# Towards Cognitive Recommender Systems
## 2 Background and Related Work
### Classic Recommender Systems
#### 2.1.1 Single-Domain Recommenders  
The majority of RSs belong to the single-domain recommenders category, which recommends items related to a single domain. For instance, Netflix ([[https://www.netflix.com/](https://www.netflix.com/)]([https://www.netflix.com/](https://www.netflix.com/))) suggests movies and TV series and Last.fm ([[https://www.last.fm/](https://www.last.fm/)]([https://www.last.fm/](https://www.last.fm/))) offers recommendations for songs and music albums. Single-domain Recommender Systems can be divided into four main categories--(i) Content-based: they use the past preferred items' descriptions for recommendation process; (ii) Collaborative Filtering; they look in like-minded people to predict users' preferences; (iii) Demographic-based: they identify demographic features of users; and (IV) Hybrid: they combine some of the previous methods in a single model. Figure 1: Three main Dimensions of a Cognitive Recommender System—(i) knowledge-driven [15]— which enables mimicking the knowledge of domain experts using crowdsourcing techniques; (ii) data-driven [14]: which enables leveraging Artificial Intelligence and Machine Learning technologies to understand the Big Data generated on Open, Private and Social platforms/systems to improve the accuracy of recommendations; and (iii) cognition-driven [17]: which enables understanding the end-users personality and Analyze their behaviour and attitude over time. **Content-based. ** This category of RSs have their roots in information retrieval [23]. In this category, a RS learns to recommend items, which its content are similar to those liked by the users in the past. Content-based approaches mostly rely on analyzing items' contents and users' profiles in order to recommend items that are matched with users' interest. One of the most common methods in this class is Term Frequency/Inverse Document Frequency (TF-IDF), which is a well-known measure in information retrieval [18]. **Collaborative Filtering-based (CF)**. The pure CF RSs have a better performance compared to content-based approaches since they can make a recommendation regardless of the content of user/item despite content-based approaches which highly depend on the descriptions of users and items for making a prediction [24]. The core principle behind these techniques is that among all users in Online Social Networks (OSNs), those who have similar tastes in the past are more likely to share similar interests in the future. CF methods can be generally categorized into two main classes: memory-based and model-based approaches. Memory-based approaches are also known as Neighbourhood-based, since it looks for the best neighbourhood users who share similar interest [1]. This similarity can be computed either between users (i.e., user-based) or items (i.e., item-based). Figure 2: An overview of Recommender Systems’ related work. User-based CF approaches predict users' ratings on an unseen item using similarity metrics such as the Pearson correlation or the cosine similarity [25]. The item-based CF approaches recommend items which are similar to those that users have preferred them before. In contrast to memory-based algorithms, model-based methods try to learn users' preferences by a user-item matrix. Matrix Factorization (MF) as one of the most popular methods in this class shows superior performance compared to the memory-based approaches, which factorizes user-item interaction matrix in latent features of the rating [2]. **Demographic-based**. \"Demographic information can be used to identify the types of users that like a certain object\"[26]. This type of RSs employ users' demographic features, for example, age, nationality, gender and level of education to categorize users and make a better recommendation. If we take age as an example, a set of users who are under 20 years old may have a similar interests on specific items.