We　propose　a　novel　pairwise　unified　recommendation　model　(short　for　pairwise　URM).　The　pairwise　URM　combines　two　pairwise　ranking-oriented　collaborative　filtering　approaches,　namely　Collaborative　Less-is-More　Filtering　(CLiMF)　and　Bayesian　Personal　Ranking　(BPR).　By　sharing　common　latent　features　of　users　and　items　in　BPR　and　CLiMF,　the　pairwise　URM　can　benefit　from　the　two　methods　to　improve　recommendation　qualities.　The　experimental　evaluation　is　conducted　on　two　real-world　datasets　with　different　scales　and　demonstrates　the　positive　effect　of　the　performance　of　the　pairwise　URM.