SEO and IA: The Beginning of a Beautiful Friendship

2007 IA Summit: Las Vegas SEO and IA: The Makings of a Beautiful Friendship

Introduction
Me: Information architect/Search specialist
IA since 1998
Search since 2004
Topic: Search engine optimization and IA
Shift in user locus of attention
From navigation to search box
Shift in our locus of attention
From macro-structure to micro-wayfinding
What I want
IA to become a partner in developing search technology that works with the user
IA community to “think” about how users find their websites when they design them
Key takeaways [fingers crossed at my end]
Search optimization and IA can and should co-exist
One should not exist at the expense of the other

Search Usability
Web analytics show preference for search box over any site navigation of any kind
Search enables users to develop a need-specific/use-specific information path
Search engine users visit more pages than those using navigation
Pogo effect
Ask.com now offers preview service so user does not have to click through
How much of the navigation will they see in a thumbnail?
Out of the top 20 results and you are out of sight and out of mind for a majority of users

Blame it all on Google
PageRank is a pre-query valuation
Based on number of links to the page
1 link=1 vote
Most votes wins top placement
Has no relationship to the subject of the query
Googlearchy : dominant Web sites become more firmly entrenched in search results by nature of size
Link rich get richer
Failings soon uncovered
Link farms
Googlehacks

Search 2.0
Web 2.0 give us Search 2.0
Harnessing the collective intelligence
Online bookmarking
Architecture of participation
Open source Search
Peer-to-peer Search
Index of nodes in system
Query passed to find appropriate node
Remixable data sources and data transformation
Local search
Any of the “maps” applications
Kayak.com and other travel sites
Software above the level of a single device
Mobile search
Compensation for the commercialization of organic search
Paid ads do not have to map semantically to the results they accompany
Wales and Searchipedia
Program not tied to a revenue model

Now It is All About Meaning
As Moore’s Law brings about cheaper, faster, stronger hardware, the quest changes from indexing everything to the presentation of results
Search challenge to determine relevance without understanding meaning
Transition from strict computation to computational techniques to determine meaning
Hilltop Algorithm
Topic-sensitive PageRank

Hilltop Algorithm
Segmentation of corpus into broad topics
Subset that is then extrapolated to Web as a whole
Created by Jon Kleinberg at Cornell in late 1990s
Consultant to Google
Selection of authority sources within these topic areas
Authorities have lots of non-related pages on the same subject pointing to them
Quality of links more important than quantity of links
Determination of HUBS
Pages that point to many authority sources
Pre query calculations applied at query time
Likely part of Google’s Florida update in 2004

Topic-Sensitive PageRank
Consolidation of Hypertext Induced Topic Selection [HITS] and PageRank
Pre-query calculation of factors based on subset of corpus
Context of term use in document
Context of term use in history of queries
Context of term use by user submitting query
Creator now a Senior Engineer at Google

Search Further Down the Road
Semantic search technology patents
Search tool with preset categories and keywords
4-part database of information
Index, categories, keywords, document-specific data
Categories define topics through human-mediation
Keywords extracted from document text
User can iterate search results through related keywords presented from database
Search manager
Brokering application that facilitates selection of best search engine for the user’s query
Similarity estimation
Creates “sketch” or compact representation
Compares sketches based on determined similarity threshold
Deleted duplicate entries
Personalized search
Microsoft: Compares snippets of Web search engine results with data collected from user behavior and client
Demonstrated in NYT article March 7, 2007
Google: user bookmarks [online and client] used to construct “personalized search object” that is used to filter Web search result
Predictive search
Bayesian model
Compares user choices to predict more appropriate result from same vector space

SEO and IA: Choices
Capitulate
No action
Search technology continue on parallel path
Cooperate
Work with current search technology
Develop best practices that build on developments in search technology
Initiate
Influence development of search technology
Become a partner in developing user-centric search technology
Action Items
Influence the technology to work for not against user
Site Navigation Strategy
Site Organization Strategy
Link Strategy
Page Code Strategy
Content Strategy
Metadata Strategy

Initiate: Site Navigation Strategy
Locus of attention has changed from navigation to search
Hard-coded navigation structures are losing ground to pogo strikes
Navigation Blindness
Navigation Fatigue
Page Paradigm
Transitional Volatility
Users need inducement to move further into the site
Search technology rewards relationship navigation
Berrypicking Information Model
System approach to navigation development
Systems have specific behaviors and outcomes

Initiate: Site Organization Strategy
Distance makes a difference
Hierarchy reflects relevance
MOSS 2007 and use of structural factors
URL depth: the further from the homepage, the less important it must be
Click distance: the further from an authority page, the less important it must be
Architecture extends from the site to the page

Initiate: Linking Strategy
Links are human-mediated relationships
Blast services are no longer worthwhile
Related sites, niche directories, online bookmarking sites, provide starting points
Create link-based relationship model of relevance
Create or find authority
Hook up to HUBs
Think beyond the site

Cooperate: Page Code Strategy
Reveal the site to the search technology
Sitemap.xml
Provide on the page navigation
Don’t rely on dynamic navigation that spider cannot read
Craft structures that cue technology on importance
Illuminate the non-textual functionality
Optimize JScript and Flash

Initiate: Content Strategy
Dense, subject-specific content is what is indexed
People will scroll
If they don't scroll, they will print it out
Content to code ratio of 25%
Promote a keyword-to-content ratio 10–15%
Design on-the-page structure to move important information to the top
Design relational content models
Next steps as well as more information
Develop authority sections on site
Topic-based, not type-based

Cooperate: Metadata Strategy
Many forms of description
In the code
Page title [in the browser window]
Description
Keywords?
In the content
Display title
Content headings
Most effective if unique to the content on the page
Say goodbye to cut and paste
Description rivals structure for importance for user context
Ask.com thumbnails
Humans determine the “meaning” of the document and inform the machine

SEO and IA: Threats and Opportunities
Threats
Search technology advances without user representation
Search engines have become dominant navigation tool through information spaces
Bountiful
Relevant?
Traditional IA methodology increasingly less useful
Hierarchy: pages further from the home page deemed less important
Hard-coded navigation: not visible to search engines
Not Authority-based
Opportunities
Users seeking human-mediated guides to find information
Current search rewards a more flexible and intuitive IA
Replaced by a new structural paradigm based on relationship and context
Hub and authorities
Quality over quantity
Birds of a feather subject-wise

Marianne Sweeny [email_address]

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SEO and IA: The Beginning of a Beautiful Friendship

by Marianne Sweeny

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