2023

Paun, I., Moshfeghi, Y. and Ntarmos, N. (2023) White-box: on the prediction of collaborative filtering recommendation systems' performance. ACM Transactions on Internet Technology, 23(1), 8. (doi: 10.1145/3554979)

2022

Kavanagh, W., Fraser, D., Hunter, E. , Pancheva, A., Parkinson, J. , Paun, I., Wallis, T. , Ada, M. B., Border, H. and Norman, G. (2022) Experience report: challenges and opportunities of remote labs for a computer science department. Postgraduate Pedagogies, 2(1), pp. 173-207.

2021

Paun, I., Moshfeghi, Y. and Ntarmos, N. (2021) Are we there yet? Estimating Training Time for Recommendation Systems. In: EuroMLSys ’21, 26 Apr 2021, pp. 39-47. ISBN 9781450382984 (doi: 10.1145/3437984.3458832)

2020

Paun, I. (2020) Efficiency-Effectiveness Trade-offs in Recommendation Systems. In: 14th ACM Conference on Recommender Systems (RecSys 2020), 22-26 Sep 2020, pp. 770-775. ISBN 9781450375832 (doi: 10.1145/3383313.3411452)

Williamson, J. H. , Quek, M., Popescu, I., Ramsay, A. and Murray-Smith, R. (2020) Efficient human-machine control with asymmetric marginal reliability input devices. PLoS ONE, 15(6), e0233603. (doi: 10.1371/journal.pone.0233603) (PMID:32479507) (PMCID:PMC7263597)

Kyosev, I., Paun, I., Moshfeghi, Y. and Ntarmos, N. (2020) Measuring Distances Among Graphs En Route To Graph Clustering. In: 5th IEEE Workshop on Advances in High-Dimensional Big Data 2020, IEEE Big Data 2020, 10-13 Dec 2020, pp. 3632-3641. ISBN 9781728162515 (doi: 10.1109/BigData50022.2020.9378333)

Morgan, C., Paun, I. and Ntarmos, N. (2020) Exploring Contextual Paradigms in Context-Aware Recommendations. In: 4th IEEE Workshop on Human-in-the-Loop Methods and Future of Work in Big Data 2020, IEEE Big Data 2020, Atlanta, GA, USA, 10-13 Dec 2020, ISBN 9781728162515 (doi: 10.1109/BigData50022.2020.9377964)

2018

Popescu, I., Portelli, K., Anagnostopoulos, C. and Ntarmos, N. (2018) The Case for Graph-Based Recommendations. In: 2017 IEEE International Conference on Big Data (IEEE BigData 2017), Boston, MA, USA, 11-14 Dec 2017, pp. 4819-4821. ISBN 9781538627150 (doi: 10.1109/BigData.2017.8258553)

Articles

Paun, I., Moshfeghi, Y. and Ntarmos, N. (2023) White-box: on the prediction of collaborative filtering recommendation systems' performance. ACM Transactions on Internet Technology, 23(1), 8. (doi: 10.1145/3554979)

Kavanagh, W., Fraser, D., Hunter, E. , Pancheva, A., Parkinson, J. , Paun, I., Wallis, T. , Ada, M. B., Border, H. and Norman, G. (2022) Experience report: challenges and opportunities of remote labs for a computer science department. Postgraduate Pedagogies, 2(1), pp. 173-207.

Williamson, J. H. , Quek, M., Popescu, I., Ramsay, A. and Murray-Smith, R. (2020) Efficient human-machine control with asymmetric marginal reliability input devices. PLoS ONE, 15(6), e0233603. (doi: 10.1371/journal.pone.0233603) (PMID:32479507) (PMCID:PMC7263597)

Conference Proceedings

Paun, I., Moshfeghi, Y. and Ntarmos, N. (2021) Are we there yet? Estimating Training Time for Recommendation Systems. In: EuroMLSys ’21, 26 Apr 2021, pp. 39-47. ISBN 9781450382984 (doi: 10.1145/3437984.3458832)

Paun, I. (2020) Efficiency-Effectiveness Trade-offs in Recommendation Systems. In: 14th ACM Conference on Recommender Systems (RecSys 2020), 22-26 Sep 2020, pp. 770-775. ISBN 9781450375832 (doi: 10.1145/3383313.3411452)

Kyosev, I., Paun, I., Moshfeghi, Y. and Ntarmos, N. (2020) Measuring Distances Among Graphs En Route To Graph Clustering. In: 5th IEEE Workshop on Advances in High-Dimensional Big Data 2020, IEEE Big Data 2020, 10-13 Dec 2020, pp. 3632-3641. ISBN 9781728162515 (doi: 10.1109/BigData50022.2020.9378333)

Morgan, C., Paun, I. and Ntarmos, N. (2020) Exploring Contextual Paradigms in Context-Aware Recommendations. In: 4th IEEE Workshop on Human-in-the-Loop Methods and Future of Work in Big Data 2020, IEEE Big Data 2020, Atlanta, GA, USA, 10-13 Dec 2020, ISBN 9781728162515 (doi: 10.1109/BigData50022.2020.9377964)

Popescu, I., Portelli, K., Anagnostopoulos, C. and Ntarmos, N. (2018) The Case for Graph-Based Recommendations. In: 2017 IEEE International Conference on Big Data (IEEE BigData 2017), Boston, MA, USA, 11-14 Dec 2017, pp. 4819-4821. ISBN 9781538627150 (doi: 10.1109/BigData.2017.8258553)