In　e-commerce　platform,　users　conduct　purchase　behavior　and　write　reviews　for　the　purchased　items.　These　reviews　usually　contain　a　lot　of　valuable　information　for　recommendation,　which　can　reflect　the　purchase　preference　of　the　user　and　the　characteristic　of　the　item.　We　propose　the　Hierarchical　Attention　Cooperative　Neural　Networks　(HACN)　model　for　recommendation.　Hierarchical　attention　mechanism　is　adopted　to　enrich　user＇s　and　item＇s　feature　representation　from　review　texts.　Two　parallel　networks　based　on　review　texts　are　used　to　model　users　and　items　respectively,　which　makes　the　generated　features　more　purposeful.　Further,　the　target　ID　embedding　is　introduced　to　capture　the　global　entity　relationship　in　the　dataset.　The　experiments　are　performed　on　five　real-world　datasets　of　different　domains　from　Amazon,　and　our　proposed　HACN　model　has　achieved　better　results　than　the　existing　state-of-the-art　methods.　©　2021　Elsevier　B.V.