Since many tourists share the photos they take on social media channels, large collections of tourist attraction photos are easily accessible online. Recent research has dealt with identifying popular places from these photos, as well as computing city tourism routes based on these photo collections. Although current approaches show great potential, many tourism attractions suffer from being overrun by tourists, not least because many tourists are aware of only a few tourism hot spots that are trending. In the worst case, automatic city route recommendations based on social media photos will intensify this issue and disappoint tourists who seek individual experiences. In the best case, however, if individual preferences are appropriately incorporated into the route planning algorithm, more personalized route recommendations will be achieved. In this paper, we suggest distinguishing two different types of photo contributors, namely: first-time visitors who are usually tourists who "follow the crowd" (e.g., to visit the top tourist attractions), and repeated visitors who are usually locals who "don’t follow the crowd" (e.g., to visit photogenic yet less well-known places). This categorization allows the user to decide how to trade the one objective off against the other. We present a novel method based on a classification of photographers into locals and tourists, and show how to incorporate this information into an algorithmic routing framework based on the Orienteering Problem approach. In detailed experiments we analyze how choosing the parameter that models the trade-off between both objectives influences the optimal route found by the algorithm, designed to serve the user’s travel objective and preferences in terms of visited attraction types.