Adobe, Inc.

Deepali Gupta

Atanu Ranjan Sinha

Nikhil Sheoran

Deepali Jain

Sopan Khosla

Eunyee Koh

Sungchul Kim

Branislav Kveton

Hung Hai Bui

Charles Li Chen

Reshmi Naduparambil Sasidharan

In some embodiments, a computing system computes, with a state prediction model, probabilities of transitioning from a click state represented by interaction data to various predicted next states. The computing system computes an interface experience metric for the click with an experience valuation model. To do so, the computing system identifies base values for the click state and the predicted next states. The computing system computes value differentials for between the click state's base value and each predicted next state's base value. Value differentials indicate qualities of interface experience. The computing system determines the interface experience metric from a summation that includes the current click state's base value and the value differentials weighted with the predicted next states' probabilities. The computing system transmits the interface experience metric to an online platform, which can cause interface elements of the online platform to be modified based on the interface experience metric.

Machine-learning models applied to interaction data for facilitating experience-based modifications to interface elements in online environments

There is described a method and system in an interactive computing environment modified with user experience values based on behavior logs. An experience valuation system determines an experience value and an estimated experience value. The experience value is based on a current state of interaction data from a user session, based on a history of past events, and an estimation function defined by parameters to model the user experience values. The estimated experience value is determined based on, in addition to the current state and the estimation function, next states associated with the current state, and a reward function. The parameters of the estimation function are updated based on a comparison of the expected experience value and the estimated experience value. For another aspect, the method and system may further include a state prediction system to determine probabilities of transitioning that may be applied to determine the estimated experience value.

Characterizing and modifying user experience of computing environments based on behavior logs

Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.

Predictive analysis of target behaviors utilizing RNN-based user embeddings

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New Delhi, India

Deepali Gupta