Existing social networking sites like Facebook,
Google+ etc. recommend friends to their users supported by
their tastes and people they already perceive, that can`t
replicate users’ reality preferences on friend selection. In
this paper, we present a life style primarily based on friend
recommendation system for social networks that
recommends friends to users supporting their life designs
rather than social graphs. By exploiting sensor-rich
smartphones, this technique makes an endeavor to derive
life kinds of users by exploitation data obtained from sensors
that's terribly user-centric. It, in addition measures the
similarity of life styles between users, and recommends
friends to users if their life styles have high similarity. It
permits users to talk with friends. Galvanized by text
mining, we tend to model a user’s manner as life documents;
from that his/her life designs area unit are extracted by
exploitation the Latent Dirichlet Allocation formula. We
tend to additional propose a similarity metric to live the
similarity of life designs between users, and calculate user’s
impact in terms of life styles with a friend-matching graph.
Upon receiving asking, system returns an inventory of
people with highest recommendation scores to the question
user. Finally, this model additionally integrates a feedback
mechanism to boost the recommendation accuracy and users
satisfaction.
Pratidnya K. Gorade : Student of B.E Computer
Engineering, PGMCOE, Wagholi. Savitribai Phule Pune University.
Jyoti R. Diwate : Student of B.E Computer
Engineering, PGMCOE, Wagholi. Savitribai Phule Pune University.
Jagdish K Khetre : Student of B.E Computer
Engineering, PGMCOE, Wagholi. Savitribai Phule Pune University.
Achyut Walse : Student of B.E Computer
Engineering, PGMCOE, Wagholi. Savitribai Phule Pune University.
Data Mining
Friend Recommendation
Sensors
Android
Smartphones
Machine Learning
Lifestyle
In this paper, we presented the design of the Friend
recommendation through linguistics primarily based
matching and cooperative filtering System in social
networks. That is totally different from the friend
recommendation mechanisms wishing on social graphs in
existing social networking services, this recommendation
system takes the user connected knowledge collected from
user and by exploitation that knowledge we tend to create
the friend match graph and by exploitation that graph we
tend to suggested potential friends to users if they share
similar life styles? We tend to conjointly acquire the
feedback and question from the user relating to sure issue
in order that we are able to resolve the matter. We tend to
conjointly obtain the feedback from the user regarding our
suggested system. We tend to enforce our suggested
system on the Android-based smart-phones, and evaluated
its performance on each small-scale experiment. The
results showed that the recommendations accurately
replicate the preferences of users in selecting friends.
Beyond the present model, the longer term work will be
four-fold.
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