Grouping hyperlinks for improved voice/mobile accessibility Alex Penev UNSW / NICTA (Australia)

Accessibility Heavily presentation-oriented web blind users “read” sequentially layout gets in the way

Heavily presentation-oriented web

blind users “read” sequentially

layout gets in the way

Accessibility Heavily presentation-oriented web blind users “read” sequentially layout gets in the way mobile users have small-screen devices For some users, “less is more”

Heavily presentation-oriented web

blind users “read” sequentially

layout gets in the way

mobile users have small-screen devices

For some users, “less is more”

Hyperlinks Often visit a site with intention of following a link

Often visit a site with intention of following a link

Hyperlinks Often visit a site with intention of following a link Idea: cluster links partition a page’s links into topical groups helps find a link (cf. IVR) way to organize a set of links

Often visit a site with intention of following a link

Idea: cluster links

partition a page’s links into topical groups

helps find a link (cf. IVR)

way to organize a set of links

Example: Centrelink.gov.au Government agency, human services Population obtains info, forms, etc. …but do not visit regularly information need

Government agency, human services

Population obtains info, forms, etc.

…but do not visit regularly

information need

Centrelink 111 links on the page Some problems for blind users: pop-up menus flatten it?

111 links on the page

Some problems for blind users:

pop-up menus

flatten it?

Centrelink 111 links on the page Some problems for blind users: pop-up menus flatten it? Some problems for mobiles: hard to fit/navigate a 3-tier pop-up menu

111 links on the page

Some problems for blind users:

pop-up menus

flatten it?

Some problems for mobiles:

hard to fit/navigate a 3-tier pop-up menu

Link clustering Group related links together hopefully in topical groups

Group related links together

hopefully in topical groups

Link clustering Group related links together hopefully in topical groups How? label links with descriptions partition descriptions assign summary label to each cluster

Group related links together

hopefully in topical groups

How?

label links with descriptions

partition descriptions

assign summary label to each cluster

Link clustering Navigation process: invoke clustering step presented with clusters advertise a label

Navigation process:

invoke clustering step

presented with clusters

advertise a label

Link clustering Navigation process: invoke clustering step presented with clusters advertise a label select best-sounding cluster layout is lost Faster than serial reading

Navigation process:

invoke clustering step

presented with clusters

advertise a label

select best-sounding cluster

layout is lost

Faster than serial reading

Forming descriptions A set of keywords for each link anchor text, nested tags, URL, content

A set of keywords for each link

anchor text, nested tags, URL, content

Forming descriptions A set of keywords for each link anchor text, nested tags, URL, content further possibilities: anchor text window, folksonomy tags, <meta>, ontology Example australia.gov.au australia, gov, government, website, travel, taxes, business, people, immigration, etc.

A set of keywords for each link

anchor text, nested tags, URL, content

further possibilities: anchor text window, folksonomy tags, <meta>, ontology

Example

australia.gov.au

australia, gov, government, website, travel, taxes, business, people, immigration, etc.

Forming descriptions Descriptions can be short and have little overlap Solution?

Descriptions can be short and have little overlap

Solution?

Forming descriptions Descriptions can be short and have little overlap Solution? take into account layout by spreading a link’s influence across DOM borrow descriptions from neighbor links decay weight with distance

Descriptions can be short and have little overlap

Solution?

take into account layout by spreading a link’s influence across DOM

borrow descriptions from neighbor links

decay weight with distance

Clustering step Propagation enriches descriptions relates neighboring links less sparse feature vectors good for clustering! Use a partitioning algorithm e.g. k-means

Propagation enriches descriptions

relates neighboring links

less sparse feature vectors

good for clustering!

Use a partitioning algorithm

e.g. k-means

Possible uses Blind users navigate to link via clustering skip layout, skip uninteresting topics general surfing?

Blind users

navigate to link via clustering

skip layout, skip uninteresting topics

general surfing?

Possible uses Blind users navigate to link via clustering skip layout, skip uninteresting topics general surfing? Mobile users portal pages

Blind users

navigate to link via clustering

skip layout, skip uninteresting topics

general surfing?

Mobile users

portal pages

Sample results - Centrelink 12 clusters agency, site, main sections, welfare allowances, study & training, health, carers, family, children, maternity & job search/retirement, farming, online security Full table in paper

12 clusters

agency, site, main sections, welfare allowances, study & training, health, carers, family, children, maternity & job search/retirement, farming, online security

Full table in paper

Conclusion Simple, well-known ideas applied to a different problem VSM (tf.idf), clustering May help voice/mobile browsing Future work what is a good description? what is a good label?

Simple, well-known ideas applied to a different problem

VSM (tf.idf), clustering

May help voice/mobile browsing

Future work

what is a good description?

what is a good label?