Addressing the cold-start problem using data mining techniques and improving recommender systems by Cuckoo algorithm: a case study of Facebook

The popularity of Social networks, user demands, market realities, and technology developments are driving recommendation systems to explore new models of marketing and advertisements. Due to the great bulk of data on social media websites, the process of extracting hidden knowledge from data has become a hectic activity. For achieving this goal data mining techniques have been flourishing to discover interesting knowledge along with recommendation systems to suggest appropriate items to users based on this extracted knowledge. One of the most common obstacles in recommendation systems is a "cold-start" problem, which is related to users who do not indicate any behavior on social media. This paper aims to propose a solution for tackling this problem by using data mining techniques. In the next level, we enhance the recommendation method through Cuckoo algorithm to offer minimum number of items to get maximum feedback from users. Results indicate high performance of our proposed solution.