# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 2 Analysis of Modern-Day Smart Services
### Recommender Systems  
With the rise of services such as _YouTube_ (see [[https://www.youtube.com/](https://www.youtube.com/)]([https://www.youtube.com/](https://www.youtube.com/)), accessed on 30 August 2022) or large online shopping platforms such as _Amazon_ (see [[https://www.amazon.com/](https://www.amazon.com/)]([https://www.amazon.com/](https://www.amazon.com/)), accessed on 30 August 2022), we have entered an age of oversupply. Users cannot obtain a complete overview of all available items (e.g., video clips or products). Therefore, in all these services and shopping platforms, a mechanism is needed to provide users with information about the items that are particularly relevant to them. Recommender systems represent such a mechanism. In simple terms, a recommender system is a tool that predicts a user's interests based on his or her previous interactions with the service or shopping platform. Such interactions can be, e.g., watching a video clip or buying a product. In this way, information overload can be minimized by presenting a user with only the most relevant information tailored to his or her needs [93]. Although e-resource services such as YouTube or e-commerce platforms such as Amazon are the most prominent uses of recommender systems, such systems have become indispensable in almost all modern e-services [94]. However, if recommender systems would base their suggestions only on the previous interaction of the user in question, no new items could be recommended to him or her. The advantage of these systems is that they not only have knowledge about a single user but can also draw on the interactions of a large number of users. By means of so-called _market basket analysis_, they can determine which combinations of items users frequently interact with, e.g., which video clips a user has on his or her watchlist or which products a user buys at the same time. From such item lists, _association rules_ can be derived that describe which item combinations are encountered regularly. If a user is interested in a subset of the items of an association rule, the remaining items can be recommended to him or her [95]. In Figure 6, it is shown how a recommender system can proceed to be able to make tailored recommendations. Basically, there are two different approaches to this end: _content-based filtering_ and _collaborative filtering_. In content-based filtering, only the items are considered. For this purpose, similar items are clustered. The clusters are homogeneous within themselves--i.e., the items of a cluster are mutually highly similar with regard to the relevant properties--and heterogeneous to each other--i.e., the items of different clusters are as different as possible with regard to the relevant properties. If a user interacts with an item (or has interacted with it in the past), the remaining items of the same cluster can be recommended to the user, since it is likely that they are also of interest to him or her [96]. Yet, this approach completely neglects social aspects. By including knowledge about the users themselves as well as social relationships between them, considerably better recommendations can be made [97]. In collaborative filtering, therefore, users are also clustered, for instance, based on common interests. This has two effects: On the one hand, the recommendation set can be reduced significantly. It is no longer necessary to recommend all similar items but only those that were also popular among other users from the same cluster. On the other hand, new recommendations can also include distinct items if other users from the cluster have interacted with them [98]. In addition to these two main types of recommender systems, however, there is a variety of hybrid approaches that mix aspects of content-based filtering and collaborative filtering [99]. There are two issues in this regard: First, third parties, e.g., content creators or manufacturers of a product, might try to increase the relevance of their items by submitting fake data to be prioritized in the recommendations.