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Meta-­‐Prod2Vec:	
  Simple	
  Product	
  
Embeddings	
  with	
  Side-­‐Informa:on	
  	
  
	
  
	
  
	
  
	
  
	
  
Flavian	
  Vasile,	
  Elena	
  Smirnova	
  @Criteo	
  
Alexis	
  Conneau	
  @FAIR	
  
Contents	
  
•  Product	
  Embeddings	
  for	
  Recommenda:on	
  	
  
•  Embedding	
  CF	
  signal:	
  Word2Vec	
  and	
  Prod2Vec	
  
•  Meta-­‐Prod2vec:	
  Embedding	
  with	
  Side-­‐Informa:on	
  	
  
•  Experimental	
  Results	
  
•  Conclusions	
  
Product	
  Embeddings	
  for	
  
Recommenda5on	
  	
  
	
  
Product	
  Embeddings	
  for	
  
Recommenda5on	
  	
  
	
  
Represent	
  items	
  (and	
  some/mes	
  users)	
  as	
  
vectors	
  in	
  the	
  same	
  space	
  and	
  use	
  their	
  
distances	
  to	
  compute	
  recommenda/ons.	
  
•  At	
  a	
  certain	
  level,	
  nothing	
  new!	
  
•  We	
  already	
  had	
  Matrix	
  Factoriza/on	
  
•  It	
  is	
  yet	
  another	
  way	
  of	
  crea/ng	
  latent	
  
representa/ons	
  for	
  Recommenda/on	
  
Some	
  of	
  the	
  NN	
  methods	
  can	
  be	
  
translated	
  back	
  into	
  MF	
  techniques.	
  	
  
	
  
Differences:	
  
•  new	
  ways	
  to	
  compute	
  matrix	
  entries	
  	
  
•  new	
  loss	
  func/ons	
  
Where	
  do	
  we	
  fit?	
  
•  Hybrid	
  model	
  that	
  uses	
  CF	
  with	
  content	
  
side-­‐informa/on	
  
•  Incursion	
  on	
  the	
  embedding	
  methods	
  
using	
  side	
  info	
  
Embedding	
  CF	
  signal:	
  	
  
Word2Vec	
  and	
  Prod2Vec	
  
	
  
(Word-­‐to-­‐hidden	
  matrix)	
  x	
  (Hidden-­‐to-­‐Word	
  context	
  matrix)	
  
	
  
	
  
Word2Vec:	
  Skip-­‐gram	
  
 
Word2Vec	
  
In	
  this	
  space,	
  words	
  that	
  appear	
  in	
  similar	
  contexts	
  
will	
  tend	
  to	
  be	
  close:	
  
	
  
 
The	
  same	
  idea	
  can	
  be	
  applied	
  to	
  other	
  sequen:al	
  
data,	
  such	
  as	
  user	
  shopping	
  sessions	
  -­‐	
  Prod2vec.	
  
	
  
Words	
  =	
  products	
  
Sentences	
  =	
  shopping	
  sessions	
  
Grbovic	
  et	
  al.	
  E-­‐commerce	
  in	
  Your	
  Inbox:	
  Product	
  RecommendaBons	
  at	
  Scale,	
  WWW	
  2013	
  
	
  
Prod2Vec	
  
Prod2Vec	
  
The	
  resul:ng	
  embedding	
  will	
  co-­‐locate	
  products	
  
that	
  appear	
  in	
  the	
  vicinity	
  of	
  the	
  same	
  products.	
  
	
  
Prod2Vec	
  loss	
  func5on 	
   	
  	
  	
  
Meta-­‐Prod2vec:	
  Embedding	
  with	
  Side-­‐
Informa5on	
  	
  
	
  
Meta-­‐Prod2vec:	
  Embedding	
  with	
  Side-­‐
Informa5on	
  	
  
	
  
Idea:	
  Use	
  not	
  only	
  the	
  product	
  sequence	
  
informa:on,	
  but	
  also	
  product	
  meta-­‐data.	
  
	
  
Where	
  is	
  it	
  useful?	
  
Product	
  cold-­‐start,	
  when	
  sequence	
  
informa:on	
  is	
  sparse.	
  
How	
  can	
  it	
  help?	
  
We	
  place	
  addi:onal	
  constraints	
  on	
  
product	
  co-­‐occurrences	
  using	
  external	
  
info.	
  
	
  
We	
  can	
  create	
  more	
  noise-­‐robust	
  
embeddings	
  for	
  products	
  suffering	
  from	
  
cold-­‐start.	
  
Type	
  of	
  product	
  side-­‐informa5on:	
  	
  
•  Categories	
  
•  Brands	
  
•  Title	
  &	
  Descrip:on	
  
•  Tags	
  
How	
  does	
  Meta-­‐Prod2Vec	
  leverage	
  this	
  
informa5on	
  for	
  cold-­‐start?	
  
Mo5va5ng	
  example:	
  
Let’s	
  say	
  we	
  are	
  trying	
  to	
  build	
  a	
  
recommender	
  system	
  for	
  songs...	
  	
  
We	
  want	
  to	
  build	
  a	
  very	
  simple	
  solu5on	
  
that	
  based	
  on	
  the	
  last	
  song	
  the	
  user	
  
heard,	
  recommends	
  the	
  next	
  song.	
  	
  
Two	
  different	
  recommenda:on	
  situa:ons:	
  	
  
•  Simple:	
  the	
  previous	
  song	
  is	
  popular	
  
•  Hard	
  one:	
  the	
  previous	
  song	
  is	
  
rela:vely	
  unknown	
  (suffers	
  from	
  cold	
  
start).	
  	
  
Simple	
  case:	
  	
  
	
  
	
  
	
  
	
  
	
  
Query	
  song:	
  Shake	
  It	
  Off	
  by	
  Taylor	
  SwiL.	
  	
  
Best	
  next	
  song:	
  It’s	
  all	
  about	
  the	
  Bass	
  by	
  Meghan	
  Trainor.	
  	
  
	
  
CF	
  and	
  Prod2Vec	
  both	
  work!	
  
Hard	
  case:	
  	
  
	
  
	
  
	
  
	
  
	
  
Query	
  song:	
  S/ll	
  by	
  Taylor	
  SwiL,	
  but	
  is	
  one	
  of	
  her	
  earlier	
  songs,	
  
e.g.	
  You’re	
  Not	
  Sorry.	
  	
  
Best	
  next	
  song:	
  ?	
  
	
  	
  ?	
  
Hard	
  case	
  +	
  unlucky:	
  
	
  
•  Just	
  one	
  user	
  listened	
  to	
  You’re	
  Not	
  Sorry	
  
•  He	
  also	
  listened	
  to	
  Rammstein’s	
  Du	
  Hast!	
  
Hard	
  case	
  +	
  unlucky:	
  	
  
	
  
	
  
	
  
	
  
	
  
Your	
  Recommenda5on	
  Is	
  Not	
  Working!	
  
This	
  is	
  where	
  Meta-­‐Prod2Vec	
  comes	
  in	
  
handy!	
  
When	
  compu:ng	
  how	
  plausible	
  it	
  is	
  for	
  a	
  
user	
  to	
  like	
  a	
  pair	
  of	
  songs,	
  you	
  can	
  place	
  
addi5onal	
  constraints	
  by	
  taking	
  into	
  
account	
  the	
  song	
  ar5sts.	
  	
  
Prod2Vec	
  constraints	
  
	
  
	
   	
   	
   	
   	
  	
  	
  
You’re	
  not	
  sorry	
   Du	
  Hast	
  
P(Du	
  Hast|Youʹ′re	
  Not	
  Sorry)	
  -­‐>	
  the	
  next	
  song	
  depends	
  on	
  the	
  current	
  
song	
  
Prod2Vec	
  constraints	
  
	
  
	
   	
   	
   	
   	
  	
  	
  
You’re	
  not	
  sorry	
   Du	
  Hast	
  
Youʹ′re	
  Not	
  Sorry	
  is	
  a	
  fringe	
  song	
  -­‐>	
  low	
  evidence	
  for	
  the	
  posi/ve	
  and	
  
nega/ve	
  pairs 	
  	
  
Ar5st	
  metadata	
  constraints	
  
	
  
	
   	
   	
   	
   	
  	
  	
  
You’re	
  not	
  sorry	
   Du	
  Hast	
  
Taylor	
  SwiU	
   Rammstein	
  
However,	
  the	
  associated	
  singer	
  is	
  popular	
  -­‐>	
  good	
  evidence	
  that	
  Taylor	
  
SwiL	
  and	
  Rammstein	
  do	
  not	
  really	
  co-­‐occur	
  (have	
  distant	
  vectors) 	
  	
  
Ar5st	
  and	
  Song	
  constraints	
  (1)	
  
	
  
	
   	
   	
   	
   	
  	
  	
  
You’re	
  not	
  sorry	
   Du	
  Hast	
  
Taylor	
  SwiU	
   Rammstein	
  
Furthermore,	
  we	
  can	
  enforce	
  that	
  the	
  songs	
  and	
  their	
  ar5sts	
  should	
  be	
  
close... 	
  	
  
Ar5st	
  and	
  Song	
  constraints	
  (2)	
  
	
  
	
   	
   	
   	
  	
  
You’re	
  not	
  sorry	
   Du	
  Hast	
  
Taylor	
  SwiU	
   Rammstein	
  
Finally,	
  we	
  add	
  two	
  more	
  constraints	
  between	
  the	
  ar/sts	
  and	
  the	
  
previous/next	
  song	
  (they	
  s/ll	
  have	
  more	
  support	
  than	
  the	
  original	
  pairs)	
  
Meta-­‐Prod2Vec	
  constraints	
  
	
  
	
   	
   	
   	
   	
  	
  	
  
You’re	
  not	
  sorry	
   Du	
  Hast	
  
Taylor	
  SwiU	
   Rammstein	
  
#1.	
  P(Rammstein	
  |	
  Youʹ′re	
  Not	
  Sorry)	
  
the	
  ar/st	
  of	
  the	
  next	
  song	
  should	
  be	
  plausible	
  	
  
given	
  the	
  current	
  song	
  
	
  
#2.	
  P(Du	
  Hast	
  |	
  Taylor	
  SwiW)	
  
the	
  next	
  song	
  should	
  depend	
  on	
  the	
  	
  
current	
  ar/st	
  selec/on	
  
	
  
#3.	
  P(Youʹ′re	
  Not	
  Sorry	
  |Taylor	
  SwiW)	
  	
  
and	
  P(Du	
  Hast	
  |	
  Rammstein)	
  	
  
the	
  current	
  ar/st	
  selec/on	
  should	
  also	
  influence	
  	
  
the	
  current	
  song	
  selec/on	
  
	
  
#4.	
  P(Rammstein	
  |	
  Taylor	
  SwiW)	
  
the	
  probability	
  of	
  the	
  next	
  ar/st	
  should	
  	
  
be	
  high	
  given	
  the	
  current	
  ar/st.	
  	
  
PuXng	
  it	
  all	
  together:	
  
	
  
Meta-­‐Prod2Vec	
  loss 	
   	
  	
  	
  
 
Rela5onship	
  with	
  MF	
  with	
  Side-­‐Info:	
  
	
  
MP2V	
  Implementa5on	
  
	
  
•  No	
  changes	
  in	
  the	
  Word2Vec	
  code!	
  
•  Changes	
  just	
  in	
  the	
  input	
  pairs:	
  we	
  generate	
  
(propor:onally	
  to	
  the	
  importance	
  
hyperparameter)	
  4	
  addi:onal	
  types	
  of	
  pairs.	
  
Experimental	
  Results	
  
	
  
 
Task	
  &	
  Metrics	
  
	
  
Task:	
  Next	
  Event	
  Predic:on	
  
	
  
Metrics:	
  
•  Hit	
  ra:o	
  at	
  K	
  (HR@K)	
  	
  
•  Normalized	
  Discounted	
  Cumula:ve	
  Gain	
  
(NDCG@K)	
  	
  
 
Methods	
  
	
  
BestOf:	
  (rank	
  by)	
  popularity	
  
CoCounts:	
  cosine	
  similarity	
  of	
  candidate	
  item	
  to	
  query	
  item	
  	
  
Prod2Vec:	
  cosine	
  similarity	
  of	
  item	
  embedding	
  vectors	
  
Meta-­‐Prod2Vec:	
  cosine	
  similarity	
  of	
  improved	
  embedding	
  
vectors	
  
Mix(Prod2Vec,	
  CoCounts):	
  linear	
  combina:on	
  of	
  the	
  two	
  scores	
  
Mix(Meta-­‐Prod2Vec,	
  CoCounts):	
  same	
  as	
  previous	
  
 
Dataset:	
  30Music	
  Dataset	
  
	
  
•  playlists	
  data	
  from	
  Last.fm	
  API	
  
•  sample	
  of	
  100k	
  user	
  sessions	
  	
  
•  resul:ng	
  vocabulary	
  size:	
  433k	
  songs	
  
and	
  67k	
  ar:sts.	
  	
  
	
  
 
Global	
  Results	
  
	
  
Method	
   Type	
   HR@20	
   NDCG@20	
  
BestOf	
   Head	
   0.0003	
   0.002	
  
CoCounts	
   Head	
   0.0160	
   0.141	
  
Prod2Vec	
   Tail	
   0.0101	
   0.113	
  
MetaProd2Vec	
   Tail	
   0.0124	
   0.125	
  
Mix(Prod2Vec,	
  CoCounts)	
   Global	
   0.0158	
   0.152	
  
Mix(MetaProd2Vec,	
  CoCounts)	
   Global	
   0.0180	
   0.161	
  
 
Results	
  on	
  Cold	
  Start	
  (HR@20)	
  
	
  
	
   Method	
   Type	
   Pair	
  freq	
  =	
  0	
   Pair	
  freq	
  <	
  3	
  
BestOf	
   Head	
   0.0002	
   0.0002	
  
CoCounts	
   Head	
   0.0000	
   0.0197	
  
Prod2Vec	
   Tail	
   0.0003	
   0.0078	
  
MetaProd2Vec	
   Tail	
   0.0013	
   0.0198	
  
Mix(Prod2Vec,	
  CoCounts)	
   Global	
   0.0002	
   0.0200	
  
Mix(MetaProd2Vec,	
  CoCounts)	
   Global	
   0.0007	
   0.0291	
  
Conclusions	
  and	
  	
  
Next	
  Steps	
  
Conclusions	
  and	
  	
  
Next	
  Steps	
  
	
  
Using	
  side-­‐info	
  for	
  product	
  embeddings	
  
helps,	
  especially	
  on	
  cold-­‐start.	
  
	
  
Conclusions	
  and	
  	
  
Next	
  Steps	
  
•  Beeer	
  ways	
  to	
  mix	
  Head	
  and	
  Tail	
  
recommenda:on	
  methods	
  
•  Mix	
  CF	
  and	
  Meta-­‐Data	
  at	
  test	
  :me	
  	
  -­‐	
  product	
  
embeddings	
  using	
  all	
  available	
  signal	
  (CF,	
  
categorical,	
  text	
  and	
  image	
  product	
  
informa:on)	
  
 
Thanks!	
  	
  
 
Ques5ons?	
  

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Meta-Prod2Vec: Simple Product Embeddings with Side-Information

  • 1.     Meta-­‐Prod2Vec:  Simple  Product   Embeddings  with  Side-­‐Informa:on               Flavian  Vasile,  Elena  Smirnova  @Criteo   Alexis  Conneau  @FAIR  
  • 2. Contents   •  Product  Embeddings  for  Recommenda:on     •  Embedding  CF  signal:  Word2Vec  and  Prod2Vec   •  Meta-­‐Prod2vec:  Embedding  with  Side-­‐Informa:on     •  Experimental  Results   •  Conclusions  
  • 3. Product  Embeddings  for   Recommenda5on      
  • 4. Product  Embeddings  for   Recommenda5on       Represent  items  (and  some/mes  users)  as   vectors  in  the  same  space  and  use  their   distances  to  compute  recommenda/ons.  
  • 5. •  At  a  certain  level,  nothing  new!   •  We  already  had  Matrix  Factoriza/on   •  It  is  yet  another  way  of  crea/ng  latent   representa/ons  for  Recommenda/on  
  • 6. Some  of  the  NN  methods  can  be   translated  back  into  MF  techniques.       Differences:   •  new  ways  to  compute  matrix  entries     •  new  loss  func/ons  
  • 7. Where  do  we  fit?   •  Hybrid  model  that  uses  CF  with  content   side-­‐informa/on   •  Incursion  on  the  embedding  methods   using  side  info  
  • 8. Embedding  CF  signal:     Word2Vec  and  Prod2Vec    
  • 9. (Word-­‐to-­‐hidden  matrix)  x  (Hidden-­‐to-­‐Word  context  matrix)       Word2Vec:  Skip-­‐gram  
  • 10.   Word2Vec   In  this  space,  words  that  appear  in  similar  contexts   will  tend  to  be  close:    
  • 11.   The  same  idea  can  be  applied  to  other  sequen:al   data,  such  as  user  shopping  sessions  -­‐  Prod2vec.    
  • 12. Words  =  products   Sentences  =  shopping  sessions   Grbovic  et  al.  E-­‐commerce  in  Your  Inbox:  Product  RecommendaBons  at  Scale,  WWW  2013     Prod2Vec  
  • 13. Prod2Vec   The  resul:ng  embedding  will  co-­‐locate  products   that  appear  in  the  vicinity  of  the  same  products.    
  • 15. Meta-­‐Prod2vec:  Embedding  with  Side-­‐ Informa5on      
  • 16. Meta-­‐Prod2vec:  Embedding  with  Side-­‐ Informa5on       Idea:  Use  not  only  the  product  sequence   informa:on,  but  also  product  meta-­‐data.    
  • 17. Where  is  it  useful?   Product  cold-­‐start,  when  sequence   informa:on  is  sparse.  
  • 18. How  can  it  help?   We  place  addi:onal  constraints  on   product  co-­‐occurrences  using  external   info.     We  can  create  more  noise-­‐robust   embeddings  for  products  suffering  from   cold-­‐start.  
  • 19. Type  of  product  side-­‐informa5on:     •  Categories   •  Brands   •  Title  &  Descrip:on   •  Tags  
  • 20. How  does  Meta-­‐Prod2Vec  leverage  this   informa5on  for  cold-­‐start?  
  • 22. Let’s  say  we  are  trying  to  build  a   recommender  system  for  songs...    
  • 23. We  want  to  build  a  very  simple  solu5on   that  based  on  the  last  song  the  user   heard,  recommends  the  next  song.    
  • 24. Two  different  recommenda:on  situa:ons:     •  Simple:  the  previous  song  is  popular   •  Hard  one:  the  previous  song  is   rela:vely  unknown  (suffers  from  cold   start).    
  • 25. Simple  case:               Query  song:  Shake  It  Off  by  Taylor  SwiL.     Best  next  song:  It’s  all  about  the  Bass  by  Meghan  Trainor.       CF  and  Prod2Vec  both  work!  
  • 26. Hard  case:               Query  song:  S/ll  by  Taylor  SwiL,  but  is  one  of  her  earlier  songs,   e.g.  You’re  Not  Sorry.     Best  next  song:  ?      ?  
  • 27. Hard  case  +  unlucky:     •  Just  one  user  listened  to  You’re  Not  Sorry   •  He  also  listened  to  Rammstein’s  Du  Hast!  
  • 28. Hard  case  +  unlucky:               Your  Recommenda5on  Is  Not  Working!  
  • 29. This  is  where  Meta-­‐Prod2Vec  comes  in   handy!  
  • 30. When  compu:ng  how  plausible  it  is  for  a   user  to  like  a  pair  of  songs,  you  can  place   addi5onal  constraints  by  taking  into   account  the  song  ar5sts.    
  • 31. Prod2Vec  constraints                   You’re  not  sorry   Du  Hast   P(Du  Hast|Youʹ′re  Not  Sorry)  -­‐>  the  next  song  depends  on  the  current   song  
  • 32. Prod2Vec  constraints                   You’re  not  sorry   Du  Hast   Youʹ′re  Not  Sorry  is  a  fringe  song  -­‐>  low  evidence  for  the  posi/ve  and   nega/ve  pairs    
  • 33. Ar5st  metadata  constraints                   You’re  not  sorry   Du  Hast   Taylor  SwiU   Rammstein   However,  the  associated  singer  is  popular  -­‐>  good  evidence  that  Taylor   SwiL  and  Rammstein  do  not  really  co-­‐occur  (have  distant  vectors)    
  • 34. Ar5st  and  Song  constraints  (1)                   You’re  not  sorry   Du  Hast   Taylor  SwiU   Rammstein   Furthermore,  we  can  enforce  that  the  songs  and  their  ar5sts  should  be   close...    
  • 35. Ar5st  and  Song  constraints  (2)               You’re  not  sorry   Du  Hast   Taylor  SwiU   Rammstein   Finally,  we  add  two  more  constraints  between  the  ar/sts  and  the   previous/next  song  (they  s/ll  have  more  support  than  the  original  pairs)  
  • 36. Meta-­‐Prod2Vec  constraints                   You’re  not  sorry   Du  Hast   Taylor  SwiU   Rammstein   #1.  P(Rammstein  |  Youʹ′re  Not  Sorry)   the  ar/st  of  the  next  song  should  be  plausible     given  the  current  song     #2.  P(Du  Hast  |  Taylor  SwiW)   the  next  song  should  depend  on  the     current  ar/st  selec/on     #3.  P(Youʹ′re  Not  Sorry  |Taylor  SwiW)     and  P(Du  Hast  |  Rammstein)     the  current  ar/st  selec/on  should  also  influence     the  current  song  selec/on     #4.  P(Rammstein  |  Taylor  SwiW)   the  probability  of  the  next  ar/st  should     be  high  given  the  current  ar/st.    
  • 37. PuXng  it  all  together:     Meta-­‐Prod2Vec  loss        
  • 38.   Rela5onship  with  MF  with  Side-­‐Info:    
  • 39. MP2V  Implementa5on     •  No  changes  in  the  Word2Vec  code!   •  Changes  just  in  the  input  pairs:  we  generate   (propor:onally  to  the  importance   hyperparameter)  4  addi:onal  types  of  pairs.  
  • 41.   Task  &  Metrics     Task:  Next  Event  Predic:on     Metrics:   •  Hit  ra:o  at  K  (HR@K)     •  Normalized  Discounted  Cumula:ve  Gain   (NDCG@K)    
  • 42.   Methods     BestOf:  (rank  by)  popularity   CoCounts:  cosine  similarity  of  candidate  item  to  query  item     Prod2Vec:  cosine  similarity  of  item  embedding  vectors   Meta-­‐Prod2Vec:  cosine  similarity  of  improved  embedding   vectors   Mix(Prod2Vec,  CoCounts):  linear  combina:on  of  the  two  scores   Mix(Meta-­‐Prod2Vec,  CoCounts):  same  as  previous  
  • 43.   Dataset:  30Music  Dataset     •  playlists  data  from  Last.fm  API   •  sample  of  100k  user  sessions     •  resul:ng  vocabulary  size:  433k  songs   and  67k  ar:sts.      
  • 44.   Global  Results     Method   Type   HR@20   NDCG@20   BestOf   Head   0.0003   0.002   CoCounts   Head   0.0160   0.141   Prod2Vec   Tail   0.0101   0.113   MetaProd2Vec   Tail   0.0124   0.125   Mix(Prod2Vec,  CoCounts)   Global   0.0158   0.152   Mix(MetaProd2Vec,  CoCounts)   Global   0.0180   0.161  
  • 45.   Results  on  Cold  Start  (HR@20)       Method   Type   Pair  freq  =  0   Pair  freq  <  3   BestOf   Head   0.0002   0.0002   CoCounts   Head   0.0000   0.0197   Prod2Vec   Tail   0.0003   0.0078   MetaProd2Vec   Tail   0.0013   0.0198   Mix(Prod2Vec,  CoCounts)   Global   0.0002   0.0200   Mix(MetaProd2Vec,  CoCounts)   Global   0.0007   0.0291  
  • 46. Conclusions  and     Next  Steps  
  • 47. Conclusions  and     Next  Steps     Using  side-­‐info  for  product  embeddings   helps,  especially  on  cold-­‐start.    
  • 48. Conclusions  and     Next  Steps   •  Beeer  ways  to  mix  Head  and  Tail   recommenda:on  methods   •  Mix  CF  and  Meta-­‐Data  at  test  :me    -­‐  product   embeddings  using  all  available  signal  (CF,   categorical,  text  and  image  product   informa:on)