Matrix　factorization　(MF)　models　and　their　extensions　are　widely　used　in　modern　recommender　systems.　MF　models　decompose　the　observed　user-item　interaction　matrix　into　user　and　item　latent　factors.　In　this　paper,　we　propose　mixture　models　which　combine　the　technology　of　MF　and　the　embedding.　We　show　that　some　of　these　models　significantly　improve　the　performance　over　the　state-of-the-art　models　on　two　real-world　datasets,　and　explain　how　the　mixture　models　improve　the　quality　of　recommendations.