Music Recommendation By Using Collaborative Filtering And Cuckoo Search

Abstract

In the modern era, the large amount of information has been generated from a lot of resources, such as shopping, entertainment, advertisement, ... on the Internet. Therefore, the search tools like Google, Bing, ...always use the priority of each item to show what is needed first. It makes a great effectiveness for user experience - reducing waste time for making decisions. On the other hand, entertainment companies want to take the attention of all people for increasing their customers. It is necessary to have a recommender system solve this problem. By discovering users’ behavior with suitable algorithms, a recommender system can recommend the most appropriate items for each user. This thesis proposes a API for each user when they listen a song, it will handle and calculate similarity number then predict the group of users.