## 2 Background
### Recommendation Systems  
As mentioned previously, the need for RSs was initially realized when platforms like Amazon and Netflix wanted to predict the interests of their users based on their historical data to make their platforms more attractive to their users as well as become more profitable. The development of these systems started in 2009. As discussed by [9] in a detailed article on the research trends on RSs, 2009-2012 was the stable period that saw the initial development of RSs. After the stable period, the rapid growth period started, which saw an increase in publications related to RSs. Next came the outbreak period (2016-2017), which was accompanied by the period where Artificial Intelligence (AI) came back into the spotlight. The increase in the popularity of AI can be considered a reason behind the outbreak period of RSs. From 2019 onward, studies related to RSs started to focus on energy efficiency in buildings. Meanwhile, in the domain of energy efficiency, most studies focused on the technological aspect of energy saving through monitoring [10]. The shift toward focusing on user behavior became important when occupant participation and behavioral change was identified as an important factor [11] for improving energy efficiency in buildings. Context-aware RSs became a great option in this regard because they could take into consideration human behavior, AI, and human decision-making processes [12]. This resulted in the use of RSs and AU in the domain of energy efficiency and buildings. As more and more work was conducted in the domain of RSs, various algorithms were used and found to produce results for varying scenarios. An extensive literature survey [13] presented a taxonomy of RSs that have been developed so far with classifications in terms of purpose, algorithms, and computing platforms. In terms of purpose, there are two main categories of RSs. One is strategy-based and the other is action-based. Strategy-based RSs provide an all-encompassing or general strategy toward a given challenge. The authors in [14] developed an RS for energy management in buildings which suggested reductions in energy based on previous cases for similar buildings. The suggested reduction in energy consumption could be used in developing a strategy for energy management in buildings. Action-based RSs are associated with the daily needs and life of the end user. Their objective was to give daily recommendations to end users based on past data as well as energy-efficiency principles. Finally, the authors in [15] developed an RS that gave suggestions for energy savings to its end user. Moving toward algorithms, there are various algorithms found in the literature that are used to build an RS. Case-based RSs are one of the most common RSs. In common product-based applications of RSs, case-based RSs find similarities between distinct product features like color, price, and make. In the context of energy-efficiency RSs, these product features are replaced by the user behavior linked with the actions at certain time stamps that allow for energy saving. So, using past data [14], the algorithm suggests energy-savingmeasures. To identify similar behaviors at specific time stamps, the K-Nearest Neighbor (KNN) approach can be used. Similarly, pattern-mining algorithms [16] can also be used for creating appliance usage profiles that form a correlation with time stamps. This way, when a behavior that is different from one of the profiles is detected, a notification is provided to the user. The second most popular is collaborative filtering. Collaborative filtering is different from content-based filtering in a way that it takes into account a group of users and does not specifically focus on the content. In the context of a movie RS, it works by focusing on similar movies watched by different people in a group and then recommends a movie to some people in the group who have already been observed by other people in the same group.