# Towards Cognitive Recommender Systems
## 2 Background and Related Work
### Intelligent Recommenders  
#### 2.5.1 Cognitive-Based Recommenders  
Cognitive computing is a field of study of brain-inspired mechanisms and systems [76]. There are different definitions for cognitive computing and there is no commonly accepted definition despite extensive research in this domain [77]. Some studies defined cognitive computing as \"a collection of emerging technologies inspired by the biological processing of information in the nervous systems, human reasoning, decision making, and natural selection\" [78]. Furthermore, it is also defined as \"an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain\" [78]. Cognitive computing can be used in cloud computing [79], natural language processing [80], emotional cognitive systems with speech and facial expression [81] and big data and machine learning era [82]. Discovering meaningful patterns from users' social behaviours can play a vital role in helping RSs to find the interesting items (e.g, music, job, link, hotel and etc.) to suggest to users before even they start looking for them[PERSON] The main purpose of the cognitive recommenders is to inspire the human brain which consists of two main different dimensions, that is, explicit behavioural pattern and implicit behavioural pattern, for gathering and linking data across domains and assist users' decision-making process. **Explicit Behavioural Pattern**. We categorize methods in this class based on the type of information that they used in their model[PERSON] _(i) Text-based_ methods that can use posts, comments, and reviews and the context of them to enrich their recommender engines. For instance, [PERSON] et al. [83] use customers' reviews to apply sentiment analysis technique and extract some useful features such as emotionalism, reflexivity and addressing to help users with their decision making process. _(ii) Location-based_ approaches such as SPETA can provide tourists with more services and suggestions as it can use the knowledge of a user's current location, preferences, and the history of past locations [84]. Traveller is another location-based method that can recommend holidays packages and tours [85][PERSON] There are more models, Entree for instance, which employ location features to recommend restaurants [86][PERSON] _(iii) Action-based_ such as liking, Rating, Voting can be used in this class of methods. In like/dislike systems, users explicitly express their opinion about the specific items by classifying them as \"interesting\" or \"not interesting\", [PERSON]relevant[PERSON] or \"not relevant\" or as \"like\" or \"dislike\" [87]. Rating scale also scale is another feature which is typically numeric, Amazon bookstore for instance, provide readers to rate the books on a five-point scale to help them with their next interested items. _(iii) Feature-based_ methods which use features such as age, gender and profile can make a better recommendation. Although there are some studies which incorporate user and item profiles into some of the current methods such as Reference [88] and Reference [26], there may need to be more complex and advanced profiling like data mining rules [89], and signatures [90]. **Implicit Behavioural Pattern**. Approaches belong to this category can be roughly categorized into three main classes. The first class uses _social-based_ features such as personality, trust and attitudes. For instance, APAR as a personality-based RSs can implicitly detect users' personality types and integrate it into a matrix factorization model [91]. _Trust-based_ methods is the second line of implicit cognitive RS. Trust which is defined as a context-dependent factor, can help RS to select a small and the best neighbourhood for a user [92].