1. S. B. Roy, S. A.-Yahia, A. Chawla, G. Das, and C. Yu SIGMOD 2010 Constructing and Exploring Composite Items 2011/4/14 1

2. Outline 2011/4/14 2 Motivation Three challenges Maximal package construction Summarization Visual Effect Experiments Conculsion

3. Motivation 2011/4/14 3 Nowadays, online shopping has become a daily activity. While many online sites are still centered around facilitating a user’s interatction with individual items, an increasing emphasis, composite items, is being put on helping users. Central item budget satellite item

4. Three challenges 2011/4/14 4 The goal of this work is to develop a principled approach for constructing composite items and helping users explore them efficiently and effectively.  To identify all valid and maximal satellite packages with a central item. ‚ To summarize the packages associated with a central item into k representative packages ƒ To efficiently identify an ordering of the k packages which maximizes the visual effect of diversity.

5. Valid Packages 2011/4/14 5

6. (Cont.) 2011/4/14 6

7. (Cont.) 2011/4/14 7 Compatible:

8. Example 2011/4/14 8 To consider a user shopping an iPhone for less than $350

9. 2011/4/14 9

10. (Cont.) 2011/4/14 10 To consider a user shopping an iPhone for less than $350

11. Maximal Packages 2011/4/14 11 iPhone3G /8GB S4caseS2chargerS1kitS4screenS1penTotal cost $99$39.95$99$24.95$66$19.95$348.85 iPhone 3GS/8GB S2speaker $199$149$348

12. Summarization 2011/4/14 12 Maximal package can still become very large in practice. Different maximal packages associated with the same central item, may overlap significantly in their satellite items. iPhone 3G/16GB S2caseS4chargerS3cableS3speaker-- iPhone 3G/16GB S2caseS4charger-S3speakerS3screenS1pen

13. (Cont.) 2011/4/14 13 Maximal package can still become very large in practice. Different maximal packages associated with the same central item, may overlap significantly in their satellite items. Hence, this paper further propose to summarize maximal packages into a smaller set Ic, summary set, containing k representative packages. iPhone 3G/16GB S2caseS4chargerS3cableS3speaker-- iPhone 3G/16GB S2caseS4charger-S3speakerS3screenS1pen

14. Visual Effect 2011/4/14 14 After obtaining k summary packages, how to effectively present them to the user. It use diversity to rank the summary packages to avoid presenting a package that is too similar to a package the user has just seen. This paper introduce the notion of satellite type prioritization. One user looking for an iPhone may prefer seeing variety in chargers over in speakers One user may prefer variety in protective screens over in cables.

15. (Cont.) 2011/4/14 15

16. (Cont.) 2011/4/14 16

17. (Cont.) 2011/4/14 17 pv(p 1,p 2 )=

18. (Cont.) 2011/4/14 18 pv(p 2,p 3 )=

19. (Cont.) 2011/4/14 19 pv(p 3,p 4 )=

20. (Cont.) 2011/4/14 20 The first ordering pv(p 1,p 2,p 3,p 4 )=

21. Maximal package construction 2011/4/14 21 Central item: iPhone 3G/16GB  $199 Budget: $300  the budget for the satellite package is $101 Assume there are 5 satellite items : S1kit($24.95), S3cable($34.95), S3speaker($64.95), S4screen($66), S2pen($9.95)  {S3cable}  $34.95<$101

22. (Cont.) 2011/4/14 22 Central item: iPhone 3G/16GB  $199 Budget: $300  the budget for the satellite package is $101 Assume there are 5 satellite items : S1kit($24.95), S3cable($34.95), S3speaker($64.95), S4screen($66), S2pen($9.95)  {S3cable}  $34.95<$101 ‚ {S3cable, S3speaker}  $34.95+$64.95=$99.9<$101

23. (Cont.) 2011/4/14 23 Central item: iPhone 3G/16GB  $199 Budget: $300  the budget for the satellite package is $101 Assume there are 5 satellite items : S1kit($24.95), S3cable($34.95), S3speaker($64.95), S4screen($66), S2pen($9.95)  {S3cable}  $34.95<$101 ‚ {S3cable, S3speaker}  $34.95+$64.95=$99.9<$101 ƒ {S3cable, S3speaker, S2pen}  $34.95+$64.95+$9.95=$109.85>$101

24. (Cont.) 2011/4/14 24 Central item: iPhone 3G/16GB  $199 Budget: $300  the budget for the satellite package is $101 Assume there are 5 satellite items : S1kit($24.95), S3cable($34.95), S3speaker($64.95), S4screen($66), S2pen($9.95)  {S3cable}  $34.95<$101 ‚ {S3cable, S3speaker}  $34.95+$64.95=$99.9<$101 ƒ {S3cable, S3speaker, S2pen}  $34.95+$64.95+$9.95=$109.85>$101

25. (Cont.) 2011/4/14 25 Central item: iPhone 3G/16GB  $199 Budget: $300  the budget for the satellite package is $101 Assume there are 5 satellite items : S1kit($24.95), S3cable($34.95), S3speaker($64.95), S4screen($66), S2pen($9.95)  {S3cable}  $34.95<$101 ‚ {S3cable, S3speaker}  $34.95+$64.95=$99.9<$101 „ {S3cable, S3speaker}is a maximal package

26. (Cont.) 2011/4/14 26 Central item: iPhone 3G/16GB  $199 Budget: $300  the budget for the satellite package is $101 Assume there are 5 satellite items : S1kit($24.95), S3cable($34.95), S3speaker($64.95), S4screen($66), S2pen($9.95)  {S3cable}  $34.95<$101 ‚ {S3cable, S3speaker}  $34.95+$64.95=$99.9<$101 „ {S3cable, S3speaker}is a maximal package … To judge {S3cable, S3speaker}whether exist Mc

27. (Cont.) 2011/4/14 27 Central item: iPhone 3G/16GB  $199 Budget: $300  the budget for the satellite package is $101 Assume there are 5 satellite items : S1kit($24.95), S3cable($34.95), S3speaker($64.95), S4screen($66), S2pen($9.95)  {S3cable}  $34.95<$101 ‚ {S3cable, S3speaker}  $34.95+$64.95=$99.9<$101 „ {S3cable, S3speaker}is a maximal package … To judge {S3cable, S3speaker}whether exist Mc † If it doesn’t exist, count ({S3cable, S3speaker})=1

28. (Cont.) 2011/4/14 28 Central item: iPhone 3G/16GB  $199 Budget: $300  the budget for the satellite package is $101 Assume there are 5 satellite items : S1kit($24.95), S3cable($34.95), S3speaker($64.95), S4screen($66), S2pen($9.95)  {S3cable}  $34.95<$101 ‚ {S3cable, S3speaker}  $34.95+$64.95=$99.9<$101 „ {S3cable, S3speaker}is a maximal package … To judge {S3cable, S3speaker}whether exist Mc † If it exists, count ({S3cable, S3speaker})++

29. (Cont.) 2011/4/14 29

30. Summarization 2011/4/14 30 The goal of summarization is to compute a set of k representative maximal packages Ic such that Coverage (Ic) is maximized.

31. (Cont.) 2011/4/14 31 The goal of summarization is to compute a set of k representative maximal packages Ic such that Coverage (Ic) is maximized. Selecting p 1 and p 3 2 8 -1+2 5 -1-(2 3 -1)=279

32. (Cont.) 2011/4/14 32 Baseline Greedy algorithm: Assume k=2 Ic={} Ic  p1 Compute p2, p3, p4 with p1 coverage argmax p =p3 Ic  p3 return

33. (Cont.) 2011/4/14 33 Because of the need to compute the coverage of multiple sets at each iteration, baseline greedy algo. Can still be quite expensive in practice. It proposed FastGreedy algo. to improve upon the performance and maintain the same approximation bound. Key : using Bonferroni upper and lower bounding techniques ?

34. (Cont.) 2011/4/14 34 In practice, the number of maximal packages can be large and limits how fast the summary can be generated. It describes a randomized algo. to produce k representative packages directly from the set of compatible satellite items. It makes similar random walks to generate a set of maximal packages. Two differences: It stops as soon as k packages are generated. Each random walk invoked from within Algorithm 4 is designed to generate a package that is as different as possible from the packages already discovered by the previous random walks.

35. (Cont.) 2011/4/14 35

36. (Cont.) 2011/4/14 36

37. (Cont.) 2011/4/14 37 Algorithm 4 discovers the max. satellite package p1={s1kit, s3speaker, s2 pen} at the first iteration In the second iteration, the probabilities of the items that appear in p1 are reduced. S1kit gets 16% probability of being chosen at second iteration, compared against its 20% probability in the fisrt iteration.

38. Visual Effect 2011/4/14 38

39. (Cont.) 2011/4/14 39

40. (Cont.) 2011/4/14 40

41. Experiments 2011/4/14 41 The number of maximal packages grows quickly As the price budget goes up As the number of compatible satellite items increases

42. (Cont.) 2011/4/14 42

43. (Cont.) 2011/4/14 43

44. (Cont.) 2011/4/14 44

45. Conclusion 2011/4/14 45 In this paper, it designs and implements efficient algorithms to address three chanllenges.  To identify all valid and maximal satellite packages with a central item. ‚ To summarize the packages associated with a central item into k representative packages ƒ To efficiently identify an ordering of the k packages which maximizes the visual effect of diversity.