TY - GEN
T1 - Personalization with dynamic group profile
AU - Taha, Kamal
AU - Elmasri, Ramez
PY - 2012
Y1 - 2012
N2 - In this paper, we propose an XML-based recommender system, called PDGP. It is a type of collaborative information filtering system. PDGP uses ontology-driven social networks, where nodes represent social groups. A social group is an entity that defines a group based on demographic, ethnic, cultural, religious, age, or other characteristics. In the PDGP framework, query results are filtered and ranked based on the preferences of the social groups to which the user belongs. The user's social groups are inferred implicitly by the system without involving the user. PDGP constructs the social groups and identifies their preferences dynamically on the fly. These preferences are determined from the preferences of the social groups' member users using a group modeling strategy. PDGP can be used for various practical applications, such as Internet or other businesses that market preference-driven products. We experimentally compared PDGP with an existing system. Results showed marked improvement.
AB - In this paper, we propose an XML-based recommender system, called PDGP. It is a type of collaborative information filtering system. PDGP uses ontology-driven social networks, where nodes represent social groups. A social group is an entity that defines a group based on demographic, ethnic, cultural, religious, age, or other characteristics. In the PDGP framework, query results are filtered and ranked based on the preferences of the social groups to which the user belongs. The user's social groups are inferred implicitly by the system without involving the user. PDGP constructs the social groups and identifies their preferences dynamically on the fly. These preferences are determined from the preferences of the social groups' member users using a group modeling strategy. PDGP can be used for various practical applications, such as Internet or other businesses that market preference-driven products. We experimentally compared PDGP with an existing system. Results showed marked improvement.
KW - Group profile
KW - Personalization
KW - Recommender system
UR - https://www.scopus.com/pages/publications/84874262459
U2 - 10.1109/ASONAM.2012.83
DO - 10.1109/ASONAM.2012.83
M3 - Conference contribution
AN - SCOPUS:84874262459
SN - 9780769547992
T3 - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
SP - 488
EP - 492
BT - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
T2 - 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Y2 - 26 August 2012 through 29 August 2012
ER -