Constructing and Exploring Composite Items

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

2. Outline • Motivation • Three challenges • Maximal package construction • Summarization • Visual Effect • Experiments • Conculsion

3. Motivation • 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. budget satellite item Central item

4. Three challenges • 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 • To efficiently identify an ordering of the k packages which maximizes the visual effect of diversity.

5. Valid Packages

6. (Cont.)

7. (Cont.) • Compatible:

8. Example • To consider a user shopping an iPhone for less than $350

10. (Cont.) • To consider a user shopping an iPhone for less than $350

11. Maximal Packages

12. Summarization • 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.

13. (Cont.) • 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.

14. Visual Effect • 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.)

16. (Cont.)

17. (Cont.) • pv(p1,p2)=<0,0,0,0,0,0,1>

18. (Cont.) • pv(p2,p3)=<0,0,0,0,0,0,1>

19. (Cont.) • pv(p3,p4)=<0,0,0,0,0,0,1>

20. (Cont.) • The first ordering pv(p1,p2,p3,p4)=<0,0,0,0,0,0,3>

21. Maximal package construction • 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.) • 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.) • 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, S2pen} $34.95+$64.95+$9.95=$109.85>$101

24. (Cont.) • 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, S2pen} $34.95+$64.95+$9.95=$109.85>$101

25. (Cont.) • 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.) • 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.) • 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.) • 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.)

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

31. (Cont.) • The goal of summarization is to compute a set of k representative maximal packages Ic such that Coverage (Ic) is maximized. • Selecting p1 and p3 • 28-1+25-1-(23-1)=279

32. (Cont.) • Baseline Greedy algorithm: • Assume k=2 • Ic={} • Icp1 • Compute p2, p3, p4 with p1 coverage • argmaxp =p3 • Icp3 • return

33. (Cont.) • 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 FastGreedyalgo. to improve upon the performance and maintain the same approximation bound. • Key : using Bonferroni upper and lower bounding techniques ?

34. (Cont.) • 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.)

36. (Cont.)

37. (Cont.) • Algorithm 4 discovers the max. satellite packagep1={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

39. (Cont.)

40. (Cont.)

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

42. (Cont.)

43. (Cont.)

44. (Cont.)

45. Conclusion • 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 • To efficiently identify an ordering of the k packages which maximizes the visual effect of diversity.