Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce

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Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce


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Licentiate thesis
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Public defence 2018-03-16
Time 13:00
Location NIB:0E07
Faculty examiner Jan Persson, Fakulteten för teknik och samhälle, Datavetenskap
Publication Licentiate Thesis
Licentiate Thesis, comprehensive summary
Title Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce
Author Paraschakis, Dimitris
Date 2018
English abstract
Recommender systems have become an integral part of virtually every e-commerce application on the web. These systems enable users to quickly discover relevant products, at the same time increasing business value. Over the past decades, recommender systems have been modeled using numerous machine learning techniques. However, the adoptability of these models by commercial applications remains unclear. We assess the receptiveness of the industrial sector to algorithmic contributions of the research community by surveying more than 30 e-commerce platforms, and experimenting with various recommenders on proprietary e-commerce datasets. Another overlooked but important factor that complicates the design and use of recommender systems is their ethical implications. We identify and summarize these issues in our ethical recommendation framework, which also enables users to control sensitive moral aspects of recommendations via the proposed “ethical toolbox”. The feasibility of this tool is supported by the results of our user study. Because of moral implications associated with user profiling, we investigate algorithms capable of generating user-agnostic recommendations. We propose an ensemble learning scheme based on Thompson Sampling bandit policy, which models arms as base recommendation functions. We show how to adapt this algorithm to realistic situations when neither arm availability nor reward stationarity is guaranteed.
DOI (link to publisher's fulltext.)
Publisher Malmö university, Faculty of Technology and Society
Series/Issue Studies in Computer Science;4
ISBN 978-91-7104-900-1
Language eng (iso)
Subject recommender systems
recommendation ethics
collaborative filtering
thompson sampling
multi-arm bandits
reinforcement learning
Research Subject Categories::TECHNOLOGY
Included papers
  1. (1) "Comparative Evaluation of Top-N Recommenders in E-commerce: an Industrial Perspective". In proceedings of the 14th International Conference on Machine Learning and Applications (ICMLA), IEEE, 2015. DOI: 10.1109/ICMLA.2015.183.

  2. (2) "Towards an ethical recommendation framework". In proceedings of the 11th International Conference on Research Challenges in Information Science (RCIS), IEEE, 2017. DOI: 10.1109/RCIS.2017.7956539.

  3. (3) "Ensemble Recommendations via Thompson Sampling: an Experimental Study within E-commerce". In proceedings of the 23rd International Conference on Intelligent User Interfaces (IUI), ACM, 2018. In press.

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