This document discusses self-guiding mobile applications that can provide engaging user experiences through predictive features. It presents a proof of concept for a photo editing app that uses crowdsourced usage data and machine learning algorithms to predict users' next actions and display relevant tools prominently. Performance results are promising. The document recommends exposing predictive models through REST APIs for loose coupling and continuous development, demonstrating how models trained on a platform can receive API requests and send predictions to applications.

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Self Guiding Applications
Venkatesh Yadav

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Why ?
 If an app takes more than a few seconds to learn, a majority of users are going to
uninstall (MOBILE).
 Creating that engaging initial use experience is challenging, predominantly
constrained by
 Complexity of the app
 Screen space
 Etc.,
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Why ?
 We want to provide best “Engagement Experience” possible using crowd sourced
application usage data.
3

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Self Guiding Applications
4

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Self Guiding Applications
95
%
Action 1
92
%
Action 2
88
%
Action 3
85
%
Action 4
80
%
Action 5

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Phase 1 – Proof Of Concept
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 The Setting
 A mobile photo editing app.
 Relatively less complicated – approx. 20 possible actions
 Constrained in space – ribbon scroll and searching for actions
 The Goal
 Create engaging user experience, minimize scrolling and searching
 Predictive Feature Panel and Contextual Window

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Crowdsourced Product Usage Data
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 Each row is a set of actions (like a workflow) performed in an image editing
session
 Total 100K rows of data, of approx. 20 possible actions
001 002 003

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Machine Learning Algorithms
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 Markov Model
 Next action is dependent only on the present action, not on the sequence of previous actions
 N Action Bayesian Model
 Next action is dependent on the sequence of past N actions
 N Action Neural Network Model
 Next action is dependent on the sequence of past N actions

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Performance
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Predictive APIs
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 Evaluated few approaches to expose predictive models via HTTP REST
APIs
 Loose coupling between model creation and consumption
 Continuous model development and deployment capability
 Create Java POJO for the predictive
model
 Wrap POJO by any Java Web API
framework
 Can better integrate with Adobe.io
platform

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REST API – H2O
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Training
Dataset
Platfor
m
Server
Train Model
 Data Science
Team
 Smart Apps
Predictiv
e Model
(Java)
Predictive
API (Jar
file)

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Demo
“Once models are deployed to the platform, they can
begin receiving API requests and sending predictions back
to the applications.”
 Loose coupling
 Continues model development
 Provide REST API interface to predictive model
(Demo)
 Integration into an application (Next Step)

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Demo