1. Learning to Question: Leveraging User Preferences for Shopping Advice Author : Mahashweta Das, Aristides Gionis, Gianmarco De Francisci Morales, and Ingmar Weber Presented by : Fei Shao

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)  Photo’s 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  R ANK SVM  Goal ： Learn a weight vector for the technical attributes of the products A > B B > C B > D. R ANK SVM model R ANK SVM model ABDC...ABDC... features Product’s technical attributes

12. 12 a1a2a3a4a5 Product A10111 Product B10010

13. ‚Learning the tree structure 13

14. 14 System result

15. ƒStopping criterion 15 1) 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.

16. Outline  Introduction  Method  Experiments  Conclusion 16

17. Datasets 17 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

18. Experiment setup 18

19. Quality evaluation 19

20. Comparison 20

21. Performance 21

22. Example of SA trees 22

23. Example of SA trees 23

24. Conclusion 24

25. Pros and Cons 25