# Towards Cognitive Recommender Systems
## 2 Background and Related Work
### Classic Recommender Systems
#### 2.1.2 Cross-Domain Recommenders  
Based on how the knowledge is exploited from the source domain, cross-domain RSs can be classified into two different classes--(i) Aggregating knowledge-based, and (ii) Transferring knowledge-based.  
**Aggregating Knowledge-Based**. In this line of research, the knowledge can be obtained from source domains in three different ways _merging user preferences_, _mediating user modelling data_, and _combining recommendations_ in order to make or improve recommendations in a target domain. Among the mentioned approaches, merging user preferences is one of the widely used methods in cross-domain recommenders. Enriching sparse users' preferences data with integrated data from other domains can improve the recommendations even with existence of cold-start and sparsity conditions [30; 31]. The work proposed by [PERSON] et al. [32] is one of the pioneering models in recommendation systems that relies on mediating user modelling data to show the benefit of this technique in improving the recommendation accuracy. Integrating the most similar neighbours can be considered as a type of mediating user modelling to show the performance of recommendation compared with the single-domain RSs [33]. In combining recommendations, the recommendations are combined with their associated weights from the different single domains. The weights are computed based on how important is the recommendation from that source domain to the target domain [34].  
**Transferring knowledge-based**. This category can be classified into three main classes, such as _linking domains_, _sharing latent features_ and _transferring rating patterns_. Linking domains-based approaches try to link domains where they share in common items' attributes. For instance, there may be a connection between comedy movies and humorous books. Though they come from different domains, they may share semantic features [22]. [PERSON] et al. [35] propose a cross-domain RS in which they filter the items attributes that are in common between the source and target domain to make a more accurate recommendation. Their work is among one of the earliest RSs which fall in this category. Latent factor models are one of the widely adopted approaches in collaborative filtering, where the users and items attributes are very sparse [36]. In cross-domain recommendation, transferring the latent features between domains can be done adaptively and collectively. In order to overcome the data sparsity problem in the target domain, an approach is proposed by [PERSON] et al. [37] to adaptively utilize user and item information from the source domain to perform a singular value decomposition (SVD)-based recommendation model in the target domain. While in collectively-based approaches, latent features are learned simultaneously through a single objective function including both domains [38]. Except for mentioned approaches, a different line of research exploits users' rating patterns and assumes that in close domains a sample of users may share similar preferences for a set of items. The work proposed by [PERSON] et al. uses association rules to discover the pattern of a set of commonly rated items in a group of similar users. Then, they integrate these rules into their CF method to make a better recommendation [22].