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PREPARED FOR

2007 IA Summit: Las Vegas

SEO and IA: The Makings of a Beautiful Friendship

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omIntroduction

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

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omSearch 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

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omBlame 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

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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

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omNow 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

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omHilltop 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

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omTopic-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

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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

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omSEO 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

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omInitiate: 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

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omInitiate: 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

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omInitiate: 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

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omCooperate: 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

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omInitiate: 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

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omCooperate: 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

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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

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Marianne Sweenymarianne.sweeny@ascentium.com