the rise of recommendation engines

The rise of Recommendation

Who is Gravity R&D?

Gravityuses

machine learningand

Big Data analyticsto create

personalized user journeysacross

all touch points

Scientific Exellence• 4 PhDs in the founding team since 2006• Tied for first place in the Netflix Prize• 200+ journal publications that have been cited over 2,000

times• Patented algorithms USPTO 8,676,736

What is Big Data

Information overloadEach day

500 million tweets 1 billion active user on Facebook 750,000 hours of video uploaded to Youtube

Not just about the size of data sets

Gartner: Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.

What do we do with a lot of data?

Portal

Search Engine

Information aggregation

Information retrieval

Recommendationsystem

Information filtering

Recommendation systems …

… help users find content that is relevant to their interests 

Gravity R&D

Examples

Facebook

Netflix

Amazon

Tiki.vn

35% revenue come from recommended products

75% of what people watch is from personalization

Why Recommendation?

It’s not information overload.It’s filter failure

Clay Shirky

interesting book or cool music

What are the right keywords?

Paradox of Choice

Fewer Options lead to More Actions

More Options lead to Fewer Actions

A lot of times, people don’t know what they want until you show it

to themSteve Jobs

Recommendation in Vietnam

Challenges• Completely from scratch

Scientific knowledge Implementation algorithms into code

• Open source solution Research about the solution System architecture Fine tuning Maintenance

• Technology vendor You have to pay

ChallengeHow to keep users engaged and generate extra margin on existing users

SolutionPersonalization on the Home Page and Similar Item on Product and Cart Page. Cross recommendation on mobile app and email.

ResultsGravity significantly outperformed both other competitors in AB test.

Gravity’s recommendations resulted in...

“Gravity has been outperforming Strands and TargetingMantra in every important aspect and KPI: Revenue, AOV, CTR and response time.” — Hung Tran Viet, Product Owner, Tiki.vn

a GMV of

$13.15

/1000 recs

reached

6 %conversion

rate

ChallengeIncrease the discoverability of jobs and the relevant of job offers in the newsletter

SolutionGravity show similar jobs on the job detail page and personalize the jobs listed in the newsletter based on user behavior.

ResultsJob applicants are much more engaged with the personalized list of jobs in compare to the vanilla list

Users viewed Gravity’s recommendation …

“Gravity provides a job recommendation solution that has plug and play integration and works out of the box. Our new job recommendation engine helps our jobseekers discover their dream jobs. ” — Eduardo Mora, Head of Product & Engineering, Vietnamworks

clicks

59%more often

apply

26%more

The Future

• ML theories often based on long logs of user preference• In practice sometimes not feasible• People use item2item methods instead

relatively good result cheap to implement/calculation but only use last click information for recommendation

• DL models user’s session preference first click as input sequential input feeded into DL layers

• Thumbnail image as input

Deep Learning based Recommendation

• Forrester Research: more than $1 trillion of retail sales in 2015 were influenced by mobile phones• Personalized promotion pushed via app notification when

customers visit store• Follow-up email if customer leaves store without purchase• Suggestion for call center agents

O2O – Omnichannel

Right Product - Right Price - Right Channel - Right Time

O2O – Omnichannel

• Self-servicing for SME• Quick and simple integration• Plugins with major ecommerce platforms Shopify, Magento,

Haravan

Recommendation as a Platform

Thank you!

Contact us:

www.gravityrd.comlamnk@gravityrd.com

For the latest trends and insights: www.facebook.com/gravityrd