TY  - JOUR
T1  - A Trusted-Community Based Framework for Collaborative Filtering
Recommender Systems
AU - Gorripati, Satya Keerthi AU - Vatsavayi, Valli Kumari 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 22
SP  - 6095
EP  - 6103
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.6095.6103
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.6095.6103
KW  - Trust
KW  -collobarative recommenders
KW  -community detection
KW  -confidence
KW  -framework
KW  -architecture
AB  - Recommender systems support users in the overwhelming task of examining through large quantities
of data in order to select appropriate information or items. Unfortunately such systems may be matter to attack
by spam users who want to operate the system’s recommendations to outfit their needs: to encourage their own
items/services or to originate trouble in the recommender system. Attacks can cause the recommender system
to become untrustworthy and unreliable, resulting in user dissatisfaction. Traditional recommender systems
rely on like-minded neighbors irrespective of their preferences/tastes when computing predictions and assume
users are independent and identically distributed and completely ignore the social activities between users
which are not reliable. In reality people heavily rely on their friend’s recommdations since, social networks
demonstrate a strong community effect. Furthermore, people in cluster/group tend to trust each other and share
common preferences with each other more than those in outside the groups. Based on this intuition in this
framework, architecture of trusted-community recommender system is proposed. User’s preferences expressed
by incorporating trusted neighbors within community of the target user are merged in order to find the similar
preferences. In addition, the worth of merged ratings is measured by the confidence considering the number
of ratings inside the community and the percentage of clashes between negative and positive views. Further,
the rating confidence is incorporated into the computation of user similarity. The prediction for an unrated item
is computed by aggregating the ratings of similar users within community. Experimental results on real-world
data set validate that our method overtakes other complements in terms of accuracy.
ER  -