# Towards Cognitive Recommender Systems
## 1 Introduction  
A Recommender System (RS), that is, a program which attempts to recommend the most suitable products/services to a user, aims at providing personalized services by retrieving the most relevant information and services from the big data generated on open, private, social and IoT (Internet of Things) data islands [1; 2; 3; 4; 5]. Traditionally, Recommender Systems have been recognized as playlist generators for video/music services (e.g., Netflix ([[https://www.netflix.com/](https://www.netflix.com/)]([https://www.netflix.com/](https://www.netflix.com/))) and Spotify ([[https://www.spotify.com/](https://www.spotify.com/)]([https://www.spotify.com/](https://www.spotify.com/)))), e-commerce product recommenders (e.g., Amazon ([[https://www.amazon.com/](https://www.amazon.com/)]([https://www.amazon.com/](https://www.amazon.com/))) and eBay ([[https://www.ebay.com/](https://www.ebay.com/)]([https://www.ebay.com/](https://www.ebay.com/)))), or social content recommenders (e.g., Facebook ([[https://www.facebook.com/](https://www.facebook.com/)]([https://www.facebook.com/](https://www.facebook.com/))) and Twitter ([[https://twitter.com/](https://twitter.com/)]([https://twitter.com/](https://twitter.com/)))). Today, almost every organization leverages Recommender Systems to better understand their customers and to suggest their products and services. For example, in financial sectors, Recommender Systems are used to suggest novel products from the long-tail region; or in governments, Recommender Systems aim to personalize the advertisements in elections or improve customer relationship management. A Recommender System is a subclass of information filtering system that seeks to predict the rating/preference a user would give to an item, where the goal is to predict a user's interest, narrow down the set of choices, and mitigate the information overload problem [1; 2; 3; 4; 5]. Recommender Systems are vital in business processes as they play an important role in today's electronic markets, and helping businesses to understand their customers' needs and preferences [6]. Recommender Systems in modern enterprises are highly data-driven and rely on users' cognitive aspects such as personality, behavior, and attitude. For example, let us focus on a motivating scenario in financial sectors, where most existing Recommender Systems employ the collaborative or community-based approach--which makes it difficult to recommend products to new customers who have no preference at all (i.e., cold-start problem). Complementary to these conventional Recommender Systems, Sequential Recommender Systems [3; 7; 8; 9] put the first step towards understanding and modeling the sequential user behaviors, the interactions among users and items, and the evolution of users' preferences and item popularity over time. However, Sequential Recommender Systems do not consider cognitive science (analysis of Personality, Behaviour and Attitude over time) and cognitive computing (encompass machine learning, natural language processing and Crowdsourcing) as first-class citizens. In this context, advances in cognitive technology combined with evolutionary progress in areas such as Artificial Intelligence [10] (including Machine Learning, Deep Learning and Natural Language Processing), knowledge representation [11], user experience technologies [12] and crowdsourcing [13], have the potential to transform Recommender Systems in a fundamental way. In this paper, we survey and summarize previously published studies in Recommender Systems to help readers understand our method's contributions to the field in this context.