Recommender　systems　have　been　widely　used　in　our　life　in　recent　years　to　facilitate　our　life.　And　it　is　very　important　and　meaningful　to　improve　recommendation　performance.　Generally,　recommendation　methods　use　users＇　historical　ratings　on　items　to　predict　ratings　on　their　unrated　items　to　make　recommendations.　However,　with　the　increase　of　the　number　of　users　and　items,　the　degree　of　data　sparsity　increases,　and　the　quality　of　recommendations　decreases　sharply.　In　order　to　solve　the　sparsity　problem,　other　auxiliary　information　is　combined　to　mine　users＇　preferences　for　higher　recommendation　quality.　Similar　to　rating　data,　review　data　also　contain　rich　information　about　users＇　preferences　on　items.　This　paper　proposes　a　novel　recommendation　model,　which　harnesses　an　adversarial　learning　among　auto-encoders　to　improve　recommendation　quality　by　minimizing　the　gap　of　the　rating　and　review　relation　between　a　user　and　an　item.　The　empirical　studies　on　real-world　datasets　show　that　the　proposed　method　improves　the　recommendation　performance.