Cagali, T;
Sadrzadeh, M;
Newell, C;
(2022)
Enhancing Personalised Recommendations with the Use of Multimodal Information.
In:
Proceedings - 23rd IEEE International Symposium on Multimedia, ISM 2021.
(pp. pp. 186-190).
IEEE: Naple, Italy.
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Abstract
Whenever we watch a TV show or movie, we process a substantial amount of information that is conveyed to us via various multimedia mediums, in particular: visual, textual, and audio. These data signify distinctive properties that aid in creating a unique motion picture experience. In effort to not only produce a more personalised recommender system, but also tackle the problem of popularity bias, we develop a system that incorporates the use of multimodal information. Specifically, we investigate the correlation between features that are extracted using state of the art techniques and deep learning models from visual characteristics, audio patterns and subtitles. The framework is evaluated on a dataset comprising of 145 BBC TV programmes against genre and user baselines. We demonstrate that personalised recommendations can not only be improved with the use of multimodal information, but also outperform genre and user-based models in terms of diversity, whilst maintaining matching levels of accuracy.
| Type: | Proceedings paper |
|---|---|
| Title: | Enhancing Personalised Recommendations with the Use of Multimodal Information |
| Event: | 2021 IEEE International Symposium on Multimedia (ISM) |
| Dates: | 29 Nov 2021 - 1 Dec 2021 |
| ISBN-13: | 9781665437349 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ISM52913.2021.00037 |
| Publisher version: | http://dx.doi.org/10.1109/ISM52913.2021.00037 |
| 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: | Deep learning, Measurement, Visualization, TV, Multimedia systems, Neural networks, Feature extraction |
| UCL classification: | 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 Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10146178 |
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