Recommendation Systems Different Techniques, Challenges and Future Directions

As a major research interest, the Recommender Systems (RS) has evolved to help consumers locate products online by offering recommendations that closely fit their interests. This article presents a comprehensive study of accomplishments and the future direction in the field of Recommender Systems. It was thought that helping users cope with the issue of data overload was the original role of information retrieval systems or search engines, but what separates suggested systems from the existing search engines is the requirements for personalized useful and interesting. The "intelligence" aspect is what suggests more interesting and useful. Intelligence is one of the main routes of personalization to know the interests of the user, anticipate the unknown favorites of the user, and eventually provide suggestions by matching the question and the content beyond a basic search. This analysis has resulted in many important results, which will allow current and the next generation researchers of RS to evaluate and set the roadmap of their research in this field.