Which car fits my life? - inovex · DeepStack: Expert-level artificial intelligence in heads-up no-limit poker (Science, March 2017) Recent Breakthroughs in Deep Learning Reasons

Which car fits my life?Mobile.de’s approach to recommendations

PyData Berlin, July 1st, 2017Florian Wilhelm, Arnab Dutta

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Introduction

Dr. Florian Wilhelm

Data Scientist

inovex GmbH

� @FlorianWilhelm

� FlorianWilhelm

florianwilhelm.info

Dr. Arnab Dutta

Data Scientist

mobile.de GmbH

� @kopfhohen

� kraktoso

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Outline

§Introduction§Use-cases§Theory§Our Approach§Example§Outlook

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MOBILE.DEGERMAN MARKET LEADER

13.5 MIO UNIQUE USER PER MONTH

1.6 MIO VEHICLES

290EMPLOYEES

DREILINDEN / FRIEDRICHSHAIN BERLINHEADQUARTERS

Part ofebay Tech

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IT-project house for digital transformation:‣ Agile Development & Management‣ Web · UI/UX · Replatforming · Microservices‣ Mobile · Apps · Smart Devices · Robotics‣ Big Data & Business Intelligence Platforms‣ Data Science · Data Products · Search · Deep Learning‣ Data Center Automation · DevOps · Cloud · Hosting‣ Trainings & Coachings

Using technology to inspire our clients. And ourselves.

inovex offices inKarlsruhe · Pforzheim · München · Köln · Hamburg.

www.inovex.de

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Outline

§Introduction§Use-cases§Theory§Our Approach§Example§Outlook

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Why Recommendations?Why Recommendations?

Show width of offering

Inspiration

Engagement

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- - engagement- - inspiration- - relevance

Why Recommendations?

- - high click-through-rate - - small exit- & bounce-rates

User Benefits

Business Benefits

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Mobile.de Conversion Funnel

WishlistHome Search Result Page

View Contact Buy

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Recommendations on Home

Home

Recommendations based on preferencesof visiting users as an alternative entry point.

WishlistHome SRP View Contact Buy

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Recommendations on Search Results Page

Recommendations basedon similar vehicle makeand model id to presentalternatives

WishlistHome SRP View Contact Buy

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Recommendations on View Item Page

VIP

Recommendations based on the specific make and model a user is viewing to present alternatives

WishlistHome SRP View Contact Buy

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Recommendations on your Wishlist

Recommendations based on the specific make and model of a deleted ad to provide almost identical recommendations

Recommendations based on theusers car preferences and the parking lot items.

WishlistHome SRP View Contact Buy

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§Introduction§Use-cases§Theory§Collaborative Filtering§Content Based§Our Approach§Example§Outlook

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Collaborative Filtering

items similar

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Watched / Rated

Unwatched

Item-Item Similarity

??

??

????Item-based Recommendations

Cosine Similarity

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Recommendation

Item-based Recommendations

P

P

P

P

P P

Wishlist

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Summary of Collaborative Filtering✓Collective behaviour of users

✓Standard-Method (it works, it’s reliable etc.)

� Cold Start Problem: New listings need a certain number of clicks to be recommended.

� Sparsity problems: lot fewer interactiondata points than total items and users.

� Content agnostic

� Only “batch-based” learning

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Looking For: Used Car (100%)

Prefers (Make): BMW (50%), Audi (50%)Prefers (Model): Audi A3 (25%), Audi A4 (25%),

BMW 318 (50%)Searching In: lat 52.5206, lon 13.409Search Radius: 300kmPreferred Price: 20 000€ ± 1500€ Preferred Mileage: 10 000km ± 5000km

User Preferences

Marketing

Anonymous

Content-based Filtering: User Preferences

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Content-based Filtering

interacted

<Price: 10K, Category: small>

<Price: 6K, Category: small>

<Price: 90K, Category: sports>

<Price: 10K, Category: small>

recommend

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Summary of Content-basedüWorks even if there are no other

users

ücontent-based preferences ofusers based on a weighted vectorof item features

� Hard to do recommendations fornew users (cold start problem)

� Non-applicable for heterogenouscontent types

� Low diversity, i.e. more of thesame

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§Introduction§Use-cases§Theory§Our Approach§Example§Outlook

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Find the car that perfectly fits your life

User’s Car Preferences Car Pool + Attributes(make, model, color, price, …)

Flexible(cold-start, uncertainty, real-time, ...)

Interactions of other users(views, parkings, contacts)

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Hybrid Recommender

Collaborative Filtering

Hybrid Recommender

Contentbased

• no cold-start problem of new items

• integrate new user events in real-time

• robust and reliable concepts

• easy to tune for different use-cases

• comprehensible and debuggable

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RecommendationEngine

User PreferenceService API

User EventTracking & Storage

RecommendationEngine

RecommendationEngine

User PreferenceComputation &

Storage

All Listings

User Preference+ Recommendation Architecture

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Hybrid Recommender Concept

PPP P

P

Looking For: Used Car (100%)

Prefers (Make): BMW (50%), Audi (50%)Prefers (Model): Audi A3 (25%), Audi A4 (25%),

BMW 318 (50%)

Searching In: lat 52.5206, lon 13.409Search Radius: 300kmPreferred Price: 20 000€ ± 1500€Preferred Mileage: 10 000km ± 5000km

User Profile

BuyerLast Action: YesterdayFrequent User

User 12345

Likelihood to buy: 88 %

Elastic Search Query

Score0.8

0.3

1.7

3.2

1.1

0.9

……

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§Introduction§Use-cases§Theory§Our Approach§Example§Outlook

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Finding similar make/models

§Users are often uncertainwith their choices in orientation phase

§Help users explore similarmodels to make informeddecisions

§Exploit the collaborativeaspect in defining theconcept of similarity

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Make/Model Recommender with LightFM

LightFM:§Matured and well documented Python package§Optimized and parallelized with Cython§Hybrid recommender based on matrix factorisation§Supports Learning-to-Rank objectives (BPR, WARP)

Audi A4 BMW 645 similar vehicles

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Non-negative Matrix Factorisation (NMF)LF1 LF2

LF1

LF2

M (|U| x |I|) x R (|LF| x |I|)

= X

= L (|U| x |LF|)

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NMF: Embeddings to Similarities

LF1

LF2

• car is represented as an item embedding

• Given 2 embeddings, LFitem_i and Lfitem_j_

• Compute sim(LFitem_i , LFitem_j)

• Find pairwise values for all item pairs (|I| x |I|)

item embeddings

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NMF: Persisting Item similarities

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Method§ best of both the worlds§ robust

Business§ higher CTR§ lesser exits rates

User§ engagement§ diversity

hybrid

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§Introduction§Motivation & Use-cases§Theory§Implementation§Outlook

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Deep Learning

Dermatologist-level classification of skin cancer with deep neural networks(Nature 542, 115-118, February 2017)

DeepStack: Expert-level artificial intelligence in heads-up no-limit poker(Science, March 2017)

Recent Breakthroughs in Deep Learning Reasons for Deep Learning

• captures nonlinear relations

• holistic approach, i.e. reduces number of components possibly

• less feature engineering

• possibly improved quality

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Approach: Wide and Deep Model

mileagepricecolor

history

mileagepricecolor

views

...

0.38

0.250.79

...0

10

...0

0.20.8

...

...

enco

deen

code

enco

deen

code

...

0.35

-0.152.03

cont.

cat.

cont.

cat.us

erem

bedd

ings

item

em

bedd

ings

Outp

ut

cros

s-ite

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Probability that user X likes vehicle Y

Deep Component

Wide Component

one-

hot

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Recommendations support users to find the perfect vehicle based on their preferences and by collaboration

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Any questions?lusion