A hybrid recommender system for usage within e-commerce Content-boosted, context-aware, and collaborative filtering-based tensor factorization recommender system for targeted advertising within e-commerce

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/249910
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Type: Examensarbete för masterexamen
Master Thesis
Title: A hybrid recommender system for usage within e-commerce Content-boosted, context-aware, and collaborative filtering-based tensor factorization recommender system for targeted advertising within e-commerce
Authors: Lagerstedt, Marcus
Olsson, Marcus
Abstract: Recommender systems are information filtering systems that try to predict what rating a user would give an item, usually with the goal of recommending, would be high rated items to users. Today there exists recommender systems in most online stores, in one form or another. The complexity of these systems varies greatly, where the less complex ones might base their recommendations on similar products, while others are much more complex, utilizing user modeling etc. This thesis describes changes made to a context-aware and collaborative filtering-based tensor factorization recommender system, in order to adapt it to perform better with the implicit-only data found in e-commerce, specifically garment-based e-commerce. Multiple contexts are evaluated in regard to a specific data set, and the performance impact of the changes proposed are also measured. The evaluation is carried out through use of self-implemented algorithms written in Python. The project resulted in a content-boosted, context-aware, and collaborative filtering-based tensor factorization recommender system made for implicit-only e-commerce data. The results show that the changes proposed in this thesis give a substantial performance increase, while time-based contexts do not seem to increase performance, in regard to the specific data set used for evaluation in this project.
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/20.500.12380/249910
Collection:Examensarbeten för masterexamen // Master Theses



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