Yang, K;
Toni, L;
(2019)
Graph-based recommendation system.
In:
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
(pp. pp. 798-802).
IEEE: Anaheim, CA, USA.
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Abstract
In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems. We propose a graph-based recommendation system that learns and exploits the geometry of the user space to create meaningful clusters in the user domain. This reduces the dimensionality of the recommendation problem while preserving the accuracy of MAB. We then study the effect of graph sparsity and clusters size on the MAB performance and provide exhaustive simulation results both in synthetic and in real-case datasets. Simulation results show improvements with respect to state-of-the-art MAB algorithms.
| Type: | Proceedings paper |
|---|---|
| Title: | Graph-based recommendation system |
| Event: | 2018 IEEE Global Conference on Signal and Information |
| Location: | Anaheim, CA, USA |
| Dates: | 26-29 Nov 2018 |
| ISBN-13: | 9781728112954 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/GlobalSIP.2018.8646359 |
| Publisher version: | https://doi.org/10.1109/GlobalSIP.2018.8646359 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Clustering algorithms, Simulation, Indexes, Geometry, Image edge detection, Iterative methods, Estimation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10072486 |
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