Google Cloud Platform Blog
How to build your own recommendation engine using machine learning on Google Compute Engine
Tuesday, March 1, 2016
You might like this blog post . . . if you like recommendation engines. If that sentence has a familiar ring, you've probably browsed many websites that use a
recommendation engine
.
Recommendation engines are the technology behind content discovery networks and the suggestion features of most ecommerce websites. They improve a visitor's experience by offering relevant items at the right time and on the right page. Adding that intelligence makes your application more attractive, enhances the customer experience and increases their satisfaction. Digital Trends studies show that 73% of customers prefer to get a personalized experience during their shopping experience.
There are various components to a recommendation engine, ranging from data ingestion and analytics to machine learning algorithms. In order to provide relevant recommendations, the system must be scalable and able to handle the demands that come with processing Big Data and must provide an easy way to improve the algorithms.
Recommendation engines, particularly the scalable ones that produce great suggestions, are highly compute-intensive workloads. The following features of
Google Cloud Platform
are well-suited to support this kind of workload:
Multi-tenant, redundant datacenters
Custom instance types
Global network
Years of experience in software infrastructure innovation, such as
MapReduce
,
BigTable
,
Dremel
,
Flume
, and
Spanner
.
Customers building recommendation engines are jumping on board.
Antvoice
uses Google Cloud Platform to deploy their self-learning, multi-channel, predictive recommendation platform.
This new
solution article
provides an introduction to implementing product recommendations. It shows you how you can use open source technologies to setup a basic recommendation engine on Cloud Platform. It uses the example of a house-renting website that suggests houses that the user might be interested in based on their previous behavior through a technique known as collaborative filtering.
To provide recommendations, whether in real time while customers browse your site, or through email later on, several things need to happen. At first, while you know little about your users' tastes and preferences, you might base recommendations on item attributes alone. But your system needs to be able to learn from your users, collecting data about their tastes and preferences.
Over time and with enough data, you can use machine learning algorithms to perform useful analysis and deliver meaningful recommendations. Other users’ inputs can also improve the results, effectively periodically retraining the system. This solution deals with a recommendations system that already has enough data to benefit from machine learning algorithms.
A recommendation engine typically processes data through the following four phases:
The following diagram represents the architecture of such a system:
Each component of this architecture can be deployed using various easy-to-implement technologies to get you started:
Front-End
: By deploying a simple application on
Google App Engine
, a user can see a page with top recommendations. You can take it from there, easily building a strong and scalable web platform that can manage one to several millions of users, with minimum operations.
Storage
: The solution uses
Google Cloud SQL
, our managed MySQL option. A commonly used database in the ecommerce domain, this database integrates well with
MLlib
, a machine learning library.
Machine learning
: Using
Google Cloud Dataproc
or
bdutil
, two options that simplify deployment and management of Hadoop/Spark clusters, you'll deploy and run MLlib-based scripts.
The solution also discusses considerations for how to analyze the data, including:
Timeliness concerns, such as real-time, near-real-time, and batch data analysis. This information can help you understand your options for how quickly you can present recommendations to the user and what it takes to implement each option. The sample solution focuses mainly on a near-real-time approach.
Filtering methods, such as content-based, cluster and
collaborative filtering
. You'll need to decide exactly what information goes into making a recommendation, and these filtering methods are the common ones in use today. The sample solution focuses mainly on collaborative filtering, but a helpful appendix provides more information about the other options.
We hope that this solution will give you the nuts and bolts you need to build an intelligent and ever-improving application that makes the most of the information that your users give you. Happy reading!
If you liked this blog post . . . you can get started today following these steps:
Sign up for a
free trial
Download and follow the instructions on the
Google Cloud Platform Github page
“Recommend” this solution to your friends.
-
Posted by Matthieu Mayran, Cloud Solutions Architect
No comments :
Post a Comment
Don't Miss Next '17
Use promo code NEXT1720 to save $300 off general admission
REGISTER NOW
Free Trial
GCP Blogs
Big Data & Machine Learning
Kubernetes
GCP Japan Blog
Labels
Announcements
56
Big Data & Machine Learning
91
Compute
156
Containers & Kubernetes
36
CRE
7
Customers
90
Developer Tools & Insights
80
Events
34
Infrastructure
24
Management Tools
39
Networking
18
Open Source
105
Partners
63
Pricing
24
Security & Identity
23
Solutions
16
Stackdriver
19
Storage & Databases
111
Weekly Roundups
16
Archive
2017
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Feed
Subscribe by email
Technical questions? Check us out on
Stack Overflow
.
Subscribe to
our monthly newsletter
.
Google
on
Follow @googlecloud
Follow
Follow
Follow
No comments :
Post a Comment