Denote t m ∈ R D×1 as the term distribution of domain m, where t m w is the probability of term w appearing in domain m. Then the similarity between domains m and n is formulated as: where t = 1 2 (t m + t n ) is the average distribution, and D KL (•, •) is the Kullback-Leibler divergence: We can verify that D JS (t m , t n ) ∈ [0, 1].
the range of T ermSim(m, n) is also [0, 1].