In　the　past　few　decades,　＇explicit＇　and　＇implicit＇　feedback　information　in　recommendation　systems　have　been　researched　as　two　separate　areas.　But,　in　the　real　world,　user　behaviors　contain　a　series　of　explicit　and　implicit　information,　ranging　from　clicks,　collections　to　purchases　and　ratings.　It　is　still　an　unsolved　problem　of　how　to　effectively　use　abundant　implicit　signals　to　help　predict　more　accurate　explicit　signals　and　thus　generate　more　appropriate　recommendations.　Therefore,　we　propose　a　multiple　monotonic　behavior　chains　model　(MC-Rec),　which　can　simulate　the　full　range　of　interactions.　The　Adaboost　algorithm　is　used　to　set　the　weights　of　each　behavior　chain,　and　the　monotony　of　the　behavior　chain　is　lucubrated.　The　experimental　results　are　evaluated　in　three　real　data　sets,　and　demonstrate　that　MC-Rec　model　can　effectively　improve　the　prediction　accuracy　compared　with　the　state-of-the-art　baselines.　©　2019　IEEE.