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Western consumption patterns are strongly associated with environmental pollution and climate change, which challenges us with transforming our society and consumption towards a sustainable future. This thesis takes up this challenge and aims to contribute to this debate at the intersection of ICT artifacts and social practices through the examples of food and mobility consumption. The social practice lens is employed as an alternative to the predominant persuasive or motivational lens of design in the respective consumption domains. Against this background, this thesis first presents three research papers that contribute to a broader understanding of dynamic practices and their transformation towards a sustainable stable state. The following research takes up these sections' empirical results that more intensely focus on the appropriation of materials and infrastructures utilizing Recommender Systems. Given this approach, this thesis contributes to three fields - practice-based Computing, Recommender Systems, and Consumer Informatics.
Critical consumerism is complex as ethical values are difficult to negotiate, appropriate products are hard to find, and product information is overwhelming. Although recommender systems offer solutions to reduce such complexity, current designs are not appropriate for niche practices and use non-personalized intransparent ethics. To support critical consumption, we conducted a design case study on a personalized food recommender system. Therefore, we first conducted an empirical pre-study with 24 consumers to understand value negotiations and current practices, co-designed the recommender system, and finally evaluated it in a real-world trial with ten consumers. Our findings show how recommender systems can support the negotiation of ethical values within the context of consumption practices, reduce the complexity of finding products and stores, and strengthen consumers. In addition to providing implications for the design to support critical consumption practices, we critically reflect on the scope of such recommender systems and its appropriation.
Machine Learning seems to offer the solution to the central problem in recommender systems: Learning to recommend interesting items from observations. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions from Machine Learning. This article relates the problem of recommendation by user modeling closely to the machine learning problem and explicates some inherent dilemmas. A few examples will illustrate specific approaches and discuss underlying assumptions on the domain or how learned hypotheses relate to requirements on the user model. The article concludes with a tentative 'checklist' that one might like to consider when thinking about to use Machine Learning in User Adaptive environments such as recommender systems.