With　the　rapid　development　of　social　networks,　it　is　important　to　identify　users　with　high　influence.　In　content　curation　social　networks　(CCSNs),　there　are　two　kinds　of　user　relations.　The　one　is　the　explicit　user　relations　from　user＇s　following　behavior.　And　the　other　is　the　content　based　user　relations　from　user＇s　repin　behavior.　Based　on　these　observation,　we　propose　multi　perspective　page　rank　(MPPR)　to　estimate　user　influence.　The　proposed　algorithm　integrates　both　user　relations　to　calculate　influence　scores　of　the　users　automatically.　User　influences　are　computed　based　on　the　transition　matrix　of　following　and　repin　relations.　When　the　iteration　is　convergent,　every　user　will　get　a　fixed　influence　value.　Experiments　on　the　dataset　containing　11990　users,　920610　following　relations　and　39321016　repin　relations　show　that　the　proposed　algorithm　outperforms　the　typical　PageRank　algorithm.