Learning to Question: Leveraging User Preferences for Shopping Advice

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 : KDD’13 Advisor : Jia-ling Koh Speaker : Yi-hsuanYeh

2. Outline • Introduction • Method • Experiments • Conclusion

3. 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 updatesthat 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 • SHOPPINGADVISORis a novel recommender system that helps users in shopping for technical products. car

5. Introduction • SHOPPINGADVISORgenerates a tree-shaped flowchart, in which the internal nodes of the tree contain questionsinvolve only attributes from the user space. • non-expert users can understand easily.

6. Introduction • 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. • How to produce a suitable ranking at each node. • Learning-to-rank approach

7. Outline • Introduction • Method • LEARNSATREE algorithm • Experiments • Conclusion

8. LEARNSATREEalgorithm • Table U (user) attributes users • Table R (review) • Table P (product)

9. User attributes • Car (from Yahoo! Autos) Ex：fuel economy, comfortable interior, stylish exterior • Camera (form Flickr) • Photo’s tag topic Ex：food topic (tags：fruit, market)

10. Problem definition • Build tree • Rank products node A user attribute Top-k list of product recommendations

11. Learning product rankings • RANKSVM • Goal：Learn a weight vector for the technical attributes of the products A > B B > C B > D . . . A B D C . . . RANKSVM model features Product’s technical attributes

12. rank(A) rank(B)3

13. Learning the tree structure • Goal：determine the best user attribute “” to split at node

14. Example： Correctly-rank： System result System result eval(rank) eval(rank) (), (), () (), (), ()

15. user attribute Review table node split user

16. product Rank list F B E A . . . RANKSVM Count payoff A B D C . . . RANKSVM Consider all possible user attributes , and choose as splitter the one that maximizes the pay-off.

17. Stopping criterion • Grow the tree to its “entirety” • 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.

18. Outline • Introduction • Method • Experiments • Conclusion

19. Datasets • Car datasets • Yahoo! Autos • 606 cars, 60 attributes • 2180 reviews • 2180 user, 15 tags (as attributes) Ex：fuel economy, comfortable interior, stylish exterior • Camera datasets • Flickr tags • 645 cameras (CNET) • 11468 reviews • 5647 user, 25 topic tags (as attributes) Ex：food topic (tags：fruit, market) • Synthetic datasets • 200 products, 4000 comments, 1000 users

20. Experiment setup • SHOPPINGADVISOR • Author’s method • RANKSVM • The ranked list returned by SHOPPINGADVISOR at the root • k-NN • k-nearest neighbors algorithm • SA.k-NN • Features are selected fromSHOPPINGADVISOR

21. Quality evaluation 25 topics 12 topics System result ranking list average MRR A B D . . . If user prefer “B” 

22. Performance evaluation

23. Outline • Introduction • Method • Experiments • Conclusion

24. 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 productsmatching the preferences of the user. • Compared with a baseline, and demonstrated the effectiveness of the approach.