This work explores an unsupervised approach for modelling players of a 2D cube puzzle game with the ultimate goal of customising the game for particular players based solely on their interaction data. To that end, user interactions when solving puzzles are coded as images. Then, a feature embedding is learned for each puzzle with a convolutional network trained to regress the players' completion effort in terms of time and number of clicks. Next, the known bag-of-words technique is used at two levels. First, sets of puzzles are represented using the puzzle feature embeddings as the input space. Second, the resulting first-level histograms are used as input space for characterising players. As a result, new players can be characterised in terms of the resulting second-level histograms. Preliminary results indicate that the approach is effective for characterising players in terms of performance. It is also tentatively observed that other personal perceptions and preferences, beyond performance, are somehow implicitly captured from behavioural data. CCS CONCEPTS• Computing methodologies → Machine learning; • Information systems → Personalization; • Human-centered computing → User models.