A personalized recommendation procedure for Internet shopping support

1. A personalized recommendation procedure for Internet shopping support 指導教授：詹智強 李英聯 學生：王生鼎 Kim,J.K.,Cho,Y.H.,Kim,W.J.,Kim,J.R.,Suh,j.h.,(2002).”A personalized recommendation procedure for Internet shopping support”.Electronic Commerce Research and Applications,1,301-313.

2. Outline • Abstract • Introduction • Related works • Problem statement • Recommendation procedure • Performance evaluation • Conclusion

3. Abstract • The rapid growth of e-commerce • Introduce a personalized recommendation procedure • Based on • Web usage mining • product taxonomy • association rule mining • decision tree induction

4. Introduction（1/2） • More and more competition • New marketing strategies • Collaborative filtering (CF) • Web usage mining - accurate analysis • Clickstream - identify a variety of relations (Personalized recommendation)

5. Introduction（2/2） • Unqualified recommendations False negative and false positive • WebCF-DT （Web usage mining driven Collaborative Filtering-based recommendation procedure using Decision Tree）

6. Related works（1/3） • Web usage mining （data preparation and pattern discovery） • Association rule mining （minimum support and minimum confidence） • Product taxonomy • Decision tree induction

7. Related works（2/3）

8. Related works（3/3）

9. Problem statement • Classified 1.all customers or selective customers 2.prediction and top-N recommendation problem 3.specifictime or persistently • Product taxonomy association rule mining • Rec (l,n,p,t) Rec ( 3,2,1,2002-1-1) • Marketer may decide the l,n,p,t parameters

10. Recommendation procedure（1/12）

11. Recommendation procedure（2/12） • Determining active customers • Avoid the false positives decision tree • To build an effective model

12. Recommendation procedure（3/12） • Determining active customers • Let msst, pd, pl and pr • Rec(1,2,1,2001-12-1) msst=（May 1,2001）,pd=（4 months） pl=（1month）,pr=（1month）

13. Recommendation procedure（4/12） • Determining active customers

14. Recommendation procedure（5/12） • Determining active customers

15. Recommendation procedure（6/12） • Discovering product affinity

16. Recommendation procedure（7/12） • Discovering product affinity

17. Recommendation procedure（8/12） • Discovering customer preference click-through and basket placement

18. Recommendation procedure（9/12） • Discovering customer preference

19. Recommendation procedure（10/12） • Making recommendation

20. Recommendation procedure（11/12） • Making recommendation click-to-buy rate

21. Recommendation procedure（12/12） • Making recommendation

22. Performance evaluation（1/8） • Data set • Web log data from C Internet shopping mall（2001/05/01~2001/05/30） • 66,329 customers for 1904 products • 22,49,540 records,7208 purchase,60,892 basket-placement,2,181,440 click-through

23. Performance evaluation（2/8） • Data set • Selected 116 customers purchased in both periods • Train 2001/05/01~2001/05/24 （8960 records） • Test 2001/05/25~2001/05/30 （156 records）

24. Performance evaluation（3/8） • Data set • Build the decision tree • Used customer profiles and purchasing history（age,job,sex,registration,number of purchases,number of visits.etc） • Top level 10 product classes, next level 72 product classes, bottom level 3216 contains products

25. Performance evaluation（4/8） • Evaluation metrics

26. Performance evaluation（5/8） • Experiment results • Level of taxonomy • Number of recommended products • Selection of customers

27. Performance evaluation（6/8） • Determination of minimum support and minimum confidence two values from 0.1 to 0.9 increments of 0.05 （support value of 0.35） （confidence value of 0.5）

28. Performance evaluation（7/8） • Impact of the level of taxonomy choosing the right level of taxonomy plays a hugely important role

29. Performance evaluation（8/8） • Effect of active customer selection selected customers (labeled CS) all the customers (labeled CA)

30. Conclusion • Suggest WebCF-DT • Customer behavior pattern • Decision tree induction technique • Recommend （integrating product affinities and customer preferences） • Future collaborative filtering and rule-based approaches