1. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Shopbot 2.0-Integrating recommendations and promotions with comparison shopping Presenter : Wu, Jia-Hao Authors : Robert Garfinkel, Ram Gopal, Bhavik Pathak,Fang Yin DSS (2008) 國立雲林科技大學 National Yunlin University of Science and Technology

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Shopbot Objective Experiment 1 Methodology Experiment 2 Conclusion Personal Comments

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation The current shopbots only focus compare prices of a single product of which they are already aware. As the electronic commerce continues to grow and the competition among online retailers becomes more intense, retailers turn to various strategies to attract sales on the web.

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Shopbot A kind of bot that searches the web to find the best price for a product you’re looking for. Shopbot Buyer Online Vendors Result Product User Best Price Purchase Request Domain Descript Vendor Descript

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Shopbot 2.0 If you pay the $25, you will have free shipping Price : 24.88 + 4.98 (Shipping cost) = 29.86 Price : 25.62 (Free Shipping) Relatedness score

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective The authors argue that shopbots are in the better position to offer such recommendations like choosing the best bet from the choice set. The authors develop integer programming models for shopbots to integrate sales promotions and product recommendations.

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiment 1 The authors collected recommendation data from Amazon.com Run the regression on the top 100,500,1000 Variable  Sales rank : the sales quantity of a book.  No. of reviews : the number of customer feedbacks for a book.  Average star : the aggregated rating for a book by customer.

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiment 1 The base items have lower sales rank and higher number of reviews. The result show that base item and items didn’t have necessarily related. It is obvious that retailers do not always recommend the most related items.

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Retailers might recommend items of profit maximization.  Inventory clearance and targeted promotions of writers or books.  The default choice for Amazon.com is always the top book in the list. The author’s method that choosing the optimal best bet.  Use the baseline savings, if base item with each item in the choice set that the savings are higher than the baseline savings, this item is marked as an best bet. Promotions  Free items : one free item can give you that you have high purchased items and at least amount of money is spent.  Dollars off coupons : a minimum purchase amount gets the shopper a coupon that can be used at the next time.  Free shipping : a minimum purchase amount gets the shopper free shipping.

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) An integer programming model  Choice set set be indexed by  x i be a binary variable indicating whether or not the i th book is purchased and paid for.  Similarly f i indicates whether that book is chosen to be received free.  The retailer price of the ith book in the choice set is p i, while p 0 is the price of the base item.

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) Free items :

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) Dollars off coupons :  There is a set of dollar-off coupons indexed by k = 1,…,.  An order of total expenditure no less than t k dollars yields a cost reduction of d k dollars off the total price.  Let y k be a binary variable indicating whether or not the kth coupon is used.

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) Free shipping :  Let z be a binary variable indicating whether or not the shipping is free.  If the total value of the order exceeds F dollars then shipping is free, otherwise the shipping cost is fixed at s dollars. Overall budget : Red line : free items, Blue line : Coupons, Green line : Shipping

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology (Cont.) Objective :  Let Li denote the list price of the ith book. The objective for the shopper’s economic gain maximization plus any saving from applicable promotions.

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiment 2 The average savings from author’s best bets for Amazon.com ($16.23) are 33% higher than the benchmark($12.19).

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion It is found that demand elasticity of recommendations does not change when the best bet recommended items are not from the choice set vs. when they are from the choice set. The result show that author’s method is better than Amazon.com. The online shopping website should give some promotions and recommendation.

17. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments Advantage  A good recommend model. Drawback  … Application  Electronic Commerce.  Online Shopping.