Abstract: Techniques for identifying clusters of user interactions and shopping missions may be provided. For example, the system may receive a history of interactions between a user and one or more network pages. The system may identify a most recent event from the history of interactions and identify a cluster that includes other events from the history of interactions that are of a same category as the most recent event. The determination of the cluster may be based in part on item attributes associated with the item presented on the at least one of the one or more network pages. The most recent event may then be associated with the cluster. In some examples, a shopping mission is determined and one or more notifications are provided to a user, merchant, or electronic marketplace in association with the identified shopping mission.

Abstract: A user interface (UI) feature assists users in comparing items and making purchase decisions. One or more shopping missions are identified and consolidated so that a user easily compares competing vendors for previously viewed products. Additionally, user-selectable product options (e.g., size, color, etc.) are automatically pre-selected based upon past online shopping behavior and the availability of such options are displayed in the comparison so that the user makes meaningful shopping decisions and leverage past shopping results.

Abstract: Techniques for improving access to and interactions with bots are described. In an example, a first bot, hosted on a computing system, may identify an action to be performed for a user associated with a computing device. The action may be identified based on a user interaction with the first bot, where the user interaction may be provided from the computing device. The first bot may select a second bot based on the action. The second bot may be hosted on a same or a different computing system. The first bot may determine, based on a previous user input to the first bot, a parameter value for an execution of the action and may send the parameter value to the second bot. In response, the first bot may receive a result of an execution of the action and may provide information about the result to the computing device.

Abstract: The present disclosure is directed to training and using machine learning models to determine user intent from a search query, for example via a semantic parse that identifies particular catalog fields for items in an electronic catalog that would satisfy the user's current mission as reflected in their search query intent. The determined intent can then be used to filter recommendations and/or pre-select attribute-value input fields on detail pages displayed after the user navigates away from the search results page, until the mission is complete.

Abstract: Techniques for identifying clusters of user interactions and shopping missions may be provided. For example, the system may receive a history of interactions between a user and one or more network pages. The system may identify a most recent event from the history of interactions and identify a cluster that includes other events from the history of interactions that are of a same category as the most recent event. The determination of the cluster may be based in part on item attributes associated with the item presented on the at least one of the one or more network pages. The most recent event may then be associated with the cluster. In some examples, a shopping mission is determined and one or more notifications are provided to a user, merchant, or electronic marketplace in association with the identified shopping mission.

Abstract: The technology may monitor context items as a content page is navigated. A similarities dataset may be selected from a plurality of similarities datasets as a source of recommendations based on the context items. Recommendations in the similarities dataset selected may be ranked based on the context items. The recommendations may be provided based on the ranking.