adaptive web navigation for wireless devices corin anderson pedro domingos dan weld

Post on 19-Jan-2016

218 Views

Category:

Documents

0 Downloads

Preview:

Click to see full reader

TRANSCRIPT

Adaptive Web Navigationfor Wireless Devices

Corin Anderson

Pedro Domingos

Dan Weld

2

Web personalization

• Web sites designed “one size fits all”

• But one size does not fit all– Visitors will use the site in unforeseen ways– Browser may be resource-constrained– Unintuitive or impossible to use

• Personalization adapts and customizes content for each visitor

• In most need: mobile web visitors

3

c|net on the desktop

•1024x768 screen–Multi-column okay–Many links easily

visible

•Fast network–Hierarchical link

structure okay–Large images,

HTML pages

4

c|net on a wireless Palm

• Very small screen– Only few lines of text– Lots of scrolling

• Slow net connectivity– Following links costly

Challenge: automatically improve PC-centric web site for mobile browsing

5

Web site personalizers

• An intermediary between server and visitor

• Adapts and customizes site for each visitor

• Personalizations include:– Add shortcut links shorten long paths– Rearrange content to increase salience– Elide content, replacing with link

Visitor Personalizer Web server

6

Old and new

• 590AI – Autumn 2000– Proteus– Shortcut links and content elision– Key ideas: Expected utility and search

• 590AI – Spring 2001– MinPath– Shortcut links– Key ideas: clustering and predictive models

7

Trails

• A trail is a sequence of page requests…

• …coherent in time…

• …and coherent in space

8

Shortcuts

• Connect two previously unconnected pages

• Savings of shortcut is # links skipped– Don’t forget the link you follow – the shortcut

itself!

9

Shortcut link selection problem

• Given:– visitor V– trail prefix– maximum number of shortcuts m

• Output: – list of shortcuts

that minimize the number of links the visitor must follow to reach the visitor’s destination

ipp ,,0

mii qpqp ,,1

10

Finding shortcuts

• If we know the whole trail

• finding the right shortcut is easy

• Unfortunately, omniscience is hard to come by

ni ppp ,,,,0

ni pp

11

Expectation

• All we really know is the prefix

• We must guess the rest of the trail

• Idea:– Foreach suffix of trail on site

• Calculate the probability of that suffix• Add that probability to the shortcut to the end of

that suffix

– Return top m shortcuts

12

Predictive models

• We don’t really guess the suffix – we try all of them on the site

• We calculate each probability using a model of behavior

• [[ predict the next request given the past requests, the position in the trail, and the visitor’s identity ]]

• We’ve tried a number of variations…

13

Unconditional model

• Ignore visitor identity, trail prefix, position

• [[equation 1 from paper]]

• Of course, the visitor is bound to the links actually on the page; MinPath thus uses:

• [[next equation from paper]]

14

Naïve Bayes mixture

• Unconditional builds one model for everyone

• Intuitively, we suspect that not everyone behaves the same

• Cluster visitors, and condition prediction on cluster identity

15

Clustering

• Use Expectation-Maximization– Simultaneously cluster, build models

• Cluster sequences, not visitors

• …

16

Clusters of unconditional models

17

Markov models

• Condition on past history

• First order: one bit of history

• [[ equation ]]

• Also build mixtures of Markov models

18

Request position

• Training data suggest ordinal position important

• We’ll try adding position to unconditional and Markov models

19

Experiments

• Train models on 20 days of logs (35,000 trails)

• Test on 1.5 days (2,500 trails)

• All trails have length >= 3 (3 pages, 2 links)

• Measure performance as # links followed to reach destination

20

Results

• Compare different models

• Compare different cluster sizes

• Compare cluster assignment method

MinPath does save navigational effort Markov models offer most savings Clustering helps

21

figure 1 from ijcai paper

22

Figure 2 from ijcai paper

23

Clusters at www.cs

Anecdotal clusters from www.cs data

24

Summary

• Shortcuts for navigation

• cluster

• model

• save 40%

25

Ongoing work

• Proteus, MinPath feed on HTML and web graph

• But many sites are built from databases and templates

• What adaptations are possible given a declarative model of the site?

top related