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Assoc. Prof. Tevfik Aytekin

Bahçeşehir University, Faculty of Engineering and Natural Sciences

Computer Engineering Department

JOURNAL

  1. Yağcı, A. M., Aytekin, T., & Gürgen, F. S. (2019). Parallel pairwise learning to rank for collaborative filtering. Concurrency and Computation: Practice and Experience, 31(15), e5141.
  2. Aytekin, A. M., & Aytekin, T. (2019). Real-time recommendation with locality sensitive hashing. Journal of Intelligent Information Systems, 53(1), 1-26.
  3. Yagci, A. M., Aytekin, T., & Gurgen, F. S. (2019). A Meta-algorithm for Improving Top-N Prediction Efficiency of Matrix Factorization Models in Collaborative Filtering. International Journal of Pattern Recognition and Artificial Intelligence.
  4. Aytekin, T. (2018). Reservoir Sampling Based Streaming Method for Large Scale Collaborative Filtering. International Journal of Intelligent Systems and Applications in Engineering, 6(3), 191-196.
  5. Karakaya, M. Ö., & Aytekin, T. (2018). Effective methods for increasing aggregate diversity in recommender systems. Knowledge and Information Systems, 56(2), 355-372.
  6. Muter, I., & Aytekin, T. (2017). Incorporating Aggregate Diversity in Recommender Systems Using Scalable Optimization Approaches. INFORMS Journal on Computing, 29(3), 405-421.
  7. Yagci, A. M., Aytekin, T., & Gurgen, F. S. (2017). Scalable and adaptive collaborative filtering by mining frequent item co-occurrences in a user feedback stream. Engineering Applications of Artificial Intelligence, 58, 171-184.
  8. Aytekin, T., & Karakaya, M. Ö. (2014). Clustering-based diversity improvement in top-N recommendation. Journal of Intelligent Information Systems, 42(1), 1-18.
  9. Sayan, T. A. E., & Aytekin, T. (2012). Fodor on Causes of Mentalese Symbols. Organon F, 19(1), 3-15.
  10. Sayan, T. A. E. (2010). Misrepresentation and Robustness of Meaning. Organon F, 17(1), 21-38.

CONFERENCE PAPER

  1. Uslu, A., Tekin, S., & Aytekin, T. (2019, April). Sentiment analysis in Turkish film comments. In 2019 27th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  2. Yıldız, T. Z., & Aytekin, T. (2019, April). Short term water demand forecasting using regional data. In 2019 27th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
  3. Kara, K. C., Esen, S., Kahyalar, N., Karakaş, A. A., & Aytekin, T. (2017, October). Design and implementation of a job recommender system. In Computer Science and Engineering (UBMK), 2017 International Conference on (pp. 729-733). IEEE.
  4. Yagci, M., Aytekin, T., & Gurgen, F. (2017, August). On parallelizing SGD for pairwise learning to rank in collaborative filtering recommender systems. In Proceedings of the Eleventh ACM Conference on Recommender Systems (pp. 37-41). ACM.
  5. Yağcı, A. M., Aytekin, T., & Gürgen, F. S. (2016). Balanced random forest for imbalanced data streams. In Signal Processing and Communication Application Conference (SIU), 2016 24th (pp. 1065-1068). IEEE.
  6. Macit, M., Delibaş, E., Karanlık, B., İnal, A., & Aytekin, T. (2016). Real time distributed analysis of MPLS network logs for anomaly detection. In Network Operations and Management Symposium (NOMS), 2016 IEEE/IFIP (pp. 750-753). IEEE.
  7. Yağci, A. M., Aytekin, T., & Gürgen, F. S. (2015). An ensemble approach for multi-label classification of item click sequences. In Proceedings of the 2015 International ACM Recommender Systems Challenge (p. 7). ACM.
  8. Aytekin, A. M., & Aytekin, T. (2015). Locality sensitive hashing based scalable collaborative filtering. In Signal Processing and Communications Applications Conference (SIU), 2015 23th (pp. 1030-1033). IEEE.
  9. Ülker, C. C., & Aytekin, T. (2013, September). Improving the performance of active voxel selection in the analysis of fMRI data using genetic algorithms. In Proceedings of the 6th Balkan Conference in Informatics (pp. 129-136). ACM.
  10. Baskaya, O., & Aytekin, T. (2012). How similar is rating similarity to content similarity?. In RUE@ RecSys (pp. 27-29).
  11. Aytekin, T. (2003). Modeling multiplication fact retrieval: the effect of noise," in Proceedings of the European Cognitive Science Conference (pp. 37-42). 
  12. Aytekin, T., Korkmaz, E. E., & Güvenir, H. A. (1995). An application of genetic programming to the 4-OP problem using map-trees. In Progress in Evolutionary Computation (pp. 28-40). Springer Berlin Heidelberg.

BOOK