Benjamin Paul Chamberlain
Benjamin Paul Chamberlain
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Neural embeddings of graphs in hyperbolic space
BP Chamberlain, J Clough, MP Deisenroth
arXiv preprint arXiv:1705.10359, 2017
Customer lifetime value prediction using embeddings
BP Chamberlain, A Cardoso, CHB Liu, R Pagliari, MP Deisenroth
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017
Predicting twitter user socioeconomic attributes with network and language information
N Aletras, BP Chamberlain
Proceedings of the 29th on Hypertext and Social Media, 20-24, 2018
SIGN: Scalable Inception Graph Neural Networks
F Frasca, E Rossi, D Eynard, B Chamberlain, M Bronstein, F Monti
arXiv e-prints, arXiv: 2004.11198, 2020
Temporal graph networks for deep learning on dynamic graphs
E Rossi, B Chamberlain, F Frasca, D Eynard, F Monti, M Bronstein
arXiv preprint arXiv:2006.10637, 2020
Scalable hyperbolic recommender systems
BP Chamberlain, SR Hardwick, DR Wardrope, F Dzogang, F Daolio, ...
arXiv preprint arXiv:1902.08648, 2019
Probabilistic inference of twitter users’ age based on what they follow
BP Chamberlain, C Humby, MP Deisenroth
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017
Real-time community detection in full social networks on a laptop
BP Chamberlain, J Levy-Kramer, C Humby, MP Deisenroth
PloS one 13 (1), e0188702, 2018
Generalising random forest parameter optimisation to include stability and cost
CHB Liu, BP Chamberlain, DA Little, Â Cardoso
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017
Learning Embeddings for Product Size Recommendations
K Dogani, M Tomassetti, S De Cnudde, S Vargas, B Chamberlain
eCOM@SIGIR, 2019
Fashion Outfit Generation for E-commerce
EM Bettaney, SR Hardwick, O Zisimopoulos, BP Chamberlain
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019
A recurrent neural network survival model: Predicting web user return time
GL Grob, Â Cardoso, CHB Liu, DA Little, BP Chamberlain
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018
Designing experiments to measure incrementality on facebook
CH Liu, EM Bettaney, BP Chamberlain
arXiv preprint arXiv:1806.02588, 2018
Online Controlled Experiments for Personalised e-Commerce Strategies: Design, Challenges, and Pitfalls
CH Liu, BP Chamberlain
arXiv preprint arXiv:1803.06258, 2018
On overlapping community-based networks: generation, detection, and their applications
CHB Liu, MP Deisenroth, BP Chamberlain
Master’s thesis, Imperial College London, London, United Kingdom, 2016
Tuning Word2vec for Large Scale Recommendation Systems
BP Chamberlain, E Rossi, D Shiebler, S Sedhain, MM Bronstein
In Fourteenth ACM Conference on Recommender Systems (pp. 732-737), 2020
What is the value of experimentation & measurement?
CH Liu, BP Chamberlain
IEEE International Conference on Data Mining (ICDM) 2019, 2019
Speeding up bigclam implementation on snap
CH Liu, BP Chamberlain
arXiv preprint arXiv:1712.01209, 2017
What is the Value of Experimentation and Measurement? Quantifying the Value and Risk of Reducing Uncertainty to Make Better Decisions
CHB Liu, BP Chamberlain, EJ McCoy
Data Science and Engineering 5, 152-167, 2020
Practical challenges of learning and representation for large graphs
BP Chamberlain
University of London, 2018
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