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) .

2

Existing

In most existing social tagging applications, users are presented with tag suggestions that are made based on the history of tag selections

3

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

4

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

5

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.

6

Algorithms

A Naïve Algorithm

TOP (Top popular)

Simple algorithm (baseline)

Rank score of an item equals number of selections of this item.

7

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.

8

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

9

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

10

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

11

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

13

+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

14

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

19

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