Recommendation　methods　usually　use　users＇　historical　ratings　on　items　to　predict　ratings　on　their　unrated　items　to　make　recommendations.　However,　the　sparse　rating　data　limit　the　recommendation　quality.　In　order　to　solve　the　sparsity　problem,　other　auxiliary　information　is　combined　to　mine　users＇　preferences　for　higher　recommendation　quality.　This　paper　proposes　a　novel　recommendation　model,　which　harnesses　an　adversarial　learning　among　auto-encoders　to　improve　recommendation　quality　by　minimizing　the　gap　of　rating　and　review　relation　of　users　and　items.　The　empirical　studies　on　real-world　datasets　prove　that　the　proposed　method　improves　recommendation　performance.