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A personalized recommendation procedure for Internet shopping support

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.

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A personalized recommendation procedure for Internet shopping support

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  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

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