Collaborative　Web　search　improves　search　quality　by　users＇　working　in　cooperation　and　is　a　subset　of　social　search.　Current　Web　browsers　and　search　engines　provide　limited　support　for　it.　However,　it　is　easier　for　experts,　who　are　familiar　with　some　topics,　to　fulfill　they　needs　through　search　engines　due　to　their　backgrounds,　domain　knowledge　and　so　on.　A　sharing　experts＇　experiences　approach　should　be　struck　based　on　today＇s　Web　browsers　and　major　search　engines.　This　paper　presents　a　convenient　way　for　users　to　share　and　utilize　experts＇　experiences　through　a　Web　browser　toolbar　for　collaborative　Web　search.　The　toolbar　can　catch　search　histories　and　favorites　and　display　recommendations　for　every　user　in　a　popular　Web　browser　through　integrating　with　mainstream　search　engines　like　Google,　Yahoo!,　et　al.　These　collected　users＇　data　are　uploaded　to　a　recommendation　server,　in　which　recommendations　are　built　according　to　some　rules　based　on　an　utilizing　experts＇　experiences　approach.　The　toolbar　can　download　some　valuable　recommendations　merging　into　default　search　list　for　prompting　a　searcher.　The　core　of　our　proposed　approach　is　a　scalable　method　to　measure　＇to　what　degree　a　user　is　an　expert＇　for　a　given　topic　and　to　detect　an　expert＇s　experiences　based　on　a　hierarchical　user　profile.　Experiments　showed　that　the　novel　collaborative　Web　search　way　is　acceptant　to　users　and　experts＇　experiences　improved　search　quality　when　compared　to　standard　Google　rankings.　More　importantly,　results　verified　our　hypothesis　that　a　significant　improvement　on　search　quality　can　be　achieved　by　utilizing　experts＇　experiences.　©　2010　IEEE.