Learning to Question: Leveraging User Preferences for Shopping Advice

1. Learning to Question: Leveraging User Preferences for Shopping Advice Author: Mahashweta Das, Aristides Gionis, De Francisci Morales, Ingmar Weber Present by: Wei Zhu EECS, Case Western Reserve University

2. Introduction • E-commerce VS Traditional Way Shopping • Without any expert’s help • Online shops are big • Products update very fast • This paper present a novel recommender system to help users in shopping online by leveraging the user preferences and technical attributes of products. • Shopping Advisor

3. Outline • Background and inspiration • Problem definition • Method and algorithms • Experiments • Conclusion

4. Background • Marketing Strategy “Which product should I buy?” • By asking some questions, leading to suggested shopping options. • Drawbacks • Manually design question flowchart is time consuming • Some technical attributes are not easy to be understand

5. Inspiration • Shopping assistant • Do you intend to use to laptop to play modern games? • Instead of “How many GB of Ram do you need?” • User information can map the technical attributes to the features that non-expert can understand • Technical information can evaluate a product in different ranking in different using preference

6. Example

7. Problem Definition • Given a product table P, a review table R, a user table U and integer h and k, to learn a shopping advisor tree T, which can provide relevant recommendations. • Each internal node of the tree contains a question formed by a user information. • Each node contains a top-k ranked list of products • A shopper can start from the root and traverse down the tree, answering some questions. Then receive a top-k recommendations of products.

8. Example

9. Method • General framework for solving the Shopping Advisor problem • Payoff function: used to choose the best question to ask at any node of the Shopping Advisor tree • Rank function: used to determine the ranking of the products recommended to user

10. Learning the tree structure • Each Node of the Tree = attribute of the user • Given a user attribute a, the users U can be split into two group: • :match the attribute a • :do not match the attribute a • The user group corresponds to each node match all the attributes on the path from root to the node

11. Payoff function • To determine which attribute to split Uqat node q, this paper present a function called payoff • The idea here is to split the Uq into to sub groups, the users of which have similar preferences and rank the products in a similar way.

12. Learning product rankings • Rank the products in P at a given node q • Input: Uq, Product table P, Review table corresponding to UqRq • The goal is to learn a weight vector w={w1,…,wmp} • RankSVMmethed • Optimizing Objective function:

13. Evaluate function • Measure the number of correctly-ranked pairs in the ranking generated for the products Pq at node q. • Minimizing the number of inversions is the most common way to optimize a pairwise learning-to-rank function, which is appropriate to RankSVM

14. Experiments • Evaluate with both real and synthetic data • Compare the Shopping Advisor system with baseline system which not leveraging user performance • Performance evaluation • Example

15. Datasets • Cars: extract from Yahoo! Autos • 2180 users with 15 tags • 606 products with 60 attributes • 2180 reviews • Camera: extract from flickr • 5647 users with 25 attributes (tag topic) • 654 cameras (attributes from CNET) • 11468 • Synthetic • 1000 users with 20 tags • 200 products with 20 attributes • 4000 reviews

16. Experiment setup • Ten-fold cross-validation • Partition each of the datasets into training and test sets • Mean reciprocal rank (MRR) • How far from the top of the list the relevant item is. • Ranki is the position of the i-th test user’s relevant item

17. Quality evaluation • Baseline: RankSVM, (returned rank list at the root of Shopping Advisor) • Useful to existing recommendation techniques • k-NN: return a ranked list of items by aggregating the item lists for top neighbor users • SA.k-NN: use Shopping Advisor to select features and weights, then perform k-NN

18. Comparison • SA increase the quality by 50% • SA.k-NN can reach the same quality by asking less questions

19. Performance • RankSVM is expensive operation • Materialize a preference Matrix for all training instance to reduce the training time

20. Example of SA trees Observation: Include fuel economy: recommend hybrid and ecoboost cars Exclusion fuel economy: audi(compromise mileage for performance) Popularity: jeep grand cherokee

21. Example of SA trees Observation: For events: Olympus E-3 (lightweight) For people: Canon EOS 30D (auto image rotation function) Popularity: Canon EOS Digital Rebel XS

22. Conclusion • The system proposed by this paper can automatically build a question flowchart to help users find the recommendation products they need. • Besides, the idea of this paper can benefit other filtering methods such as k-NN

23. Pros and Cons • Pros • Building the decision tree from user feature space can make the node understandable to non-expert. • The idea can be applied to other existing method and improve the performance and quality • Cons • Popular products have high possibility to be recommend, but may not completely appropriate to the user’s need • Materializing a preference matrix takes several days.

24. Thank You!