Convolutional Neural Networks for Sequence-Aware Recommender Systems

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/256422
Download file(s):
File Description SizeFormat 
256422.pdfFulltext784.1 kBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Master Thesis
Title: Convolutional Neural Networks for Sequence-Aware Recommender Systems
Authors: Kerschbaumer, Tim
Abstract: Recommender systems are prominent components of many of today’s web applications. Historically, the most successful recommender systems have been based on a matrix completion formulation. However, in some domains having sequence-aware recommender systems, i.e systems that take data’s sequential nature into account, may be beneficial to capture user’s short-term interests as well as long-term sequential patterns. The most successful methods for sequence-aware recommender systems have been based on recurrent neural networks. Recurrent neural networks, however, are often hard to train and suffer from several disadvantages in regard to speed and memory requirements. Several recent papers have suggested that convolutional neural networks can be used to process sequential data more efficiently and sometimes with better results than recurrent networks. In this thesis, we propose the use of convolutional neural networks for the task of sequence-aware recommendations. We present a two-stage deep learning approach to recommendations, where convolutional neural networks are used for sequence-aware candidate generation. Our results show that convolutional neural networks can achieve predictive performance comparable to state-of-the-art for sequence-aware recommendation tasks.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2018
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/20.500.12380/256422
Collection:Examensarbeten för masterexamen // Master Theses



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.