Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems’ performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors’ knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.

近年来，伴随着物联网（IoT）和人工智能（AI）技术的发展，推荐系统得到了长足的发展。因此，作为物联网和人工智能的结果，这些系统中包含了多种形式的数据，例如社交，隐式，本地和个人信息，这可以帮助改善推荐系统的性能并扩展其遍历不同学科的适用性。另一方面，建筑领域的能源效率正成为研究的热点，其中推荐系统通过促进节能行为和减少碳排放发挥重要作用。但是，在建筑物中部署建议框架仍然需要进行更多调查，以识别当前的挑战和问题，他们的解决方案是使研究结果无处不在的关键，因此，可以确保大规模采用该技术。因此，通过作者的知识，本文通过以下方式为能源效率推荐系统提供了第一个及时，全面的参考：（i）调查建筑物的现有节能推荐系统；（ii）讨论其演变；（iii）根据指定的标准，包括推荐引擎的性质，其目标，计算平台，评估指标和激励措施，提供这些系统的原始分类法；（iv）进行深入，关键的分析，以确定其局限性和未解决的问题。