Dr Sham Puthiya Parambath

  • Research Associate (School of Computing Science)

Biography

Shameem Ahamed Puthiya Parambath (Sham) is a member of the Essence Research Lab under the research group Information, Data and Analysis (IDA) in the School of Computing Science, University of Glasgow working with Dr. Christos Anagnostopoulos and Prof. Roderick Murray-Smith ​

Before joining University of Glasgow, Sham was a Postdoctoral Researcher at Qatar Computing Research Institute, Doha in the area of machine learning for recommender systems. Shameem holds a BTech, MS and PhD in Computing Science from Kerala University (India), Umea University (Sweden) and  University of Technology Compiegne (France), respectively.​

Research interests

  • Sequential Learning
  • Learning with feedback
  • Mutli-Armed Bandits
  • Recommender Systems

 

Google Scholar: [link]

Publications

List by: Type | Date

Jump to: 2020 | 2018 | 2017 | 2016 | 2014
Number of items: 6.

2020

Puthiya Parambath, S. A. and Chawla, S. (2020) Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations. Data Mining and Knowledge Discovery, 34(5), pp. 1560-1588. (doi: 10.1007/s10618-020-00708-6)

Mohamed, A., Parambath, S. A. , Kaoudi, Z. and Aboulnaga, A. (2020) Popularity Agnostic Evaluation of Knowledge Graph Embeddings. In: Thirty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2020), 3-6 Aug 2020, pp. 1059-1068.

2018

Puthiya Parambath, S. A. , Vijayakumar, N. and Chawla, S. (2018) SAGA: A Submodular Greedy Algorithm for Group Recommendation. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA, 2-7 Feb 2018, pp. 3900-3908.

2017

Sarkar, S., Chawla, S., Ahmad, S. , Srivastava, J., Hammady, H., Filali, F., Znaidi, W. and Borge-Holthoefer, J. (2017) Effective urban structure inference from traffic flow dynamics. IEEE Transactions on Big Data, 3(2), pp. 181-193. (doi: 10.1109/TBDATA.2016.2641003)

2016

Puthiya Parambath, S. A. , Usunier, N. and Grandvalet, Y. (2016) A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16), Boston, MA, USA, 15-19 Sept 2016, pp. 15-22. ISBN 9781450340359 (doi:10.1145/2959100.2959149)

2014

Puthiya Parambath, S. , Usunier, N. and Grandvalet, Y. (2014) Optimizing F-measures by Cost-sensitive Classification. In: Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, Canada, 8-13 Dec 2014, pp. 2123-2131.

This list was generated on Mon Jan 25 05:01:16 2021 GMT.
Jump to: Articles | Conference Proceedings
Number of items: 6.

Articles

Puthiya Parambath, S. A. and Chawla, S. (2020) Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations. Data Mining and Knowledge Discovery, 34(5), pp. 1560-1588. (doi: 10.1007/s10618-020-00708-6)

Sarkar, S., Chawla, S., Ahmad, S. , Srivastava, J., Hammady, H., Filali, F., Znaidi, W. and Borge-Holthoefer, J. (2017) Effective urban structure inference from traffic flow dynamics. IEEE Transactions on Big Data, 3(2), pp. 181-193. (doi: 10.1109/TBDATA.2016.2641003)

Conference Proceedings

Mohamed, A., Parambath, S. A. , Kaoudi, Z. and Aboulnaga, A. (2020) Popularity Agnostic Evaluation of Knowledge Graph Embeddings. In: Thirty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2020), 3-6 Aug 2020, pp. 1059-1068.

Puthiya Parambath, S. A. , Vijayakumar, N. and Chawla, S. (2018) SAGA: A Submodular Greedy Algorithm for Group Recommendation. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA, 2-7 Feb 2018, pp. 3900-3908.

Puthiya Parambath, S. A. , Usunier, N. and Grandvalet, Y. (2016) A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16), Boston, MA, USA, 15-19 Sept 2016, pp. 15-22. ISBN 9781450340359 (doi:10.1145/2959100.2959149)

Puthiya Parambath, S. , Usunier, N. and Grandvalet, Y. (2014) Optimizing F-measures by Cost-sensitive Classification. In: Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, Canada, 8-13 Dec 2014, pp. 2123-2131.

This list was generated on Mon Jan 25 05:01:16 2021 GMT.