Dr Sham Puthiya Parambath

  • Research Associate (School of Computing Science)

Biography

I, Shameem Ahamed Puthiya Parambath (Sham), am 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 the University of Glasgow, I worked as a Postdoctoral Researcher at Qatar Computing Research Institute, Doha, Qatar. I completed my Bachelors in Computer Science & Engineering, MS in Computing Science, and Ph.D. in Machine Learning from Kerala University (India), Umea University (Sweden), and University of Technology Compiegne (France), respectively.​ I also worked as a software engineer in firms like Directi and Narus Networks, before pursuing my master's degree.


At QCRI, I worked in the areas of recommender systems, Knowledge Graphs and Anomaly Detection. My work on group recommendation and cold-start recommendation were published in AAAI and ECML conferences. My work on quantifying bias in the knowledge graphs was published in UAI. During my Ph.D. thesis, I worked on multi-objective learning algorithms for multi-class/multi-label classification and personalized recommendations, which was published at venues such as NeurIPS and RecSys

Research interests

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

 

Google Scholar: [link]

Publications

List by: Type | Date

Jump to: 2021 | 2020 | 2018 | 2017 | 2016 | 2014
Number of items: 7.

2021

Puthiya Parambath, S. , Anagnostopoulos, C. , Murray-Smith, R. , MacAvaney, S. and Zervas, E. (2021) Max-Utility Based Arm Selection Strategy for Sequential Query Recommendations. In: 13th Asian Conference on Machine Learning (ACML 2021), 17-19 Nov 2021, (Accepted for Publication)

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 Tue Jan 18 22:30:48 2022 GMT.
Jump to: Articles | Conference Proceedings
Number of items: 7.

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

Puthiya Parambath, S. , Anagnostopoulos, C. , Murray-Smith, R. , MacAvaney, S. and Zervas, E. (2021) Max-Utility Based Arm Selection Strategy for Sequential Query Recommendations. In: 13th Asian Conference on Machine Learning (ACML 2021), 17-19 Nov 2021, (Accepted for Publication)

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 Tue Jan 18 22:30:48 2022 GMT.

Additional information

Reviewer for AAAI 2019, 2020, 2021, SDM 2020