Online　news　recommendation　systems　aim　to　address　the　information　explosion　of　news　and　make　personalized　recommendations　for　users.　The　key　problem　of　personalized　news　recommendation　is　to　model　users＇　interests　and　track　their　changes.　A　common　way　to　deal　with　the　user　modeling　problem　is　to　build　user　profiles　from　observed　behavior.　However,　the　majority　of　existing　methods　make　static　representations　of　user　profiles　and　little　research　has　focused　on　effective　user　modeling　that　could　dynamically　capture　user　interests　in　news　topics.　To　address　this　problem,　in　this　paper,　we　propose　UP-TreeRec,　a　news　recommendation　framework　based　on　a　user　profile　tree　(UP-Tree),　which　is　a　novel　framework　combining　content-based　and　collaborative　filtering　techniques.　First,　by　exploiting　a　novel　topic　model　namely　UI-LDA,　we　obtain　the　representation　vectors　for　news　content　in　a　topic　space　as　the　fundamental　bridge　to　associate　user　interests　with　news　topics.　Next,　we　design　a　decision　tree　with　a　dynamically　changeable　structure　to　construct　a　user　interest　profile　from　the　user＇s　feedback.　Furthermore,　we　present　a　clustering-based　multidimensional　similarity　computation　method　to　select　the　nearest　neighbor　of　the　UP-Tree　efficiently.　We　also　provide　a　Map-Reduce　framework-based　implementation　that　enables　scaling　our　solution　to　real-world　news　recommendation　problems.　We　conducted　several　experiments　compared　to　the　state-of-the-art　approaches　on　real-world　datasets　and　the　experimental　results　demonstrate　that　our　approach　significantly　improves　accuracy　and　effectiveness　in　news　recommendation.