You are invited to participate in the upcoming special issue on De-Personalisation, Diversification, Filter Bubbles and Search (Springer) (PDF of the Call)
Information retrieval, recommender systems and, more generally, approaches in machine learning have resulted in highly personalised web experiences. Building on context, location and users’ virtual (social) profiles, the web is highly aligned to users’ perceived interests, to the interests of ‘similar’ users, and to the interests of users to whom a user is digitally connected. Whilst this delivers relevant content, it also polarises informational perspectives and removes serendipity through the development of filter bubbles or echo chambers: scenarios where specific ideas, beliefs or data are reinforced through repetition of a closed system that limits the free movement of alternative (competing) ideas. There is the implication that certain ideas or outcomes dominate due to, and resulting in, a bias concerning how specific input is gathered. Search diversification has gained significant attention in information retrieval in recent years as one approach to relax over-focused views on search results and content. However, methods, reviews and evaluations that aim to qualify and quantify personalised experiences and their biasing effects are under-addressed in the literature.
Currently, there is no single source that integrates multidisciplinary research that conceptualises and evaluates the bias that results from continuous filtering and personalisation. We aim to address this gap by accepting a selective set of papers that allows researchers to better understand the influence that personalisation has on information experiences. In this context, we aim to bring together a wide range of views and approaches from information retrieval, information science, cognitive systems, computational social science and machine learning.