| dc.description.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. | en_US |
