Retailers worldwide have started deploying smart service innovations in their stores to regain market share lost to online competitors. Against this backdrop, this paper focuses on the design of product recommendation systems for fashion stores. Our research particularly aims at answering the issues of whether and to what extent (i) the sensing capabilities of smart fashion retail environments and (ii) the integration of contextual information can improve the quality of such recommendations. To this end, we consider smart fitting rooms with the ability to detect products and customers as a showcase; a transaction dataset from a leading German fashion retailer; and contextual information about the time of purchase, the store type, and the weather conditions. Our preliminary analyses indicate that sensor information regarding garment and user identification, as well as further context data help to improve product recommendations in fashion stores.