Existing　studies　have　made　a　great　endeavor　in　predicting　users＇　potential　interests　in　items　by　modeling　user　preferences　and　item　characteristics.　As　an　important　indicator　of　users＇　satisfaction　and　loyalty,　repeat　purchase　behavior　is　a　promising　perspective　to　extract　insightful　information　for　community　e-commerce.　However,　the　repeated　purchase　behaviors　of　users　have　not　yet　been　thoroughly　studied.　To　fill　in　this　research　gap　from　the　perspective　of　repeated　purchase　behavior　and　improve　the　process　of　generation　of　candidate　recommended　items　this　research　proposed　a　novel　approach　called　ReRec　(Repeat　purchase　Recommender)　for　real-life　applications.　Specifically,　the　proposed　ReRec　approach　comprises　two　components:　the　first　is　to　model　the　repeat　purchase　behaviors　of　different　types　of　users　and　the　second　is　to　recommend　items　to　users　based　on　their　repeat　purchase　behaviors　of　different　types.　The　extensive　experiments　are　conducted　on　a　real　dataset　collected　from　a　community　e-commerce　platform,　and　the　performance　of　our　model　has　improved　at　least　about　13.6%　compared　with　the　state-of-the-art　techniques　in　recommending　online　items　(measured　by　F-measure).　Specifically,　for　active　users,　with　w=1　and　NUA　&　ISIN;5,25,　the　results　of　ReRec　show　a　significant　improvement　(at　least　50%)　in　recommendation.　With　alpha　and　sigma　as　0.75　and　0.2284,　respectively,　the　proposed　ReRec　for　unactive　users　is　also　superior　to　(at　least　13.6%)　the　evaluation　indicators　of　traditional　Item　CF　when　NUB　&　ISIN;6,　25.　To　the　best　of　our　knowledge,　this　paper　is　the　first　to　study　recommendations　in　community　e-commerce.