Recommending social platform content using deep learning

Examensarbete för kandidatexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/251698
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Type: Examensarbete för kandidatexamen
Bachelor Thesis
Title: Recommending social platform content using deep learning
Authors: Goretskyy, Maxim
Håkansson, Alexander
Jaxing, Jesper
Almén, Jonatan
Olivecrona, Axel
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.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2017
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/251698
Collection:Examensarbeten för kandidatexamen // Bachelor Theses



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