Abstract: Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.

Abstract: Media is streamed in accordance with carbon footprint considerations. A streaming history of a streaming plan is analyzed to determine a historical carbon footprint. At least one streaming plan is presented that includes target carbon footprint relative to the historical carbon footprint. A selection is received for one of the at least one streaming plan, wherein the streaming performance on the streaming plan is tracked for post streaming plan carbon emissions. The user streaming performance is modified for the post streaming plan carbon emissions to substantially match the target carbon footprint of the at least one streaming plan.

Abstract: An approach is provided for generating process flow documentation. Headings and subheadings are identified in multiple system documents specifying actions required to be taken in response to service requests, which are specified by process flows. A portion of a system document included in the multiple system documents is mapped to a process block of a process flow included in the process flows. The portion is specified by a heading or subheading in the system document. The mapping is based on a similarity score indicating an amount of similarity between (i) the heading or subheading and (ii) a name of the process block, or based on a matching of the portion of the system document to the process block by a semantic understanding of the portion provided by a machine learning system.

Abstract: A system and method for modeling preferences of customers to drive personalized contextual recommendations using cognitive deep constrained filtering includes receiving a user query on an online retail platform, in response to receiving the user query, performing a first online matrix manipulation and a second online matrix manipulation, and sending a list of ranked recommended products.

Abstract: An approach is provided for evaluating a recommender system. A user's purchase date of an item and attributes of the item are extracted from a test set. Drop probabilities are assigned to the attributes. Using bootstrapping with aggregation, queries for the user and the item are generated by omitting one or more attributes from each of the queries according to the drop probabilities. Data that became available to the recommender system after the purchase date is identified. Without using the identified data and using data that became available to the recommender system before the purchase date, ranked item recommendation sets for the queries are generated. Similarity at rank K (SIM@K) values for the recommendation sets are calculated. Average SIM@K values are calculated over multiple users specified in the test set. Based on the average SIM@K values, the performance of the recommender system is evaluated.

Abstract: A system and method for modeling preferences of customers to drive personalized contextual recommendations using cognitive deep constrained filtering includes receiving a user query on an online retail platform, in response to receiving the user query, performing a first online matrix manipulation and a second online matrix manipulation, and sending a list of ranked recommended products.