Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce

Abstract

In memory of a beautiful soul ABSTRACTRecommender systems have become an integral part of virtually every e-commerce application on the web. The deployment of these expert systems has enabled users to quickly discover the products or services they need, at the same time increasing business revenues through better customer conversion. Remaining a very active research field since the mid-2000s, recommender systems have been modeled using a plethora of machine learning techniques. However, the adoptability of these models by industrial e-commerce platforms remains unclear.In this thesis, we assess the receptiveness of industrial platforms to algorithmic contributions of the research community by surveying more than 30 popular shopping cart solutions, and experimenting with various recommendation algorithms on proprietary e-commerce datasets.Another overlooked but important factor that complicates the design and use of recommender systems is their ethical implications. We provide a holistic view of these issues and summarize them in our ethical recommendation framework. This framework suggests new paradigm of ethics-awareness by design, and 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.Since the large part of moral implications stems from user profiling, we investigate algorithms capable of generating useragnostic recommendations based solely on a visited product page. 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.

Recommender systems and their ethical challenges

“…This, the authors predict, could be made achievable through the use of interlinked big data structures. Finally, Paraschakis (2016Paraschakis ( , 2017Paraschakis ( , 2018 provides one of the most detailed accounts. Focusing on e-commerce applications, Paraschakis suggests that there are five ethically problematic areas:…”

“…User privacy is one of the primary challenges for recommendation systems (Friedman et al 2015;Koene et al 2015;Paraschakis 2018). This may be seen as inevitable, given that a majority of the most commercially successful recommender systems are based on hybrid or collaborative filtering techniques, and work by constructing models of their users to generate personalised recommendations.…”

“…This makes it difficult for independent researchers to access information about their internal operations, and hence provide any evidence-based assessment. In the same vein, due to privacy concerns, providers of recommendation systems may be reluctant to share information that could compromise their users' personal data (Paraschakis 2018).…”

Recommender Systems and their Ethical Challenges

“…Finally, (Paraschakis, 2016(Paraschakis, , 2017(Paraschakis, , 2018 provides one of the most detailed accounts.…”

“…User privacy is one of the primary challenges for recommendation systems (Friedman et al, 2015;Koene et al, 2015;Paraschakis, 2018). This may be seen as inevitable, given that a majority of the most commercially successful recommender systems are based on hybrid or collaborative filtering techniques, and work by constructing models of their users in order to generate personalised recommendations.…”