Abstract: In the era of internet has embarked on World Wide Web for searching or knowing any information about any topic which has resulted in humongous information load. Access to open source creates ever-growing content on web, where user will succumb to thousands of arrays of options available, when seeking for any product or services. As the entertainment industry is no way an exception, optimization of user’s choice is of utmost importance.
Recommendation system (RS) is the application that provides decision tool for tracking the user’s previous choices about products or browsing web pages history, clicks and choices of similar users. The main purpose of RS is to support the user to select his desired product among the multiple, equally-competitive choices available. It can be employed for diverse set of item recommendation such as books, songs, movies, restaurants, gadgets, e-learning materials etc. This chapter attempts to explore the various techniques and reveals the human–machine cooperation by implementing recommendation systems, especially meant for entertainment industry. In summary, the essence of this treatise is the soft computing/knowledge engineering approaches (such as filtering techniques, machine learning algorithms) guided by internet of things to provide prediction that can be displayed by data visualization in entertainment landscape convincingly.
Source: Lecture Notes on Data Engineering and Communications Technologies, Vol. 32.