Publications

Peer-Reviewed Conference and Workshop Papers

• C. Wang, S. Sahebi, “Continuous personalized knowledge tracing: Modeling long-term learning in online environments,” 2023.

• S. Zhao and S. Sahebi, “Graph-enhanced multi-activity knowledge tracing,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2023, pp. 529–546. paper code slides

• S. Zhao, S. Sahebi, and R. Feyzi-Behnagh, “Curb your procrastination: A study of academic procrastination behaviors,” in The 31st Conference on User Modeling, Adaptation and Personalization (UMAP), 2023. paper code slides

• S. Zhao, C. Wang, and S. Sahebi, “Transition-aware multi-activity knowledge tracing,” in The 2022 IEEE International Conference on Big Data, 2022. paper code slides video

• C. Wang, S. Sahebi, and P. Brusilovsky “Proximity-based educational recommendations: A multi-objective framework”, The 2nd Workshop on Multi-Objective Recommender Systems (MORS’22), 2022. paper

• M. Yao, S. Zhao, S. Sahebi, and R. Feyzi Behnagh “Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling”, The Thirtieth Web Conference (The Web-21), 2021. paper code slides video

• C. Wang, S. Sahebi, S. Zhao, P. Brusilovsky, and L. Moraes “Knowledge tracing for complex problem solving: Granular rank-based tensor factorization”, The 29th Conference on User Modeling, Adaptation and Personalization (UMAP-21), 2021. paper code

• M. Yao, S. Zhao, S. Sahebi, and R. Feyzi Behnagh “Relaxed clustered hawkes process for procrastination modeling in moocs”, Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021. paper code slides

• C. Wang, S. Zhao, and S. Sahebi “Learning from non-assessed resources: Deep multi-type knowledge tracing”, The 14th International Conference on Educational Data Mining (EDM-21), 2021. paper code

• M. Yao, S. Sahebi, R. Feyzi Behnagh, S. Bursali, and S. Zhao “Temporal processes associating with procrastination dynamics”, The 22nd International Conference on Artificial Intelligence in Education (AIED-21), 2021. paper

• C. Wang, S. Sahebi, and H. Torkamaan “STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data”, The 2021 IEEE/WIC/ACM International Joint Conference On Web Intelligence And Intelligent Agent Technology (WI-IAT), 2021. paper code

• C. Wang, S. Sahebi, and P. Brusilovsky “MOCHI: an Offline Evaluation Framework for Educational Recommendations”, The Workshop on Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES’21), 2021. paper code

• T.N. Doan, and S. Sahebi, “Transcrosscf: Transition-based cross-domain collaborative filtering”, 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020 paper code

• M. Mirzaei, S. Sahebi, and P. Brusilovsky, “SB-DNMF: A structure based discriminative non-negativematrix factorization model for detecting inefficient learning behaviors”, The 2020 IEEE/WIC/ACMInternational Joint Conference On Web Intelligence And Intelligent Agent Technology. WI-IAT, 2020. paper code video

• S. Zhao, C. Wang, and S. Sahebi, “Modeling Knowledge Acquisition from Multiple Learning Resource Types”, 13th International Conference on Educational Data Mining (EDM), 2020. paper code 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. paper code

• T.N. Doan, and S. Sahebi, “Review-Based Cross-Domain Collaborative Filtering: A Neural Framework”, Third Workshop on Recommendation in Complex Scenarios (ComplexRec), 2019. paper slides code

• T.N. Doan, and S. Sahebi, “Rank-Based Tensor Factorization for Student Performance Prediction”, 12th International Conference on Educational Data Mining (EDM), 2019. paper slides code

• 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. paper slides

• S. Sahebi, , and P. Brusilovsky, “Student Performance Prediction by Discovering Inter-Activity Relations”, 11th International Conference on Educational Data Mining (EDM), 2018. paper

• 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. paper

• 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. paper code

• 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. paper

• 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. paper

• 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. paper

• 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. paper

• S. Sahebi, Y. Huang, and P. Brusilovsky, “Predicting Student Performance in Solving Parameterized Exercises”, International Conference on Intelligent Tutoring Systems, 2014. p. 496-503. paper

• 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. paper

• C. Lopez, R. Farzan, S. Sahebi, and P. Brusilovsky, “What Influences the Decision to Participate in Audience-bounded Online Communities?”, iConference, 2013. paper

• 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. paper

• 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. paper

• 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. paper

• S. Sahebi, F. Oroumchian, and R. Khosravi, “An Enhanced Similarity Measure for Utilizing Site Structure in Web Personalization Systems,” in 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2008, pp. 82-85. paper

• R. Khosravi, M. Sirjani, N. Asoudeh, S. Sahebi, and H. Iravanchi, “Modeling and Analysis of Reo Connectors Using Alloy,” in Coordination Models and Languages, D. Lea and G. Zavattaro, Eds. Berlin, Heidelberg: Springer Berlin, Heidelberg, 2008, pp. 169-183. paper

• S. Sahebi, F. Oroumchian, and R. Khosravi, “Applying and Comparing Hidden Markov Model and Fuzzy Clustering Algorithms to Web Usage Data for Recommender Systems,” in Data Mining and Knowledge Discovery, 2008, pp. 179-181. paper

Posters

• D. Parra, W. Jeng, P. Brusilovsky, C. Lopez, and S. Sahebi, “Conference Navigator 3: An Online Social Conference Support System,” Poster in User Modeling and Adaptive Hypermedia, 2012. paper

Book Chapters

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. link

Theses

• [Ph.D. Thesis] Correlation Analysis in Cross-Domain Recommendation, University of Pittsburgh, 2016.

• [M.S. Thesis] Cross-Domain Recommendation: The Feasibility and the Value for the Cold Start Users, University of Pittsburgh, 2013.

• [M.S. Thesis] Applying Web Content to Web Recommendation Systems, University of Tehran, 2009.

• [B.S. Thesis] A System for Evaluating View Updating in RDBMSs, Sharif University of Technology, 2005.

Other Publications

• R. Feyzi-Behnagh, S. Bursali, J. R. Ferrari, and S. Sahebi, “Procrastination, indecision, and self-regulated learning: Relationship within online learning environments,” in The Annual Meeting of the Eastern Psychological Association (EPA), 2022.

• R. Feyzi-Behnagh, S. Bursali, S. Sahebi, M. Yao, and S. Zhao, “Operationalizing and measuring academic procrastination behavior using trace data,” in The Annual Meeting of the Eastern Psychological Association (EPA), 2022.

• R. Feyzi-Behnagh, S. Bursali, S. Sahebi, M. Yao, and S. Zhao, “Procrastination: Associations with emotion regulation, achievement emotions, and achievement goal-orientation,” in The 2022 American Educational Research Association Conference (AERA 2022), 2022.

• R. Feyzi-Behnagh, S. Bursali, S. Sahebi, M. Yao, and S. Zhao, “Operationalizing and measuring academic procrastination behavior using trace data,” in The 2022 American Educational Research Association Conference (AERA 2022), 2022