Farfetch PBS Presentation
Transcript of 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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