Recommending social platform content using deep learning
dc.contributor.author | Goretskyy, Maxim | |
dc.contributor.author | Håkansson, Alexander | |
dc.contributor.author | Jaxing, Jesper | |
dc.contributor.author | Almén, Jonatan | |
dc.contributor.author | Olivecrona, Axel | |
dc.contributor.department | Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers) | sv |
dc.contributor.department | Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers) | en |
dc.date.accessioned | 2019-07-03T14:36:08Z | |
dc.date.available | 2019-07-03T14:36:08Z | |
dc.date.issued | 2017 | |
dc.description.abstract | In this thesis a model based on artificial neural networks, seeing how they have found successes in a variety of fields in recent years, is proposed as an alternative to existing recommender systems. A recurrent neural network model is built for recommending content to users on the social forum Reddit based on the titles of posts. The model is compared against Facebook’s fastText classifier and a model based on N-grams. The model proposed is performing close to, but not as well as either fastText or the N-grams based model. The model does not show any real advantages in its current state but a lot of potential improvements are proposed. | |
dc.identifier.uri | https://hdl.handle.net/20.500.12380/251698 | |
dc.language.iso | eng | |
dc.setspec.uppsok | Technology | |
dc.subject | Data- och informationsvetenskap | |
dc.subject | Computer and Information Science | |
dc.title | Recommending social platform content using deep learning | |
dc.type.degree | Examensarbete för kandidatexamen | sv |
dc.type.degree | Bachelor Thesis | en |
dc.type.uppsok | M2 | |
local.programme | Datateknik 300 hp (civilingenjör) |
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