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Recommender systems
Thesis (MCom)--Stellenbosch University, 2019.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2019
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| _version_ | 1867613762343665664 |
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| access_status_str | Open Access |
| author | Dumbleton, Bronwyn Catherine |
| author2 | Bierman, Surette |
| author_browse | Bierman, Surette Dumbleton, Bronwyn Catherine |
| author_facet | Bierman, Surette Dumbleton, Bronwyn Catherine |
| author_sort | Dumbleton, Bronwyn Catherine |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MCom)--Stellenbosch University, 2019. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/107114 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:41:16.700Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/107114 Recommender systems Dumbleton, Bronwyn Catherine Bierman, Surette Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science. Collaborative filtering Data sparsity Multi-Label Classification Recommender systems (Information filtering) Factorization (Mathematics) Information filtering systems Supervised learning (Machine learning) UCTD Thesis (MCom)--Stellenbosch University, 2019. ENGLISH ABSTRACT: A Recommender System (RS) is a particular type of information filtering system used to propose relevant items to users. Their successful application in online retail is reflected in increased customer satisfaction and sales revenue, with further application in entertainment, e-commerce and services, and content. Hence it may be argued that recommender systems currently present some of the most successful and widely used machine learning algorithms in practice. We provide an overview of both standard and more modern approaches to recommender systems, including content-based and collaborative filtering, as well as latent factor models for collaborative filtering. A limitation of standard latent factor models is that their input is typically restricted to a set of item ratings. In contrast, general purpose supervised learning algorithms allow more flexible inputs, but are typically not able to handle the degree of data sparsity prevalent in recommendation problems. Factorisation machines, which are supervised learning methods, are able to incorporate more flexible inputs and are well suited to deal with the effects of data sparsity. We therefore study the use of factorisation in recommender problems and report an empirical study in which we compare the effects of data sparsity on latent factor models, as well as on factorisation machines. Currently in RS research, emphasis is placed on the advantages of recommender systems that yield recommendations that are simple to explain to users. Such recommender systems have been shown to be much more trustworthy than more complex, unexplainable systems. Towards a proposal for explainable recommendations, we also provide an overview of the connection between the recommender problem and Multi-Label Classification (MLC). Since some of the recent MLC approaches facilitate the interpretation of predictions, we conduct an empirical study in order to evaluate the use of various MLC approaches in the context of recommender problems. AFRIKAANSE OPSOMMING: ’n Aanbevelingstelsel (ABVS) is ’n spesifieke tipe inligting-siftingstelsel wat gebruik word om relevante items aan gebruikers voor te stel. Die suksesvolle toepassing van hierdie stelsels in aanlyn-aankope word gereflekteer in hoër gebruikersatisfaksie en wins, met verdere toepassings in die vermaaklikheidswêreld, e-handel, dienste, en inhoud. Derhalwe sou ’n mens kon argumenteer dat aanbevelingstelsels huidiglik van die suksesvolste en algemeenste masjienleer-algoritmes in die praktyk is. Hierdie tesis gee ’n oorsig oor beide die standaard-, en ook oor die moderner benaderings tot aanbevelingstelsels, insluitend inhoudsgebaseerde- en samewerkingsifting, sowel as latente faktor modelle vir samewerkingsifting. ’n Beperking van standaard latente faktor modelle is dat hulle invoer tipies slegs in die vorm van ’n versameling itemgraderings kan wees. In teenstelling hiermee, laat algemene ondertoesig leer-algoritmes buigsamer invoer toe, maar is hulle nie instaat om die graad van dataskaarsheid te hanteer wat in aanbevelingsprobleme aanwesig is nie. Faktoriserings-algoritmes, as ondertoesig leer-algoritmes, is daartoe instaat om buigsamer invoere te inkorporeer, en is geskik om die gevolge van dataskaarsheid to hanteer. Die gebruik van faktoriserings-algoritmes in aanbevelingsprobleme word derhalwe in hierdie tesis bestudeer, en die gevolge van dataskaarsheid op latente faktor modelle, sowel as op faktoriseringsalgoritmes, word empiries vergelyk. Huidiglik in ABVS navorsing, word die voordele van stelsels wat aanbevelings lewer wat makliker is om aan gebruikers te verduidelik, beklemtoon. Dit is bewys dat sulke stelsels meer betroubaar as ingewikkelder, onverduidelikbare stelsels is. In aanloop tot ’n voorstel vir meer verklaarbare aanbevelings, word ’n oorsig gegee oor die verband tussen die aanbevelingsprobleem en meervuldige-Y klassifikasie (MYK). Aangesien sommige van die onlangse meervuldige-Y klassifikasie benaderings die interpretasie van vooruitskattings fasiliteer, word ’n empiriese studie gedoen ten einde die gebruik van ’n aantal MYK benaderings in aanbevelingsprobleme te evalueer. Masters 2019-11-18T10:20:55Z 2019-12-11T06:48:04Z 2019-11-18T10:20:55Z 2019-12-11T06:48:04Z 2019-12 Thesis http://hdl.handle.net/10019.1/107114 en_ZA Stellenbosch University xv, 170 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Collaborative filtering Data sparsity Multi-Label Classification Recommender systems (Information filtering) Factorization (Mathematics) Information filtering systems Supervised learning (Machine learning) UCTD Dumbleton, Bronwyn Catherine Recommender systems |
| title | Recommender systems |
| title_full | Recommender systems |
| title_fullStr | Recommender systems |
| title_full_unstemmed | Recommender systems |
| title_short | Recommender systems |
| title_sort | recommender systems |
| topic | Collaborative filtering Data sparsity Multi-Label Classification Recommender systems (Information filtering) Factorization (Mathematics) Information filtering systems Supervised learning (Machine learning) UCTD |
| url | http://hdl.handle.net/10019.1/107114 |
| work_keys_str_mv | AT dumbletonbronwyncatherine recommendersystems |