Matrix factorization technique has been successfully used in recommender systems. Currently, many variations are developed using this technique, e.g., biased matrix factorization, non-negative matrix factorization, multi-relational matrix factorization, etc. In the context of multi-relational data, this paper proposes another multi-relational approach for recommender systems by including all of the information from latent factor matrices to the prediction functions so that the models have more data to learn. To validate the proposed approach, experiments are conducted on standard datasets in recommender systems. Experimental results show that the proposed approach is promising.