A similarity measure on tree structured business data

In many business situations, products or user profile data are so complex that they need to be described by use of tree structures. Evaluating the similarity between tree-structured data is essential in many applications, such as recommender systems. To evaluate the similarity between two trees, concept corresponding nodes should be identified by constructing an edit distance mapping between them. Sometimes, the intension of one concept includes the intensions of several other concepts. In that situation, a one-to-many mapping should be constructed from the point of view of structures. This paper proposes a tree similarity measure model that can construct this kind of mapping. The similarity measure model takes into account all the information on nodes&rsquo; concepts, weights, and values. The conceptual similarity and the value similarity between two trees are evaluated based on the constructed mapping, and the final similarity measure is assessed as a weighted sum of their conceptual and value similarities. The effectiveness of the proposed similarity measure model is shown by an illustrative example and is also demonstrated by applying it into a recommender system.<br /

Fuzzy similarity measure model for trees with duplicated attributes

In many business situations, complex user profiles are described by tree structures, and evaluating the similarity between these trees is essential in many applications, such as recommender systems. This paper proposes a fuzzy similarity measure model for trees with duplicated attributes. In this model, the conceptual similarity between attributes and the weights of nodes are expressed by linguistic terms. To deal with duplicated attributes in the trees, nodes with the same concept are clustered. The most conceptual corresponding cluster pairs among two trees are identified. Based on the corresponding cluster pairs, the conceptual similarity and the value similarity between two trees are evaluated, and the final similarity measure is assessed as a weighted sum of their conceptual and value similarities. © 2011 Springer-Verlag Berlin Heidelberg