. Sahebi, and R. Feyzi Behnagh, “Relaxed clustered hawkes process for procras-tination modeling in moocs,” inThe Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021.[paper] [slides] [code] [video]
M. Mirzaei,S. Sahebi, and P. Brusilovsky, “Sb-dnmf: A structure based discriminative non-negativematrix factorization model for detecting inefficient learning behaviors”,” inThe2020IEEE/WIC/ACMInternational Joint Conference On Web Intelligence And Intelligent Agent Technology. WI-IAT,2020.
T.N. Doan, and S. Sahebi, “Transcrosscf: Transition-based cross-domain collaborative filtering”, 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020.
S. Zhao, C. Wang, and S. Sahebi, “Modeling Knowledge Acquisition from Multiple LearningResource Types”, 13th International Conference on Educational Data Mining (EDM), 2020. [paper] [code] [slides] [video]
M. Yao, S. Sahebi, and R. Feyzi Behnagh “Analyzing Student Procrastination in MOOCs: A Multivariate Hawkes Approach”, 13th International Conference on Educational Data Mining (EDM), 2020. [paper][code] [video]
M. Mirzaei, S. Sahebi, , and P. Brusilovsky, “Detecting Trait versus Performance Student Behavioral Pattern Using Discriminative Non-Negative Matrix Factorization”, The Thirty-Third International FLAIRS Conference, 2020.
T.N. Doan, and S. Sahebi, “Review-Based Cross-Domain Collaborative Filtering: A Neural Framework”, Third Workshop on Recommendation in Complex Scenarios (ComplexRec), 2019.
T.N. Doan, and S. Sahebi, “Rank-Based Tensor Factorization for Student Performance Prediction”, 12th International Conference on Educational Data Mining (EDM), 2019.
M. Mirzaei, S. Sahebi, , and P. Brusilovsky, “Annotated Examples and Parameterized Exercises: Analyzing Student’s Sequential Patterns”, The 20th International Conference on Artificial Intelligence in Education (AIED), 2019.
S. Sahebi, , and P. Brusilovsky, “Student Performance Prediction by Discovering Inter-Activity Relations”, 11th International Conference on Educational Data Mining (EDM), 2018.
S. Sahebi, , P. Brusilovsky, and V. Bobrikov, “Cross-domain recommendation for large-scale data”, The 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning (RecSysKTL), 2017.
S. Sahebi, Y. Lin, and P. Brusilovsky, “Tensor Factorization for Student Modeling and Performance Prediction in Unstructured Domain”, The 9th International Conference on Educational Data Mining, 2016.
S. Sahebi and P. Brusilovsky, “It Takes Two to Tango: an Exploration of Domain Pairs for Cross-Domain Collaborative Filtering”, 9th ACM Conference on Recommender Systems (RecSys), 2015. p. 131-138.
S. Sahebi and T. Walker, “Content-Based Cross-Domain Recommendations Using Segmented Models”, Workshop on New Trends in Content-based Recommender Systems (CBRecsys), 2014. p. 57-63.
J. Guerra, S. Sahebi, P. Brusilovsky, and Y. Lin, “The Problem Solving Genome: Analyzing Sequential Patterns of Student Work with Parameterized Exercises”, International Conference on Educational Data Mining, 2014. p. 153-160.
S. Sahebi, Y. Huang, and P. Brusilovsky, “Parameterized Exercises in Java Programming: using Knowledge Structure for Performance Prediction”, The second Workshop on AI-supported Education for Computer Science (AIEDCS), 2014.
S. Sahebi, Y. Huang, and P. Brusilovsky, “Predicting Student Performance in Solving Parameterized Exercises”, International Conference on Intelligent Tutoring Systems, 2014. p. 496-503.
S. Sahebi and P. Brusilovsky, “Cross-Domain Recommendation in a Cold-Start Context: The impact of User Profile Size on the Quality of Recommendation”, User Modeling and Adaptive Hypermedia, Springer Berlin Heidelberg, 2013. p. 289-295.
D. Parra and S. Sahebi, “Advanced Techniques in Web Intelligence - 2,” Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis, J. D. Vasquez et al. (Eds.), Ed. Berlin Heidelberg: Springer-Verlag, 2013, p. 149-175.
C. Lopez, R. Farzan, S. Sahebi, and P. Brusilovsky, “What Influences the Decision to Participate in Audience-bounded Online Communities?”, iConference, 2013.
S. Sahebi and W. W. Cohen, “Community-Based Recommendations: a Solution to the Cold Start Problem,” in Workshop on Recommender Systems and the Social Web (RSWEB), 2011.
P. Brusilovsky, D. Parra, S. Sahebi, and C. Wongchokprasitti, “Collaborative Information Finding in Smaller Communities: The Case of Research Talks,” in Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), IEEE, 2010.
S. Sahebi, C. Wongchokprasitti, and P. Brusilovsky, “Recommending Research Colloquia: A Study of Several Sources for User Profiling,” in International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec), 2010.