Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
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ACM Transactions on Information Systems (TOIS)
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UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.