A Fuzzy Tree Similarity Measure and Its Application in Telecom Product Recommendation

Abstract

The recommender systems field has been well developed in the last few years to provide item recommendations to related users. Existing recommendation approaches, however, assume that an item is described by a single value or a vector. Unfortunately, some items in real world applications, such as telecom products, could have a tree structure. This paper aims to handle this issue by developing a comprehensive fuzzy tree similarity measure. The fuzzy tree similarity measure compares both the concepts and values in two trees of items. The focus of this study is primarily on the fuzzy value similarity between two trees. In the similarity measure, each attribute is associated with a set of linguistic terms to express the value granularly. The node values are first transformed to membership vectors related to the linguistic terms, and the values of the conceptual corresponding nodes are then compared. These local similarities are aggregated into the final fuzzy value similarity between the two trees. A telecom product recommendation case study shows the effectiveness of the proposed fuzzy tree similarity measure and its applicability for telecom product recommendations.

A fuzzy approach for measuring development of topics in patents using Latent Dirichlet Allocation

“…The vector is actually an attribute that associates with a set of linguistic terms to describe the development states of all estimated topics. We know that, in existing research, the type of membership function that is suitable depends on the application context [28,29]. In this research, fuzzy membership functions can be inferred from the analysis of , or they may be determined by domain experts.…”