Constructing and Exploring
Composite Items
S. B. Roy, S. A.-Yahia, A. Chawla, G. Das, and C.Yu
SIGMOD 2010
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Outline
 Motivation
 Three challenges
 Maximal package construction
 Summarization
 Visual Effect
 Experiments
 Conculsion
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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
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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.
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Valid Packages
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(Cont.)
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(Cont.)
 Compatible:
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Example
 To consider a user shopping an iPhone for less than $350
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(Cont.)
 To consider a user shopping an iPhone for less than $350
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Maximal Packages
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iPhone3G S4case
/8GB
S2charger S1kit
S4screen
S1pen
Total cost
$99
$39.95
$99
$66
$19.95
$348.85
iPhone
3GS/8GB
S2speaker
$199
$149
$24.95
$348
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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.
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iPhone
3G/16GB
S2case
S4charger S3cable
S3speaker -
-
iPhone
3G/16GB
S2case
S4charger -
S3speaker S3screen
S1pen
2011/4/14
(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.
iPhone
3G/16GB
S2case
S4charger S3cable
S3speaker -
-
iPhone
3G/16GB
S2case
S4charger -
S3speaker S3screen
S1pen
 Hence, this paper further propose to summarize maximal
packages into a smaller set Ic, summary set, containing k
representative packages.
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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.
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(Cont.)
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(Cont.)
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(Cont.)
 pv(p1,p2)=<0,0,0,0,0,0,1>
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(Cont.)
 pv(p2,p3)=<0,0,0,0,0,0,1>
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(Cont.)
 pv(p3,p4)=<0,0,0,0,0,0,1>
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(Cont.)
 The first ordering pv(p1,p2,p3,p4)=<0,0,0,0,0,0,3>
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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
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(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
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(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
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(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
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(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
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(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
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(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
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(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})++
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(Cont.)
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Summarization
 The goal of summarization is to compute a set of k
representative maximal packages Ic such that Coverage (Ic) is
maximized.
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(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
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(Cont.)
 Baseline Greedy algorithm:
 Assume k=2
 Ic={}
 Icp1
 Compute p2, p3, p4 with
p1 coverage
 argmax p =p3
 Icp3
 return
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(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 FastGreedy algo. to improve upon the
performance and maintain the same approximation bound.
 Key : using Bonferroni upper and lower bounding techniques
?
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(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.
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(Cont.)
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(Cont.)
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(Cont.)
 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.
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S1kit gets 16% probability of being chosen at second
iteration, compared against its 20% probability in the fisrt
iteration.
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Visual Effect
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(Cont.)
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(Cont.)
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Experiments
 The number of maximal packages grows quickly
 As the price budget goes up
 As the number of compatible satellite items increases
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(Cont.)
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(Cont.)
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(Cont.)
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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.
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