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Collaborative Genre Tagging
Recommender systems (RS) are used extensively in online retail and on media streaming platforms to help users filter the plethora of options at their disposal. Their goal is to provide users with suggestions of products or artworks that they might like. Content-based RS's make use of user and/or ite...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2020
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| _version_ | 1867614359250796544 |
|---|---|
| access_status_str | Open Access |
| author | Leslie, James |
| author2 | Lacerda, Miguel |
| author_browse | Lacerda, Miguel Leslie, James |
| author_facet | Lacerda, Miguel Leslie, James |
| author_sort | Leslie, James |
| collection | Thesis |
| description | Recommender systems (RS) are used extensively in online retail and on media streaming platforms to help users filter the plethora of options at their disposal. Their goal is to provide users with suggestions of products or artworks that they might like. Content-based RS's make use of user and/or item metadata to predict user preferences, while collaborative-filtering (CF) has proven to be an effective approach in tasks such as predicting movie or music preferences of users in the absence of any metadata. Latent factor models have been used to achieve state-of-the-art accuracy in many CF settings, playing an especially large role in beating the benchmark set in the Netflix Prize in 2008. These models learn latent features for users and items to predict the preferences of users. The first latent factor models made use of matrix factorisation to learn latent factors, but more recent approaches have made use of neural architectures with embedding layers. This master's dissertation outlines collaborative genre tagging (CGT), a transfer learning application of CF that makes use of latent factors to predict genres of movies, using only explicit user ratings as model inputs. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/32402 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:50:47.369Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/32402 Collaborative Genre Tagging Leslie, James Lacerda, Miguel Data Science Recommender systems (RS) are used extensively in online retail and on media streaming platforms to help users filter the plethora of options at their disposal. Their goal is to provide users with suggestions of products or artworks that they might like. Content-based RS's make use of user and/or item metadata to predict user preferences, while collaborative-filtering (CF) has proven to be an effective approach in tasks such as predicting movie or music preferences of users in the absence of any metadata. Latent factor models have been used to achieve state-of-the-art accuracy in many CF settings, playing an especially large role in beating the benchmark set in the Netflix Prize in 2008. These models learn latent features for users and items to predict the preferences of users. The first latent factor models made use of matrix factorisation to learn latent factors, but more recent approaches have made use of neural architectures with embedding layers. This master's dissertation outlines collaborative genre tagging (CGT), a transfer learning application of CF that makes use of latent factors to predict genres of movies, using only explicit user ratings as model inputs. 2020-11-19T11:19:39Z 2020-11-19T11:19:39Z 2020 2020-11-19T08:06:16Z Master Thesis Masters MSc http://hdl.handle.net/11427/32402 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Data Science Leslie, James Collaborative Genre Tagging |
| thesis_degree_str | Master's |
| title | Collaborative Genre Tagging |
| title_full | Collaborative Genre Tagging |
| title_fullStr | Collaborative Genre Tagging |
| title_full_unstemmed | Collaborative Genre Tagging |
| title_short | Collaborative Genre Tagging |
| title_sort | collaborative genre tagging |
| topic | Data Science |
| url | http://hdl.handle.net/11427/32402 |
| work_keys_str_mv | AT lesliejames collaborativegenretagging |