The　social　recommender　system　can　accurately　recommend　information　to　users,　according　to　their　interests　based　on　the　characteristics　of　their　social　network,　however,　the　interaction　between　users　has　not　been　fully　captured　in　the　existing　social　recommender　systems.　This　study　contributes　to　the　literature　by　proposing　a　social　recommendation　method　on　the　basis　of　opinion　dynamics,　which　captures　the　information　on　the　interactions　between　target　users　and　opinion　leaders.　In　our　model,　the　impact　of　opinion　leaders　and　the　evolutionary　opinion　dynamics　between　opinion　leaders　and　the　target　user　are　integrated　to　make　a　recommendation.　Experiments　based　on　two　real　rating　datasets,　Epinions　and　FilmTrust　were　conducted　to　test　the　proposed　model.　The　results　show　that　our　proposed　method　can　effectively　solve　the　cold-start　problem　and　outperforms　the　baseline　models.