| dc.description.abstract |
The tremendous growth in the amount of
attention and users, on social networking sites (SNSs), has
led to information overload and that adds to the difficulty
of making accurate recommendations of new friends to the
users of SNSs. This article incorporates collaborative filtering
(CF), the most successful and widely used filtering
technique, in social networks to facilitate users in exploring
new friends having similar interests while being connected
with old ones as well. Here, first we design an implicit
rating model, for estimating a user’s affinity toward his
friends, which uncover the strength of relationship, utilizing
both attribute similarity and user interaction intensity.
We then propose a CF-based framework that offers list of
friends to the user by leveraging on the preference of likeminded
users, with a given small set of people that user has
already labeled as friends. Despite the immense success of
CF, accuracy and sparsity are still major challenges,
especially in social networking domain with a staggering
growth having enormous number of users. To address
these inherent challenges, first we have explored the idea
of adaptive similarity computation between users by
employing evolutionary algorithms to learn individual
preferences toward particular set of attributes that results in
considerable improvement in recommendation accuracy as
compared to the situation where all the attributes are given
equal importance. |
en_US |