Outdoor sport climbing in Northern Italy attracts climbers from around the world. While this country has many rock formations, it offers enormous possibilities for adventurous people to explore the mountains. Unfortunately, this great potential causes a problem in finding suitable destinations (crags) to visit for climbing activity. Existing recommender systems in this domain address this issue and suggest potentially interesting items to climbers utilizing a content-based approach. These systems understand users’ preferences from their past logs recorded in an electronic training diary. At the same time, some sports people have a behavioral tendency to revisit the same place for subjective reasons. It might be related to weather and seasonality (for instance, some crags are suitable for climbing in winter/summer only), the users’ preferences (when climbers like specific destinations more than others), or personal goals to be achieved in sport (when climbers plan to try some routes again). Unfortunately, current climbing crags recommendations do not adapt when users demonstrate these repetitive behavior patterns. Sequential recommender systems can capture such users’ habits since their architectures were designed to model users’ next item choice by learning from their previous decision manners. To understand to which extent these sequential recommendations can predict the following crags choices in sport climbing, we analyzed a scenario when climbers show repetitious decisions. Further, we present a data set from collected climbers’ e-logs in the Arco region (Italy) and applied several sequential recommender systems architectures for predicting climbers’ following crags’ visits from their past logs. We evaluated these recommender systems offline and compared ranking metrics with the other reported results on the different data sets. The work concludes that sequential models obtain comparably accurate results as in the other studies and have the prospect for climbers’ subsequent visit prediction and crags recommendations.