Automatic Selection of Social Media Responses to News
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Transcript Automatic Selection of Social Media Responses to News
Learning to Question: Leveraging
User Preferences for Shopping
Advice
Date : 2013/12/11
Author : Mahashweta Das, Aristides Gionis,
Gianmarco De Francisci Morales,
and Ingmar Weber
Source : KDD’13
Advisor : Jia-ling Koh
Speaker : Yi-hsuan Yeh
Outline
Introduction
Method
Experiments
Conclusion
2
Introduction
Motivation
•
Customers shop online, from their homes, without any
human interaction involved.
•
Catalogs of online shops are so big and with so many
continuous updates that no human, however expert, can
effectively comprehend the space of available products.
Use a flowchart asks the shopper a question, and the
sequence of answers leads the shopper to the
suggested shopping option.
3
Introduction
SHOPPINGADVISOR is a novel recommender system that
helps users in shopping for technical products.
car
4
Introduction
SHOPPINGADVISOR generates a tree-shaped flowchart, in
which the internal nodes of the tree contain questions
involve only attributes from the user space.
non-expert
5
users can understand easily.
Introduction
How to learn the structure of the tree, i.e., which
questions to ask at each node.
1.
*
2.
This paper focus on identifying the attribute of
interest, and not on the task of formulating the
question in a human interpretable way.
How to produce a suitable ranking at each node.
6
Find the best user attribute to ask at each node.
Learning-to-rank approach
Outline
Introduction
Method
–
LEARNSATREE algorithm
Experiments
Conclusion
7
LEARNSATREE algorithm
1.
Table U (user)
attributes
users
2.
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Table P (product)
3.
Table R (review)
* User attributes
Car (from Yahoo! Autos)
1.
Ex:fuel economy, comfortable interior, stylish exterior
Camera (form Flickr)
2.
Photo’s tag topic
Ex:food topic (tags:fruit, market)
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Problem definition
1.
Build tree
2.
Rank products
node 𝑞
A user attribute 𝛼
Top-k list of
product
recommendations
10
Learning product rankings
RANKSVM
A>B
B>C
B>D
features
RANKSVM
model
.
.
.
Product’s technical
attributes
A
B
D
C
.
.
.
Goal:Learn a weight vector 𝑤 = 𝑤1 , … , 𝑤𝑚𝑝 for the
𝑚𝑝 technical attributes of the products 𝑃
11
a1
a2
a3
a4
a5
Product A
1
0
1
1
1
Product B
1
0
0
1
0
𝑤 = 0.2, 0.1, 0.5, 0.1, 0.1
rank(A) = 0.2 + 0 + 0.5 + 0.1 + 0.1 = 0.9
rank(B) = 0.2 + 0 + 0 + 0.1 + 0 = 0.3
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Learning the tree structure
Goal:determine the best user attribute “𝛼” to split 𝑈𝑞
at node 𝑞
𝑠𝑢𝑚
13
Example:
System result
𝑝1
𝑝2
𝑝3
Correctly-rank:𝑝1 > 𝑝2 > 𝑝3
eval(rank) =
2∗3
3∗(3−1)
=1
(𝑝1 , 𝑝2 ), (𝑝1 , 𝑝3 ), (𝑝2 , 𝑝3 )
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System result
𝑝1
𝑝3
𝑝2
eval(rank) =
2∗2
3∗(3−1)
= 0.66
(𝑝1 , 𝑝3 ), (𝑝1 , 𝑝2 ), (𝑝3 , 𝑝2 )
user
attribute
𝛼
𝑈𝑞 𝛼
node 𝑞
split
user
𝑈𝑞
𝑈𝑞 𝛼
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Review table 𝑅
product
Rank list
RANKSVM
F
B
E
A
.
.
.
RANKSVM
A
B
D
C
Count
payoff
.
.
.
Consider all possible user attributes 𝛼, and choose as splitter the one that
maximizes the pay-off.
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Stopping criterion
1)
2)
Grow the tree to its “entirety”
Post-pruning
If a node’s child node is split by the “nearsynonomous” tag
trim the child node
Example:
vacation
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travel
Employ pruning rules
on the validation set.
Outline
Introduction
Method
Experiments
Conclusion
18
Datasets
Car datasets
1.
•
•
•
•
Yahoo! Autos
606 cars, 60 attributes
2180 reviews
2180 user, 15 tags (as attributes)
Ex:fuel economy, comfortable interior, stylish exterior
Camera datasets
2.
•
•
•
•
Flickr tags
645 cameras (CNET)
11468 reviews
5647 user, 25 topic tags (as attributes)
Ex:food topic (tags:fruit, market)
Synthetic datasets
3.
•
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200 products, 4000 comments, 1000 users
Experiment setup
SHOPPINGADVISOR
1.
Author’s
method
RANKSVM
2.
The
ranked list returned by SHOPPINGADVISOR at the root
k-NN
3.
k-nearest neighbors algorithm
SA.k-NN
4.
Features
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are selected from SHOPPINGADVISOR
Quality evaluation
25 topics
12 topics
System result ranking list
average MRR
A
B
D
.
.
.
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If user prefer “B”
1
1
𝑟𝑎𝑛𝑘 = 2
𝑖
Performance evaluation
22
Outline
Introduction
Method
Experiments
Conclusion
23
Conclusion
Proposed a novel recommender system,
SHOPPINGADVISOR, that helps users to shop for technical
products.
SHOPPINGADVISOR leverages both user preferences and
technical product attributes in order to generate its
suggestions.
At each node, SHOPPINGADVISOR suggests a ranking of
products matching the preferences of the user.
Compared with a baseline, and demonstrated the
effectiveness of the approach.
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