Abstract: Aspects disclosed herein may utilize neural embedding techniques to model session activity. A dataset may be collected from on online market place, such as an app store. The data set may include one or more user sessions comprising sequential click actions and/or item purchases. Models may be generated to represent session activity and, therefore, may be utilized for contextual recommendations of apps in an online app store. As such, the various aspects disclosed herein may also generate purchase predictions based on click-purchase relations in a sequence. The item similarities and purchase predictions may be used to provide real-time aid to users navigating an online marketplace.

Abstract: Aspects disclosed herein may utilize neural embedding techniques to model session activity. A dataset may be collected from on online market place, such as an app store. The data set may include one or more user sessions comprising sequential click actions and/or item purchases. Models may be generated to represent session activity and, therefore, may be utilized for contextual recommendations of apps in an online app store. As such, the various aspects disclosed herein may also generate purchase predictions based on click-purchase relations in a sequence. The item similarities and purchase predictions may be used to provide real-time aid to users navigating an online marketplace.

Abstract: Embodiments of the disclosure relate to apparatus for recommending items from a catalog of items to users in a population of users, configured to determine values for a measure of association between transactions of users with items in a first catalog and transactions of users with items in a second catalog and provide recommendations to users for transacting with items in the catalogs based on the determined values of association.