Personalized Recommendation System For Leisure Time Activity Using Social Media Data

Abstract

In today’s digital world wherever there’s associate degree endless variety of content to be consumed like books, videos, game, movies, music, etc., finding the content of one’s interest has become associate degree deadening task. A personalized recommendation system for leisure activities is vital in our social life due to its strength in providing enhanced entertainment. Our system has the ability to recommend leisure time activities, to a new user and others by using their social media data. It gathers all the important information, such as popularity, liking and disliking, required for recommendation. It also takes a minimum of new user information without connecting to social networks. It generates recommendations for the user based on his/her behaviour on social media. Such a system will counsel a group of films, books, music, TV shows, games and places to users supported their interest and private data using Collaborative filtering and Content based filtering. Similarity, index is measured by using Pearson correlation and Cosine based similarity and Tanimoto Coefficient based Similarity. The planned system has the flexibility to advocate leisure activity to a brand new user furthermore because the others by mistreatment social media knowledge. It effectively reduces the complexity of the search space for users and attracts more and more users to the Internet, which increases the profits of site owners.