An apparatus, method and computer program product for generating a recommendation using multi-relational learning comprising receiving first multi-relational data for a first relation, receiving second multi-relational data for a second relation associated with the first relation, unfolding the received first multi-relational data into relational matrices based on a relational graph, obtaining co-clustering, related models of the relational matrices, and generating and displaying the recommendation from the relational matrices and the second multi-relational data. In one aspect, the unfolding is performed using hierarchical nonparametric Bayesian collective matrix factorization. In one aspect, the at least two relational matrices comprise commonly shared object types having the same latent membership across the at least two relational matrices. In one aspect, the unfolding further comprises using matrix factorization on the at least two relational matrices, and performing predictive modeling of each relation of the at least two relational matrices.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for generating a recommendation using multi-relational learning comprising: receiving at a processing device, first multi-relational data for a first relation; receiving at the processing device, second multi-relational data for a second relation associated with the first relation; unfolding the received first multi-relational data into at least two relational matrices based on a relational graph; obtaining co-clustering, related models of the at least two relational matrices; and generating and displaying the recommendation from the at least two relational matrices and the second multi-relational data, in response to a query. 2. The method of claim 1, the unfolding is performed using hierarchical nonparametric Bayesian collective matrix factorization. 3. The method of claim 1, wherein the at least two relational matrices comprise commonly shared object types having the same latent membership across the at least two relational matrices. 4. The method of claim 1, the unfolding further comprising: using matrix factorization on the at least two relational matrices; and performing predictive modeling of each relation of the at least two relational matrices. 5. The method of claim 1, the obtaining co-clustering further comprising: performing cluster analysis and imputing missing values for an entire row or column vector of the at least two relational matrices. 6. The method of claim 1, further comprising: performing interference with Gibbs sampling for the at least two relational matrices. 7. The method of claim 1, further comprising: performing sparse point estimation for the at least two relational matrices. 8. An apparatus for generating a recommendation using multi-relational learning, the apparatus comprising: a memory storage device storing a program of instructions; a processor device receiving said program of instructions to configure said processor device to: receive first multi-relational data for a first relation; receive second multi-relation data for a second relation associated with the first relation; unfold the received first multi-relational data into at least two relational matrices based on a relational graph; obtain co-clustering , related models of the at least two relational matrices; and generate and display the recommendation from the at least two relational matrices and the second multi-relational data. 9. The apparatus of claim 8, wherein the unfold is performed using hierarchical nonparametric Bayesian collective matrix factorization. 10. The apparatus of claim 8, wherein the at least two relational matrices comprise commonly shared object types having the same latent membership across the at least two relational matrices. 11. The apparatus of claim 8, the unfold further comprising: use matrix factorization on the at least two relational matrices; and perform predictive modeling of each relation of the at least two relational matrices. 12. The apparatus of claim 8, the obtain co-clustering further comprising: perform cluster analysis and impute missing values for an entire row or column vector of the at least two relational matrices. 13. The apparatus of claim 8, further comprising: perform interference with Gibbs sampling for the at least two relational matrices. 14. The apparatus of claim 8, further comprising: perform sparse point estimation for the at least two relational matrices. 15. A computer readable storage medium, tangible embodying a program of instructions executable by the computer for generating a recommendation using multi-relational learning, the program of instructions, when executing, performing the following steps: receiving at a processing device, first multi-relational data for a first relation; receiving at the processing device, second multi-relational data for a second relation associated with the first relation; unfolding the received first multi-relational data into at least two relational matrices based on a relational graph; obtaining co-clustering, related models of the at least two relational matrices; and generating and displaying the recommendation from the at least two relational matrices and the second multi-relational data. 16. The computer readable storage medium of claim 15, the unfolding is performed using hierarchical nonparametric Bayesian collective matrix factorization. 17. The computer readable storage medium of claim 15, wherein the at least two relational matrices comprise commonly shared object types having the same latent membership across the at least two relational matrices. 18. The computer readable storage medium of claim 15, the unfolding further comprising: using matrix factorization on the at least two relational matrices; and performing predictive modeling of each relation of the at least two relational matrices. 19. The computer readable storage medium of claim 15, the obtaining co-clustering further comprising: performing cluster analysis and imputing missing values for an entire row or column vector of the at least two relational matrices. 20. The computer readable storage medium of claim 15, further comprising: performing interference with Gibbs sampling for the at least two relational matrices.