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Ranking and Suggesting Popular Items
Aim
Quickly learn the true popularity ranking of items
Items are suggested to users
Tags applied to content such as photos (e.g., Flickr), videos (e.g., You Tube), or Web pages (e.g., del.icio.us) .
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Existing
In most existing social tagging applications, users are presented with tag suggestions that are made based on the history of tag selections
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Problem
Based on user feedback
Users selecting items based on their own preferences either from this suggestion set or from the set of all possible items
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Proposed
We propose simple randomized algorithms for ranking and suggesting popular items designed to account for popularity bias.
We focus on understanding the limit ranking of the items provided by the algorithms
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Introduction
Item Suggestion
Items are suggested to users to aid tasks such as browsing or tagging of the content.
Items could be search query keywords, documents, tags, etc.
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Algorithms
A Naïve Algorithm
TOP (Top popular)
Simple algorithm (baseline)
Rank score of an item equals number of selections of this item.
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Vi 3 2 1 2 1 1
Algorithms
Ranking rules
Rank rule 1
Simple ranking rule
Rank score for item i increases whenever a user selects this item.
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ρi 0.33 0.17 0.17 0.08 0.08 0.08
count 3 2 2 1 1 1
Algorithms
Ranking rules
Rank rule 2
Rank rule 1 may fail to discover true popularity order.
Here, rank score updated only for an item that was not suggested.
Slow rate of convergence
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ρi 0.33 0.17 0.11 0.03 0.04 0.04
count 12 13 1 1 1 2
Algorithms
Suggestion rules
PROP (Frequency Proportional)
randomized algorithm
Suggestion set is sampled with probability proportional to current rank score.
More robust to imitation than TOP
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Suggestion set
Algorithms
Suggestion rules
M2S (Move-to-set)
Suggest the last used item
Suggestion set updated only when a user selects an item that is not in S
Random iterative update rule of suggestion set
computationally very simple
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Suggestion set
Algorithms
Suggestion rules
FM2S (Frequency move-to-set)
can go to suggestion set only if sufficiently popular, w.r.t. true popularity
compared to M2S (previous page)
Not update counter for an item that were in suggested set
different from TOP (first simple alg.)
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Suggestion set
Algorithms
Suggestion rules
FM2S (Frequency move-to-set)
can go to suggestion set only if sufficiently popular, w.r.t. true popularity
Not update counter for an item that were in suggested set
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+3 +2
Wi 7 7 6 5 5 4 2 1
Analysis
TOP vs. FM2S
TOP fail to catch true distribution
100 times of item selection sampled from true preference distribution
when imitation probability (probability for selecting from suggestion set) is 0.5
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true 25 20 15 11 10 7 6 6
count 23 86 30 13 12 9 24 3
FM2S 12 12 13 11 9 9 12 3
Modules
Administrator
Customer
Administrator
Can add or update or delete products
Inventory
Queries
orders
Customer
Registration
View brands, categories and items
Search
Orders
CONCEPTS AND TECHNIQUES
Java Technology
JSP
Java Database Connectivity
Suggestion set
entire item set
Conclusion
Ranking and Suggesting Popular Items
propose randomized algorithms for ranking and suggesting popular itemsdesigned to account for popularity bias.
M2S and FM2S learn true popularity ranking that are lightweight
self-tuning in that they do not require any special configuration parameters
FM2S confines to displaying only sufficiently popular items
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naïve
not biase
d
ranking suggesting
Rank rule 2
Rank rule 1
FM2S
top-N rank score
(baseline)
Top Popular
Japanese
Hebrew
MerciFrench
Russian
DankeGerman
GrazieItalian
GraciasSpanish
Obrigado Portuguese
Arabic
Simplified Chinese
Traditional Chinese
Thai
Korean