Abstract: Using a natural language analysis, a current message is classified into a current message class, the current message being a portion of an interaction in narrative text form. Using a trained message class prediction model, a probability of a previous message class having resulted in the current message class is determined. A previous message is extracted from the interaction using the probability, the previous message being a portion of the interaction occurring prior to the current message, the previous message being classified into the previous message class.

Abstract: Provided is a method for distributing rules across instances of a rules engine. The method includes determining a rule load for each set of rules of a plurality of sets of rules. Each set of rules is associated with a tenant of a plurality of tenants hosted on a multi-tenant system. The method includes combining the rule load for each set of rules into an overall rule load. The method further includes distributing the sets of rules across a set of rules engine instances such that approximately the same fraction of the overall rule load is assigned to each rules engine instance of the set of rules engine instances.

Abstract: A method, system and computer-usable medium for optimizing of search rules modifying search results. A rules service is initiated prior to executing a given search query from a shopper. A search rule evaluation is performed for the given search query and implementing a search rule that causes actions defined by the search rule to be applied to the given search request query. A list of search rules implemented or fired for each given search query is stored. A tracking record is built based on search rule evaluation that includes the list of implemented or fired rules and rule impact tracking (RIT) records.

Abstract: Using a natural language analysis, a current message is classified into a current message class, the current message being a portion of an interaction in narrative text form. For the interaction using a state prediction model, an interaction outcome corresponding to the current message class is forecasted, the forecasting comprising computing a probability that the current message class will result in a successful message class. Using the state prediction model, a set of next message classes and a set of predicted interaction outcomes are determined, each message in the set of next message classes corresponding to the current message class, each predicted interaction outcome in the set of predicted interaction outcomes corresponding to a next message class in the set of next message classes. According to the corresponding predicted interaction outcome, the set of next message classes is ranked.

Abstract: A method, system and computer-usable medium for optimizing of search rules modifying search results. A rules service is initiated prior to executing a given search query from a shopper. A search rule evaluation is performed for the given search query and implementing a search rule that causes actions defined by the search rule to be applied to the given search request query. A list of search rules implemented or fired for each given search query is stored. A tracking record is built based on search rule evaluation that includes the list of implemented or fired rules and rule impact tracking (RIT) records.

Abstract: Provided is a method for distributing rules across instances of a rules engine. The method includes determining a rule load for each set of rules of a plurality of sets of rules. Each set of rules is associated with a tenant of a plurality of tenants hosted on a multi-tenant system. The method includes combining the rule load for each set of rules into an overall rule load. The method further includes distributing the sets of rules across a set of rules engine instances such that approximately the same fraction of the overall rule load is assigned to each rules engine instance of the set of rules engine instances.

Abstract: Provided are a computer program product, system, and method for generation of tasks and retraining machine learning modules to generate tasks based on feedback for the generated tasks. A machine learning module processes an input text message sent in the communication channel to output task information including an intended action and a set of associated users. A task message is generated including the output task information of a task to perform. The task message is sent to a user interface panel in a user computer. Feedback is received from the user computer on the output task information in the task message. The machine learning module is retrained to output task information from the input text message based on the feedback to reinforce likelihood correct task information is outputted and reinforce lower likelihood incorrect task information is outputted.

Abstract: Using a natural language analysis, a current message is classified into a current message class, the current message being a portion of an interaction in narrative text form. For the interaction using a state prediction model, an interaction outcome corresponding to the current message class is forecasted, the forecasting comprising computing a probability that the current message class will result in a successful message class. Using the state prediction model, a set of next message classes and a set of predicted interaction outcomes are determined, each message in the set of next message classes corresponding to the current message class, each predicted interaction outcome in the set of predicted interaction outcomes corresponding to a next message class in the set of next message classes. According to the corresponding predicted interaction outcome, the set of next message classes is ranked.

Abstract: Using a natural language analysis, a current message is classified into a current message class, the current message being a portion of an interaction in narrative text form. Using a trained message class prediction model, a probability of a previous message class having resulted in the current message class is determined. A previous message is extracted from the interaction using the probability, the previous message being a portion of the interaction occurring prior to the current message, the previous message being classified into the previous message class.

Abstract: A method, computer system, and a computer program product for efficient machine learning is provided. Embodiments of the present invention may include training a machine learning model offline. Embodiments of the present invention may include receiving and storing user feedback to the machine learning model for a current interval. Embodiments of the present invention may include determining that a machine learning model performance is redundant. Embodiments of the present invention may include converting the machine learning model performance to an increase in a performance speed. Embodiments of the present invention may include updating the trained machine learning model online.