Our model can handle all these relations in the proposed framework, CTR-MGF.
the generative process of CTR-MGF for heterogeneous networks is listed as follows: , multivariate Gauss distribution and set the item latent vector as -For each social graph specific feature m * draw graph factor-specific latent feature vector -draw the shared latent user vector among multiple social graphs In the above generative process, the joint likelihood of data, i.e., R, Q k=1,...,K , W , and the latent factors U, V, S k=1,...,K under the full model is: For learning the parameters, we develop an EM-style algorithm similar to CTR.