Collaborative Genre Tagging
| dc.contributor.advisor | Lacerda, Miguel | |
| dc.contributor.author | Leslie, James | |
| dc.date.accessioned | 2020-11-19T11:19:39Z | |
| dc.date.available | 2020-11-19T11:19:39Z | |
| dc.date.issued | 2020 | |
| dc.date.updated | 2020-11-19T08:06:16Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Leslie, J. (2020). <i>Collaborative Genre Tagging</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/32402 | en_ZA |
| dc.identifier.chicagocitation | Leslie, James. <i>"Collaborative Genre Tagging."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2020. http://hdl.handle.net/11427/32402 | en_ZA |
| dc.identifier.citation | Leslie, J. 2020. Collaborative Genre Tagging. . ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/32402 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Leslie, James AB - 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. DA - 2020_ DB - OpenUCT DP - University of Cape Town KW - Data Science LK - https://open.uct.ac.za PY - 2020 T1 - Collaborative Genre Tagging TI - Collaborative Genre Tagging UR - http://hdl.handle.net/11427/32402 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/32402 | |
| dc.identifier.vancouvercitation | Leslie J. Collaborative Genre Tagging. []. ,Faculty of Science ,Department of Statistical Sciences, 2020 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/32402 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Data Science | |
| dc.title | Collaborative Genre Tagging | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |