From the time the Internet came into existence, it has revolutionized the communication domain by not only helping to connect various forms of devices but also people around the globe over the network. This ever-emerging technology is accessible everyday for almost any purpose with the goal of always providing information, be it accessing digital content via applications, exchange of audio-video calls, emails and supporting e-businesses. It has helped make the world smaller by making interactions possible digitally. Within the current ecosystem, by associating objects such as sensors and actuators into the network, it has built a way of communicating the surrounding to the digital platform either over a wired or wireless network to help gain more in-sights into the real world analyzing user preferences, behaviours, interactions etc. Based on such recorded data, smart services could be provided to the endusers thus making these objects or "Things" smart as well. Not to forget that the introduced technology would very well reduce the complexities and efforts which the old-fashioned ways could not solve. This led to the popularity of the concept called "Internet of Things" or IoT which has flourished among various domains of work; be it in the transportation industries, medicines, building management systems (BMS) etc. Any structure that utilizes information and communication technology to support building operations automatically and without the need for a manual set of steps can be referred to as a smart building. The reason it is said to be automatic in nature is because of the fact that the Internet of Things devices actuate systems or operations that are completely dependent on interactions with a clear visibility of the actionable instances. A comparison study between conventional buildings and a smart building would undoubtedly favour the smart building because it not only automates tasks but also proves to be better at maintaining a high energy saving potential with improved user comfort, productivity and well being of the occupants [1]. Real-time fault detection and abnormalities in building operations can also be detected, preventing system failure. But as time passes, these sensors are prone to deterioration and require on-site assistance for setup and maintenance, which increases time and expense. Again, the occupants of the smart space are given low priority due to the emphasis on energy. Building owners and facility owners are focused mainly with energy conservation, but because they do not always occupy the building space, it is impossible to determine whether the provided set of automated services is actually effective. Hence there is a need that the occupant must contribute equally to the set of operations at the elementary level that will supplement the process. This engagement will ensure that the user is also taken into account in order to influence the actionable instances. Considering the downside of totally depending on the IoT object data, the thesis aims at providing a smart service that will use these object data (indoor environmental conditions and external environment conditions) and incorporate user engagement as part of the decision making process so as to deduce a recommendation that fits the user comfort index at the given moment. Along with the sensor data and third party application programming interface (API) for weather information, user also has the option to provide their own context of the sensed surrounding as sensory data. The collected data then suggests the users to actuate certain local settings that would be considered apt for the surrounding thus improving user experience and help understand user preferences. The feedback from users are taken at a recognized time called micro-moments when they would be idle to provide responses and most receptive to engage into recommendations. User concentration is improved with a time management technique called Pomodoro which is implemented within the service itself. The communication across the "Things" and the service application is supported by an OASIS standard messaging protocol called "Message Queuing Telemetry Transport" (MQTT). Since it reuses a single connection/port for several messages that have low size and has a reduced average response time, it is suitable for the stated service. The recommendation engine is based on a rule based conditional logic along with AI planning for realization of action sequences. All the relevant data is stored on the MySQL database for data management, status setting and retrieval for decision making. The complete service along with data analysis and tracking is realized on a python based web framework called Django because of its clean design and distinct modules for each service or feature.