Human Aspects of Privacy in Relation to Personalization

AI Overview

This article discusses the human behavioral aspects of privacy in Recommender Systems. It reflects on a seminar about how users' information disclosure choices impact system personalization and how valid "nudging" techniques can help users make better privacy decisions without compromising the benefits of personalization.

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The concept of privacy is inherently intertwined with human attitudes and behaviours, as most computer systems are primarily designed for human-use. Especially in the case of Recommender Systems, which feed on information provided by individuals, their efficacy critically depends on whether or not information is externalized, and if it is, how much of this information contributes positively to their performance and accuracy.

In my seminar on Privacy and Big Data taken at RWTH, I discussed the impact of several factors on users’ information disclosure behaviours and privacy-related attitudes, and how users of recommender systems can be nudged into making better privacy decisions for themselves. Apart from that, I also addressed the problem of privacy adaptation, i.e. effectively tailoring Recommender Systems by gaining a deeper understanding of people’s cognitive decision-making process.

The seminar was taken under Prof. Dr. Stefan Decker at the chair of Databases and Information Systems. The full seminar report can be found here. The presentation for my delivered talk can be accessed here.