We approximate it using three factors, specifying soft membership of products and users (Ding et al., 2006) by finding: where F ∈ R nt×K represents the posterior probability of K topic clusters for each product; S ∈ R K×K encodes the distribution of each topic k; and T ∈ R K×nu indicates the posterior probability of K topic clusters for each user.
of matrix factorization, we directly obtain the probability of each user on each topic from the person-topic matrix T .