JOURNAL
- 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.
- Aytekin, A. M., & Aytekin, T. (2019). Real-time recommendation with locality sensitive hashing. Journal of Intelligent Information Systems, 53(1), 1-26.
- 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.
- 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.
- Karakaya, M. Ö., & Aytekin, T. (2018). Effective methods for increasing aggregate diversity in recommender systems. Knowledge and Information Systems, 56(2), 355-372.
- Muter, I., & Aytekin, T. (2017). Incorporating Aggregate Diversity in Recommender Systems Using Scalable Optimization Approaches. INFORMS Journal on Computing, 29(3), 405-421.
- 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.
- Aytekin, T., & Karakaya, M. Ö. (2014). Clustering-based diversity improvement in top-N recommendation. Journal of Intelligent Information Systems, 42(1), 1-18.
- Sayan, T. A. E., & Aytekin, T. (2012). Fodor on Causes of Mentalese Symbols. Organon F, 19(1), 3-15.
- Sayan, T. A. E. (2010). Misrepresentation and Robustness of Meaning. Organon F, 17(1), 21-38.
CONFERENCE PAPER
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Baskaya, O., & Aytekin, T. (2012). How similar is rating similarity to content similarity?. In RUE@ RecSys (pp. 27-29).
- Aytekin, T. (2003). Modeling multiplication fact retrieval: the effect of noise," in Proceedings of the European Cognitive Science Conference (pp. 37-42).
- 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.
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