Online　reviews　and　comments　after　product　sales　have　become　very　important　for　making　buying　and　selling　decisions.　Fake　reviews　will　affect　such　decisions　due　to　deceptive　information,　leading　to　financial　losses　for　the　consumers.　Identification　of　fake　reviews　has　thus　received　a　great　deal　of　attention　in　recent　years.　However,　most　websites　have　only　focused　on　dealing　with　problematic　reviews　and　comments.　Amazon　and　Yelp　would　only　remove　possible　fake　reviews　without　questioning　the　sellers　who　could　continue　posting　deceptive　reviews　for　business　purposes.　In　this　paper,　we　propose　a　method　for　the　detection　of　fake　reviews　based　on　review　records　associated　with　products.　We　first　analyze　the　characteristics　of　review　data　using　a　crawled　Amazon　China　dataset,　which　shows　that　the　patterns　of　review　records　for　products　are　similar　in　normal　situations.　In　the　proposed　method,　we　first　extract　the　review　records　of　products　to　a　temporal　feature　vector　and　then　develop　an　isolation　forest　algorithm　to　detect　outlier　reviews　by　focusing　on　the　differences　between　the　patterns　of　product　reviews　to　identify　outlier　reviews.　We　will　verify　the　effectiveness　of　our　method　and　compare　it　to　some　existing　temporal　outlier　detection　methods　using　the　crawled　Amazon　China　dataset.　We　will　also　study　the　impact　caused　by　the　parameter　selection　of　the　review　records.　Our　work　provides　a　new　perspective　of　outlier　review　detection　and　our　experiment　demonstrates　the　effectiveness　of　our　proposed　method.　©　1973-2011　IEEE.