BibTeX:

@article{IJIRSTV3I2054,
     title={Users Activity based Recommendation Systems and Efficient Data Sharing in Social Networking Services},
     author={Lighitha K R, Silja Varghese and Blessy Joy},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={2},
     pages={204--208},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I2054.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Online social networking sites like, Facebook, Google and Twitter are suggested to share their public and personal information and make social relationship or connection with individual or people who can be even strangers. Existing social networking facilities recommends friends to users based on their social graphs, which might not be suitable to reflect a user’s preferences on friend selection in their real life. In this system, human interest based friend recommendation system for social networks, which recommends friends to users based on his/her life styles instead of their social graphs and determine life styles of users from user-centric sensor data and measures the comparison of life styles between users and this scheme recommends friends to users if their way of lifestyle has high similarity. Social networking sites also include sharing of files or data among the users or group of users. Data sharing is not easier and an accurate analysis on the shared data provides more benefits to both the society and individuals. Data sharing with a large number of participants must take into account many issues, that is efficiency, data integrity and privacy of data owner. Also ranking is done based on searching of users profile information. Finally, this system also take part a feedback mechanism to improve the users satisfaction and recommendation accuracy.

Activity Recognition, Lifestyle Modeling, Recommendation System, SNS