Constructing a Context-aware Recommender System with Web Sessions

Examensarbete för masterexamen

Please use this identifier to cite or link to this item:
Download file(s):
File Description SizeFormat 
219471.pdfFulltext1.04 MBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Master Thesis
Title: Constructing a Context-aware Recommender System with Web Sessions
Authors: Bramstång, Albin
Jin, Yanling
Abstract: During the last decade, the importance of recommender systems has been increasing to the point that the success of many well-known service providers depends on these technologies. Recommender systems can assist people in their decision making process by anticipating preferences. However, common recommender algorithms often suffer from lack of explicit feedback and the \cold start" problem. This thesis investigates an approach of using implicit data only, to extract users' intent for fashion e-commerce in cold start situations. Markov Decision Processes (MDPs) are used on web session data to extract topic models. This thesis also explores how well the topic models can capture users intent and whether they can be used to produce good recommendations. The results show that this approach was able to accurately identify sessions topics, and in most cases the topics could successfully be translated to product recommendations.
Keywords: Informations- och kommunikationsteknik;Data- och informationsvetenskap;Information & Communication Technology;Computer and Information Science
Issue Date: 2015
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
Collection:Examensarbeten för masterexamen // Master Theses

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