The　explosion　of　intelligent　mobile　applications　has　generated　an　unprecedented　increase　in　the　demand　for　diverse　social　content　access.　Device-to-device　(D2D)　communication,　which　enables　direct　transmission　of　content,　can　partly　support　these　needs　by　reusing　cellular　spectrum　resources.　Most　existing　works　focus　on　mitigating　interference　in　D2D　communication　only　in　physical　space.　However,　the　effect　of　user　content　preference　on　interference　in　social　space　is　ignored.　In　this　article,　a　satisfied　matching-embedded　Social　Internet　of　Things　(IoT)　is　proposed　for　content　preference-aware　resource　allocation　(SIoT-RA).　The　Social　IoT　architecture　is　explored　for　user　social　characteristic　analysis　scenarios　to　ensure　credible　data　management.　Specifically,　the　user＇s　content　preference　characteristics　are　modeled　as　the　probability　of　selecting　similar　content　in　the　social　IoT　by　employing　the　Dirichlet　process.　A　satisfied　matching-based　model　is　proposed　and　embedded　into　the　architecture　as　the　core　algorithm　to　achieve　content　preference-aware　resource　allocation.　This　design　not　only　helps　to　reduce　interference　from　the　perspective　of　social　space　but　also　realizes　maximized　and　satisfactory　resource　allocation　performance　by　a　matching　algorithm　that　can　quantify　satisfaction　as　perceived　utility.　Extensive　simulation　results　demonstrate　the　significant　performance　gains　and　high　cost　effectiveness　of　the　proposed　core　algorithm　of　SIoT-RA　in　terms　of　the　weighted　sum　data　rate　and　users　matching　satisfaction.