The major application areas of reinforcement learning (RL) have traditionally been game playing and continuous control. In recent years, however, RL has been increasingly applied in systems that interact with humans. RL can personalize digital systems to make them more relevant to individual users. Challenges in personalization settings may be different from challenges found in traditional application areas of RL. An overview of work that uses RL for personalization, however, is lacking. In this work, we introduce a framework of personalization settings and use it in a systematic literature review. Besides setting, we review solutions and evaluation strategies. Results show that RL has been increasingly applied to personalization problems and realistic evaluations have become more prevalent. RL has become sufficiently robust to apply in contexts that involve humans and the field as a whole is growing. However, it seems not to be maturing: the ratios of studies that include a comparison or a realistic evaluation are not showing upward trends and the vast majority of algorithms are used only once. This review can be used to find related work across domains, provides insights into the state of the field and identifies opportunities for future work.
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and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ?
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Based on the Sustainable Development Goals introduced by the United Nations and on the circular economy concept, ICT providers are adapting to become more sustainable. Some assess the CO2 emissions in the whole life cycle, while others propose to use renewable energies during manufacturing and assembling. In contrast with the current smartphone business models that rely on ongoing patterns of production and consumption, this paper proposes a more sustainable approach by combining product modularity, Product-Service Systems (PSS), and design for attachment. With a modular design, it becomes easier to repair the product or to replace parts, allowing for an extended lifespan. In combination with PSS, we propose that the modules, when no longer used by one customer, return to the market to be reused by another one. Lastly, we discuss the impact of the users’ behaviour through emotional bond, personalization, and technology appropriation as predictors of attachment to the product and consequently an extended lifespan. Through comparing case studies and using Life Cycle Assessment to calculate the CO2 equivalent emissions, we argue that our approach would directly reduce the environmental impact of the smartphone on the production phase, which accounts for most of the emissions throughout its life cycle.
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