We　study　the　problem　of　recommending　followees　to　users　on　content　curation　social　networks　(CCSNs).　Different　from　existing　friendship-oriented　user　recommendation　approaches,　we　exploit　user　interest　homophily　to　recommend　users　of　similar　interests,　combining　the　users＇　social　network　as　well　as　their　topical　interests.　We　first　profile　users　with　social　links　and　topical　interests　derived　from　＂re-pin　paths＂.　We　further　design　a　collaborative　filtering　strategy　for　user　recommendation　based　on　interest　homophily.　Experiments　on　a　content　curation　social　network　show　that　our　recommendation　algorithm　based　on　user　interest　homophily　performs　better　than　recommendation　based　on　user　popularity.