Recommender systems (RS) have actively been implemented in many domains. Control mechanisms have been introduced to make recommendations more personal. However, those mechanisms have not been thoroughly studied in RS literature. This study aims to contribute to this stream of research and focuses on their relationship with the acceptance of RS among users. We utilize technology acceptance model and extend it with user control. By using an experimental setting, we measure the effect of control mechanisms on different aspects of RS acceptance.

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