Constructing Topical Concept Hierarchical Taxonomy of Tourist Attraction
Abstract
- A hierarchical co-clustering module by using non-negative matrix tri-factorization for allocating attractions and things of interest to topic when splitting a coarse topic into fine-grained ones.
- A concept extraction module for extracting concept of every topic that maintain strong discriminative power at different levels of the taxonomy.
理论数学表达
Non-negative Matrix Factorization
The model is to approximate the input attraction-ToI matrix with three factor matrices that assign cluster labels to tourist attractions and Things of Interest (ToI) simultaneously by solving the following optimization problem:
MATH0HTAM
where MATH14HTAM is the input attraction-word content matrix, and MATH15HTAM and MATH16HTAM are orthogonal nonnegative matrices indicating low-dimensional representations of attractions and things of interest, respectively. The orthogonal and nonnegative conditions of the two matrices MATH17HTAM and MATH18HTAM enforce the model to provide a hard assignment of cluster label for attractions and things of interest. MATH19HTAM provides a condensed view of MATH20HTAM .
