Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for updating embeddings for user accounts on a social messaging platform. One of the methods includes receiving data having multiple data tuples, each data tuple indicating a user account and an item that the user account engaged with on the social messaging platform. A portion of the data is assigned to a respective multiple clusters. Embeddings are updated using an assigned cluster for each portion of the data. The updating includes: for each iteration step of multiple iteration steps, first embeddings are updated based on a first variable coefficient and a second variable coefficient. The updated first embeddings are stored into respective temporary data structures, which are then aggregated to generate aggregated first embeddings in a preserved data structure. Second embeddings are updated based on the aggregated first embeddings.

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.