farfetch pbs presentation

8/19/2019 Farfetch PBS Presentation

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Autor / Data

Recommender SystemIn Fashion E-commerce: Finding the Rig

Isabel Portugal / 18-01-2016

André a!ares

Andreia Santos

A"g"sto Fonseca

Developed by:


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Farfetch

#onte)t

Recommender Systems*hat do they do+

,"r a$$roach

Big Data Infrastructure*hy do e need it+,"r a$$roach

Inde)


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Introduction

onte!t

As an e-commerce e'site3 Far4etch main so"rce o4 re!en"e is on"lin

D"e to a great amo"nt o4 $"'%icity e ha!e many dai%y !isits that do n

ith the e'site nor do a $"rchase5

,"r goa% ith this $roect is to increase Far4etch7s con!ersion rate3 in convert an ordinary visitor into a buyer 'y shoing items that they

%ie and de!e%o$ an in4rastr"t"re that can s"$$ort it5


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#eam

About us

André

a!ares

Siemens

AndreiaSantos

%i$

A"g"stoFonseca

;F

#hristo$he

Fig"eiredo

Far4etch

Fi%i$e

&"edes


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Recommender Systems

$hat do they do%

&stimate an utility function that automatically predicts ho a "se

item 'ased on $ast 'eha!ior3 re%ations to other "sers3 item simi%arity3 c

#o%%a'orati!e Fi%tering: Recommend items 'ased on%y on the users p

@ ser-'ased: Find simi%ar "sers to me and recommend hat they %ied5

@ Item-'ased: Find simi%ar items to those that I ha!e $re!io"s%y %ied5['()DS


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Recommender Systems

)ur Approach

(rod"ct (age

interactions

(o%arity 4rom

(rod"ct7s rand

itter Sentiment

(rod"ct #ategory (rod"ct S"'catego


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Recommender Systems

)ur Approach

Applied methods:

@ Random

@ (o$"%ar

@ Item 'ased

@ ser 'ased

B *ith o"r mode%s e scored 'est $recision res"%ts

com$aring to Far4etch7s c"rrent im$%ementation5

For e!a%"ation e a%so sho"%d im$%ement an A/ testing

(R

F(R


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Recommender Systems

Possible Implementation


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Big Data Infrastructure

$hy do *e need it% Sharing some results

*e m"st 'e a'%e to $resent a set o4 recommendations in a

short amo"nt o4 time hen "sers c%ic on a ne $rod"ct $age

their recommendations need to 'e ready5

For so%!ing this e "sed Spar+, an infrastructure +no*n for

its processing speed as it runs in memory a%most 100)

times 4aster than ;adoo$3 hich rites into dis5

*e "sed AS Alternating -east S.uares3 a $ratica%

o$timiGation techniH"e hen dea%ing ith im$%icit datasets3 and

e ere a'%e to do o"r recommendations in %ess than 9

seconds

#%icstream

Recom

S


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Some interesting findings


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Some interesting findings


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Further *or+

$hat *e *ould li+e to improve/implement

@ St"dy ho e can im$ro!e o"r recommendation system in ord

better performance and scalability5

@ Refine our affinity function eights3 $o%arity3 adding ne !a

indo5 E5g5: Add co%or and category that are JtrendyK 4or c"rrent

@ Im$ro!e "ser and $rod"ct c%"sters7 $er4ormance 'y adding m

variables5


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Suggestions

(o* *e imagine Farfetch 01

@ Providing a personali2ed homepage 'ased on the ty$e o4 "ser5

@ Flagging e!clusive products so "sers no that they are '"y

"niH"e5


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farfetch pbs presentation

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