With　the　rapid　development　of　the　information　technologies　in　the　financial　field,　extracting　meaningful　information　from　a　massive　amount　of　data　is　hugely　significant　for　efficient　business　decision　making.　The　recommendation　system　is　an　intelligent　system　that　applies　historical　knowledge　of　users　to　infer　their　preferences　and　make　a　personalized　recommendation.　However,　it　suffers　from　the　problem　of　time　effect　of　user＇s　behaviour,　which　means　a　user＇s　interests　may　change　over　time.　To　overcome　this　problem,　we　propose　a　time　effect　based　collaborative　filtering　approach　to　adaptively　statistics　the　change　of　user　preferences.　Firstly,　Item-based　collaborative　filtering　is　used　to　calculate　rating　similarity　between　items.　Since　an　Item-based　collaborative　filtering　algorithm　doesn＇t　consider　the　time　effect;　next,　the　time　decay　function　is　proposed　to　statistics　the　change　of　user　interests.　Experimental　results　show　that　the　proposed　scheme　retained　higher　accuracy　compare　to　traditional　collaborative　filtering　method.　©　Published　under　licence　by　IOP　Publishing　Ltd.