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Energy-related occupant behaviour in the built environment is considered crucial when aiming towards Energy Efficiency (EE), especially given the notion that people are most often unaware and disengaged regarding the impacts of energy-consuming habits. In order to affect such energy-related behaviour, various approaches have been employed, being the most common the provision of recommendations towards more energy-efficient actions. In this work, the authors extend prior research findings in an effort to automatically identify the optimal Persuasion Strategy (PS), out of ten pre-selected by experts, tailored to a user (i.e., the context to trigger a message, allocate a task or providing cues to enact an action). This process aims to successfully influence the employees’ decisions about EE in tertiary buildings. The framework presented in this study utilizes cultural traits and socio-economic information. It is based on one of the largest survey datasets on this subject, comprising responses from 743 users collected through an online survey in four countries across Europe (Spain, Greece, Austria and the UK). The resulting framework was designed as a cascade of sequential data-driven prediction models. The first step employs a particular case of matrix factorisation to rank the ten PP in terms of preference for each user, followed by a random forest regression model that uses these rankings as a filtering step to compute scores for each PP and conclude with the best selection for each user. An ex-post assessment of the individual steps and the combined ensemble revealed increased accuracy over baseline non-personalised methods. Furthermore, the analysis also sheds light on important user characteristics to take into account for future interventions related to EE and the most effective persuasion strategies to adopt based on user data. Discussion and implications of the reported results are provided in the text regarding the flourishing field of personalisation to motivate pro-environmental behaviour change in tertiary buildings.

Recommendation systems are gaining popularity on Internet platforms such as Amazon, Netflix, Spotify or Booking. As more users are joining these online consumer and entertainment sectors, the profile-based data for providing accurate just-in-time recommendations is rising thanks to strategies based on collaborative filtering or content-based metrics. However, these systems merely focus on providing the right item for the users without taking into account what would be the best strategy to suggest the movie, the product or the song (i.e. the strategy to increase the success or impact of the recommendation). Taking this research gap into consideration, this paper proposes a profile-based recommendation system that outputs a set of potential persuasive strategies that can be used with users with similar characteristics. The case study presented provides tailored persuasive strategies to make office-based employees enhance the energy efficiency at work (the dataset used on this research is specific of this sector). Throughout the paper, shreds of evidence are reported assessing the validity of the proposed system. Specifically, two approaches are compared: a profile-based recommendation system (RS) vs. the same RS enriched by adding an ensemble with an active learning model. The results shed light on not only providing effective mechanisms to increase the success of the recommendations but also alleviating the cold start problem when newcomers arrive.