With　the　prevalent　application　of　Internet　of　Things　(IoT)　in　real　world,　services　have　become　a　widely　used　means　of　providing　configurable　resources.　As　the　number　of　services　is　large　and　is　also　increasing　fast,　it　is　an　inevitable　mission　to　determine　the　suitability　of　a　service　to　a　user.　Two　typical　tasks　are　needed,　which　are　service　recommendation　and　service　selection.　The　prediction　for　Quality　of　Service　(QoS)　is　an　important　way　to　accomplish　the　two　tasks,　and　there　have　been　a　series　of　methods　proposed　to　predict　QoS　values.　However,　few　methods　have　been　used　to　study　the　QoS　prediction　in　IoT　environments,　where　contextual　information　is　vital.　In　this　article,　we　develop　a　holistic　framework　to　attack　the　QoS　prediction　in　the　IoT　environment,　which　is　based　on　neural　collaborative　filtering　(NCF)　and　fuzzy　clustering.　We　design　a　fuzzy　clustering　algorithm　that　is　capable　of　clustering　contextual　information　and　then　propose　a　new　combined　similarity　computation　method.　Next,　a　new　NCF　model　is　designed　that　can　leverage　local　and　global　features.　Sufficient　experiments　are　implemented　on　two　real-world　data　sets,　and　the　experimental　results　verify　the　effectiveness　of　the　proposed　framework.　©　2014　IEEE.