Abstract: A method, computer system, and a computer program product for personalized recommendations is provided. The present invention may include determining a momentum score for each item of a training data set. The present invention may include querying a recommendation engine for an initial list of items. The present invention may include generating a blended score for each item of the initial list of items, wherein the blended score is determined based on the momentum score and the confidence score for each item of the initial list of items. The present invention may include presenting a personalized list of recommendations to the user based on the blended score for each item of the initial list of items.

Abstract: An approach to controlling smart devices in response to detecting events may be provided. The location data of a user may be received, along with date and time for the location data. The location may be compared to a daily schedule to determine whether an event has occurred. If the location data is determined to be outside of the daily schedule it is determined an event has occurred. The event is then compared to other logged events within a historical event database, to determine if the event is similar to any past events. If the event is determined to be similar to a past event, the state of smart devices connected to an event driven smart device control environment are changed to mirror the state they were in at during determined similar event.

Abstract: In an approach to improve the field of architecture generation by dynamically sizing computer system architecture requirements for virtual machines. Embodiments define static parameters and dynamic parameters for customer consuming resources of the computer system architecture and store data of the static parameters and dynamic parameters in data warehouse database (DWDB) tables. Further, embodiments compute, using the data of the DWDB tables, calculate the minimum, maximum, mean and standard deviation (SD) for the user count and the CPU and memory usage, and update the DWDB tables based on the minimum, maximum, and SD values per customer.

Abstract: An embodiment parses request data representative of a request for proposal (RFP) and extracts attribute data representative of an RFP attribute that corresponds to an entity of interest in a qualification taxonomy using a cognitive process to evaluate the RFP using natural language processing. The embodiment generates answer data representative of an answer to a first question of a qualification questionnaire related to the entity of interest using the RFP attribute. The embodiment constructs the answer data based at least in part on a response pattern associated with the first question, and computes a score for the answer to the first question based at least in part on a confidence value generated by the cognitive process. The embodiment then outputs the qualification questionnaire, including the answer and the score, allowing for review by a Subject Matter Expert (SME), and allowing the cognitive process to learn from the SME review.