Internet of Things (IoT) applications can be built from a number of heterogeneous services provided by a range of devices, which are potentially resource constrained and/or mobile. As these services and applications continue to be more widespread, a key research question is how to predict user-side quality of service (QoS), to ensure the optimal selection, composition and adaptation of IoT services. The exponential growth in the number of these services means that it is not practical to invoke all candidate services to test their QoS, especially during runtime service adaptation. QoS can vary by time and location, which makes it difficult for service providers to give accurate estimates of how the service will perform for users located in changing network topologies. We propose IoTPredict, a novel neighbourhood-based prediction approach for the IoT, which uses an alternative similarity computation mechanism. Our collaborative approach requires no additional invocation of services, which is a key requirement for resource constrained devices in the IoT. We evaluate our algorithm on a QoS dataset and show that it achieves higher QoS prediction accuracy than other state of the art approaches.