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.
8
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)

9
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
15
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.
16
 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
.
.
.
21
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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