Top-N　recommendation　tasks　aim　to　solve　the　information　overload　problem　for　users　in　the　information　age.　As　a　user＇s　decision　may　be　affected　by　correlations　among　items,　we　incorporate　such　correlations　with　the　user　and　item　latent　factors　to　propose　a　Poisson-regression-based　method　for　top-N　recommendation　tasks.　By　placing　priori　knowledge　and　using　a　sparse　structure　assumption,　this　method　learns　the　latent　factors　and　the　structure　of　the　item-item　correlation　matrix　through　the　alternating　direction　method　of　multipliers　(ADMM).　The　preliminary　experimental　results　on　two　real-world　datasets　show　the　improved　performance　of　our　approach.