Download - Transparent User Models for Personalization
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Khalid El-AriniCarnegie Mellon University
Joint work with:Ulrich Paquet, Ralf Herbrich, Jurgen Van Gael, Blaise Agüera
y Arcas
Transparent User Models for
Personalization
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Personalization is ubiquitous.
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• YouTube: 72+ hours/minute of new video• Facebook: 950 million+ users• Twitter: 400+ million tweets/day• Shopping:
[1994]: 500K unique consumer goods sold in U.S.[2010]: Amazon alone offered 24 million.
Personalization is invaluable.
Keyword search is not enough.
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Personalization is often wrong.
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- J. Zaslow, November 26, 2002
“Basil…is not a neo-Nazi. Lukas…is not a shadowy stalker.David…is not Korean.
intent on giving them such labels.”
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“there's just one way to change its mind: outfox it.” - J. Zaslow, November 26, 2002
What recourse do we have?
Can we do better?
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You behave like a
vegan hipster
Vegan? Really? Why?
You: • tweeted with #meatlessmonday• follow @WholeFoods• …
We propose an alternative.
Why am I getting this?
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We propose an alternative.
Why am I getting this?
You behave like a
Brooklyn hipster
Goal: Achieve transparency via interpretable user features, learned from user activity
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You behave like a
Brooklyn hipster
Goal: Achieve transparency via interpretable user features, learned from user activity
Badges
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Approach Model Experiments Summary
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1. Define a vocabulary of badges
Apple fanboy
…
vegan runner photographer
Rich, interpretable and explainable
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1. Define a vocabulary of badges
2. Identify exemplars
How do I find vegans?
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observed label
Take advantage of how users describe themselves
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Most vegans don’t label themselves as “vegan” on Twitter…
we want to infer the attributes of these users
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1. Define a vocabulary of badges
2. Identify exemplars3. Model characteristic
behavior• Hashtags #meatlessmonday• Retweets RT @WholeFoods
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Approach Model Experiments Summary
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• We have no negative training examples.Use a generative model.
• Actions can be explained by multiple badges, even for the same user.
Noisy-or to combine badges.• How do we deal with user corrections?
Observing a latent variable.
Model sketch
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i=1…B
B badges
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u=1…N
i=1…B
N users
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u=1…N
i=1…B
F actions j=1…F
j=1…F
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bi(u)
u=1…N
i=1…BDoes user u have badge i?
j=1…F
j=1…F
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bi(u) λi(u)
u=1…N
i=1…B
j=1…F
j=1…FDoes user u have label for
badge i in his profile?
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aj(u)
bi(u) λi(u)
j=1…F u=1…N
i=1…B
Has user u performed action j?
j=1…F
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sij
aj(u)
bi(u) λi(u)
j=1…F
j=1…F
u=1…N
i=1…B
Does badge i explain action j?
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sijφij
aj(u)
bi(u) wi(u)
αφβφj=1…F
j=1…F
u=1…N
i=1…B
What’s the probability that a user with badge i performs action j?
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sijφijφbg aj(u)
bi(u) wi(u)
αφβφj=1…F
j=1…F
u=1…N
i=1…B
What is the background probability for each action?
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sijφijφbg aj(u)
bi(u) wi(u)
αφβφj=1…F
j=1…F
u=1…N
i=1…B
noisy or:Can at least one of my badges (or the background) explain it?
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sijφijφbg aj(u)
bi(u) λi(u)
αφβφj=1…F
j=1…F
u=1…N
i=1…B
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sijφijφbg aj(u)
bi(u) λi(u)
αφβφj=1…F
j=1…F
u=1…N
i=1…B
Beta priors to control sparsity
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sijφijφbg aj(u)
bi(u) λi(u)
γiT γiF
αφβφ
αT βT αF βF
j=1…F
j=1…F
u=1…N
i=1…B
Beta prior to encode low recall (e.g., 10%)
Beta prior to encode high precision
(e.g., 99.9%)
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ηisijφijφbg aj(u)
bi(u) λi(u)
γiT γiFωi
αφβφ
αη βη αω βω αT βT αF βF
j=1…F
j=1…F
u=1…N
i=1…B
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• Collapsed Gibbs sampler (with MH steps)
Inference
sijφijφbg
bi(u)
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ηisijφijφbg aj(u)
bi(u) λi(u)
γiT γiFωi
αφβφ
αη βη αω βω αT βT αF βF
j=1…F
j=1…F
u=1…N
i=1…BYou behave like a
vegan hipster.
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ηisijφijφbg aj(u)
bi(u) λi(u)
γiT γiFωi
αφβφ
αη βη αω βω αT βT αF βF
j=1…F
j=1…F
u=1…N
i=1…BYou behave like a
vegan hipster.
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Approach Model Experiments Summary
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• Start with 7 million Twitter users• Manually define 31 sample badges
by specifying labels
Data description
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• Start with 7 million Twitter users• Manually define 31 sample badges by
specifying labels• Gather 2 million tweets from August
2011• Recall: actions are hashtags and
retweets
Remove infrequent actions and inactive users, leaving us with:
75,880 users32,030 actions
Data description
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 310
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
Chart Title
Badges
artist
photographer
country music fan
book worm
Badge statistics
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Can we learn badges?
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Vegetarian badge
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Runner badge
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Hacker badge
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Manchester United badge
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Do all badges look this good?
No, but most do.
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45wine lover
Over-generalized
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Overwhelmed
Ruby on Rails
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Can we just use the labels directly?
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Inferred Apple fanboy badge
Self-described Apple fanboys
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• Compare to labeled LDA [Ramage+ 2009]– LDA extension where each document is
labeled with multiple tags– One-to-one mapping between topics and tags– Document explained only by topics
associated with its tags
• Hold out random 10% of labels, treat as ground truth, and try to predict them
Comparative Analysis
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Rank of held-out labels be
tter
Better predictiveperformance
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bett
erBetter predictions for active
users
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Sparse badges
Apple fanboy (badges) Apple fanboy (l-lda)
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Approach Model Experiments Summary
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Leveraged how users describe themselves
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Leveraged how users describe themselves to build interpretable user features You behave like a
vegan hipster
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Empirically showed we can infer a user’s attributes from his behavior
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谢谢
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What recourse do we have?
Collaborative filtering
Content-based filtering
Can we do better?
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Most vegans don’t label themselves as “vegan” on Twitter……but what about non-vegans?
“I drink too much and hate vegans.”