Adobe, Inc.

Jayakumar Subramanian

Balaji Krishnamurthy

Nikaash Puri

Piyush Gupta

Georgios Theocharous

Mihir Naware

Milan Aggarwal

Pinkesh Badjatiya

Hyman Chung

Yaman Kumar

Matthew William Rozen

Pankhri Singhai

Sukriti Verma

Tanay Anand

Shripad Vilasrao Deshmukh

Pranjal Prasoon

Somesh Singh

Nikitha Srikanth

Sriyash Poddar

Nisarg Shailesh Joshi

Seoyoung Park

Nithyakala Sainath

In implementations of systems for digital content analysis, a computing device implements an analysis system to extract a first content component and a second content component from digital content to be analyzed based on content metrics. The analysis system generates first embeddings using a first machine learning model and second embedding using a second machine learning model. The first embeddings and the second embeddings are combined as concatenated embeddings. The analysis system generates an indication of a content metric for display in a user interface using a third machine learning model based on the concatenated embeddings.

Digital content analysis

Interactions between a user and an e-commerce platform are automatically guided to increase the chances of a conversion. Previous sequences of interactions (e.g., conversion journeys and non-conversion journeys) with the e-commerce platform are collected, an artificial neural network (ANN) learns how to estimate a safety value a current user state by learning from previous user interactions (e.g., conversion and non-conversion journeys), a software agent of the e-commerce platform applies a current user state of the user to the ANN to determine a current safety value, and the software agent provides content to the user based on the current safety value and the current user state.

Customer journey management using machine learning

Systems and methods for machine learning are described. Embodiments of the present disclosure receive state information that describes a state of a decision making agent in an environment; compute an action vector from an action embedding space based on the state information using a policy neural network of the decision making agent, wherein the policy neural network is trained using reinforcement learning based on a topology loss that constrains changes in a mapping between an action set and the action embedding space; and perform an action that modifies the state of the decision making agent in the environment based on the action vector, wherein the action is selected based on the mapping.

Optimal sequential decision making with changing action space

Methods and systems are provided for generating and providing insights associated with a journey. In embodiments described herein, journey data associated with a journey is obtained. A journey can include journey paths indicating workflows through which audience members can traverse. The journey data can include audience member attributes (e.g., demographics) and labels indicating journey paths traversed by audience members. A set of audience segments are determined that describe a set of audience members traversing a particular journey path. The set of audience segments can be determined using the journey data to train a segmentation model and, thereafter, analyzing the segmentation model to identify patterns that indicate audience segments associated with the particular journey path. An indication of the set of audience segments that describe the set of audience members traversing the particular journey path can be provided for display.

Providing insights and suggestions for journeys

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Maharashtra, India

Jayakumar Subramanian