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Leveraging the multimodal information from video content for video recommendation
Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613610281271296 |
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| access_status_str | Open Access |
| author2 | De Villiers, Johan Pieter |
| author_browse | De Villiers, Johan Pieter |
| author_facet | De Villiers, Johan Pieter |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/80994 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:53.005Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/80994 Leveraging the multimodal information from video content for video recommendation De Villiers, Johan Pieter u13010396@tuks.co.za De Freitas, Allan Almeida, Adolfo Ricardo Lopes De Video recommendation item cold-start deep learning features multimodal feature fusion matrix scaling Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021. Since the popularisation of media streaming, a number of video streaming services are continually buying new video content to mine the potential profit. As such, newly added content has to be handled appropriately to be recommended to suitable users. In this dissertation, the new item cold-start problem is addressed by exploring the potential of various deep learning features to provide video recommendations. The deep learning features investigated include features that capture the visual-appearance, as well as audio and motion information from video content. Different fusion methods are also explored to evaluate how well these feature modalities can be combined to fully exploit the complementary information captured by them. Experiments on a real-world video dataset for movie recommendations show that deep learning features outperform hand crafted features. In particular, it is found that recommendations generated with deep learning audio features and action-centric deep learning features are superior to Mel-frequency cepstral coefficients (MFCC) and state-of-the-art improved dense trajectory (iDT) features. It was also found that the combination of various deep learning features with textual metadata and hand-crafted features provide significant improvement in recommendations, as compared to combining only deep learning and hand-crafted features. The MultiChoice Research Chair of Machine Learning at the University of Pretoria UP Postgraduate Masters Research bursary Electrical, Electronic and Computer Engineering MEng (Computer Engineering) Unrestricted 2021-07-27T08:39:30Z 2021-07-27T08:39:30Z 2021 2021 Dissertation * S2021 http://hdl.handle.net/2263/80994 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Video recommendation item cold-start deep learning features multimodal feature fusion matrix scaling Leveraging the multimodal information from video content for video recommendation |
| title | Leveraging the multimodal information from video content for video recommendation |
| title_full | Leveraging the multimodal information from video content for video recommendation |
| title_fullStr | Leveraging the multimodal information from video content for video recommendation |
| title_full_unstemmed | Leveraging the multimodal information from video content for video recommendation |
| title_short | Leveraging the multimodal information from video content for video recommendation |
| title_sort | leveraging the multimodal information from video content for video recommendation |
| topic | Video recommendation item cold-start deep learning features multimodal feature fusion matrix scaling |
| url | http://hdl.handle.net/2263/80994 |