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

2

Outline Introduction Method Experiments Conclusion

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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.

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Introduction SHOPPINGADVISOR is a novel recommender

system that helps users in shopping for technical products.

car

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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 users can understand easily.

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Introduction1. How to learn the structure of the tree, i.e., which

questions to ask at each node. Find the best user attribute to ask at each

node.

* This paper focus on identifying the attribute of interest, and not on the task of formulating the question in a human interpretable way.

2. How to produce a suitable ranking at each node. Learning-to-rank approach

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Outline Introduction Method

– LEARNSATREE algorithm Experiments Conclusion

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LEARNSATREE algorithm1. Table U (user)

2. Table P (product) 3. Table R (review)

attributes

users

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* User attributes1. Car (from Yahoo! Autos)

Ex ： fuel economy, comfortable interior, stylish exterior

2. Camera (form Flickr) Photo’s tag topic

Ex ： food topic (tags ： fruit, market)

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Problem definition

1. Build tree 2. Rank products

node

A user attribute

Top-k list of product recommendations

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① Learning product rankings RANKSVM

Goal ： Learn a weight vector for the technical attributes of the products

A > BB > C

B > D

.

.

.

RANKSVMmodel

ABDC

.

.

.

features

Product’s technical attributes

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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)

rank(B)3

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② Learning the tree structure Goal ： determine the best user attribute “” to

split at node

𝑠𝑢𝑚

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eval(rank) eval(rank)

(), (), () (), (), ()

Example ： Correctly-rank ：

System result

System result

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node

user

userattribut

e

split

𝑈𝑞

𝑈𝑞 (𝛼 )

𝑈𝑞 (𝛼 )

Review table

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product

RANKSVM

RANKSVM

Count payoff

ABDC...

FBEA...

Rank list

Consider all possible user attributes , and choose as splitter the one that maximizes the pay-off.

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③ Stopping criterion1) Grow the tree to its “entirety”2) Post-pruning

If a node’s child node is split by the “near-synonomous” tag trim the child node

Example: travel

vacation Employ pruning

rules on the validation set.

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Outline Introduction Method Experiments Conclusion

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Datasets1. Car datasets

• Yahoo! Autos• 606 cars, 60 attributes• 2180 reviews• 2180 user, 15 tags (as attributes)

Ex ： fuel economy, comfortable interior, stylish exterior

2. Camera datasets• Flickr tags• 645 cameras (CNET)• 11468 reviews• 5647 user, 25 topic tags (as attributes)

Ex ： food topic (tags ： fruit, market)

3. Synthetic datasets• 200 products, 4000 comments, 1000 users

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Experiment setup1. SHOPPINGADVISOR

Author’s method

2. RANKSVM The ranked list returned by SHOPPINGADVISOR at the

root

3. k-NN k-nearest neighbors algorithm

4. SA.k-NN Features are selected from SHOPPINGADVISOR

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Quality evaluation

average MRR A

BD

.

.

.

System result ranking list

25 topics

12 topics

If user prefer “B”

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Performance evaluation

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Outline Introduction Method Experiments Conclusion

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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.