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Learning to Question: Leveraging User Preferences for Shopping Advice Date : 2013/12/11 Author : Mahashweta Das, Aristides Gionis, Gianmarco De Francisci.

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Presentation on theme: "Learning to Question: Leveraging User Preferences for Shopping Advice Date : 2013/12/11 Author : Mahashweta Das, Aristides Gionis, Gianmarco De Francisci."— Presentation transcript:

1 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 : KDD13 Advisor : Jia-ling Koh Speaker : Yi-hsuan Yeh

2 Outline Introduction Method Experiments Conclusion 2

3 Introduction 3 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.

4 Introduction 4 S HOPPING A DVISOR is a novel recommender system that helps users in shopping for technical products. car

5 Introduction 5 S HOPPING A DVISOR 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.

6 Introduction 6 1. 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

7 Outline Introduction Method – L EARN SAT REE algorithm Experiments Conclusion 7

8 L EARN SAT REE algorithm 8 1. Table U (user) 2. Table P (product) 3. Table R (review) attributes users

9 *User attributes 9 1. Car (from Yahoo! Autos) Ex fuel economy, comfortable interior, stylish exterior 2. Camera (form Flickr) Photos tag topic Ex food topic (tags fruit, market)

10 Problem definition 10 1. Build tree 2. Rank products Top-k list of product recommendations

11 Learning product rankings 11 A > B B > C B > D. R ANK SVM model R ANK SVM model ABDC...ABDC... features Products technical attributes

12 12 a1a2a3a4a5 Product A10111 Product B10010

13 ‚Learning the tree structure 13

14 14 System result

15 15 user split

16 16 product R ANK SVM Count payoff ABDC...ABDC... FBEA...FBEA... Rank list

17 ƒStopping criterion 17 1) Grow the tree to its entirety 2) Post-pruning If a nodes child node is split by the near-synonomous tag trim the child node Example: travel vacation Employ pruning rules on the validation set.

18 Outline Introduction Method Experiments Conclusion 18

19 Datasets 19 1. 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

20 Experiment setup 20 1. S HOPPING A DVISOR Authors method 2. R ANK SVM The ranked list returned by S HOPPING A DVISOR at the root 3. k-NN k-nearest neighbors algorithm 4. SA.k-NN Features are selected from S HOPPING A DVISOR

21 Quality evaluation 21 average MRR ABD...ABD... System result ranking list 25 topics12 topics

22 Performance evaluation 22

23 Outline Introduction Method Experiments Conclusion 23

24 Conclusion Proposed a novel recommender system, S HOPPING A DVISOR, that helps users to shop for technical products. S HOPPING A DVISOR leverages both user preferences and technical product attributes in order to generate its suggestions. At each node, S HOPPING A DVISOR suggests a ranking of products matching the preferences of the user. Compared with a baseline, and demonstrated the effectiveness of the approach. 24


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