I was invited to speak at OMCap Berlin 2014 about the close relationship between search engines and user experience with prescriptive guidance to gain higher rankings and more conversions.
What IA, UX and SEO Can Learn from Each OtherIan Lurie
Google has become the arbiter how users experience a website. Their data-driven determinants of what constitute good UX directly influence how a site is found. This is wrong because people, not machines, should determine experience; Google does not tell the SEO or UX community what data is used to measure experience and many elements of experience cannot be measured.This presentation reveals why Google uses UX signals to determine placement in search results and how to create a customer pleasing and highly visible user experience for your website.
Birds Bears and Bs:Optimal SEO for Today's Search EnginesMarianne Sweeny
In February of 2012, Google began launching the Panda Update (bears), the first of many steps away from a link-based model of relevance to a user experience model of relevance. This bearish focus on relevance use algorithms to determine a positive user experience focused on click-through (does the user select the result), bounce rate (does the user take action once they arrive at the landing page) and conversion (does the landing page satisfy the user’s information need). Content and information design became the foundation for relevance. Sadly, no one at Google told the content strategists, user experience professionals and information architects about their new influence on search engine performance. In April of 2012, Google followed up with the Penguin update (birds), a direct assault on link building, a mainstay of traditional search engine optimization (SEO). The Penguin algorithm evaluates the context and quality of links pointing to a site. Website found to be “over optimized” with low quality links are removed from Google’s index. Matt Cutts, GOogle Webmaster and the public face of Google, summed this up best: “And so that’s the sort of thing where we try to make the web site, uh Google Bot smarter, we try to make our relevance more adaptive so that people don’t do SEO, we handle that...” Sadly, Google is short on detail about how they are handling SEO, what constitutes adaptive relevance and how user experience professionals, information architects and content strategists can contribute thought-processing biped wisdom to computational algorithmic adaptive relevance so that searchers find what they are looking for even when they do not know that that is. This presentation will provide a brief introduction to the inner workings of information retrieval, the foundation of all search engines, even Google. On this foundation, I will dive deep into the Bs of how to optimize Web sites for today’s search technology: Be focused, Be authoritative, Be contextual and Be engaging. Birds (Penguin), Bears (Panda) & Bees: Optimal SEO will provide insight into recent search engine changes, proscriptive optimization guidance for usability and content strategy and foresight into the future direction of search.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
Information is overloaded in the Internet due to the unstable growth of information and it makes information search as complicate process. Recommendation System (RS) is the tool and largely used nowadays in many areas to generate interest items to users. With the development of e-commerce and information access, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Web recommendation systems assist the users to get the exact information and facilitate the information search easier. Web recommendation is one of the techniques of web personalization, which recommends web pages or items to the user based on the previous browsing history. But the tremendous growth in the amount of the available information and the number of visitors to web sites in recent years places some key challenges for recommender system. The recent recommender systems stuck with producing high quality recommendation with large information, resulting unwanted item instead of targeted item or product, and performing many recommendations per second for millions of user and items. To avoid these challenges a new recommender system technologies are needed that can quickly produce high quality recommendation, even for a very large scale problems. To address these issues we use two recommender system process using fuzzy clustering and collaborative filtering algorithms. Fuzzy clustering is used to predict the items or product that will be accessed in the future based on the previous action of user browsers behavior. Collaborative filtering recommendation process is used to produce the user expects result from the result of fuzzy clustering and collection of Web Database data items. Using this new recommendation system, it results the user expected product or item with minimum time. This system reduces the result of unrelated and unwanted item to user and provides the results with user interested domain.
Ranking in Google Since The Advent of The Knowledge GraphBill Slawski
A Two Person Panel Discussion/Presentation by Bill Slawski and Barbara Starr On June 23, 2015
The Lotico Semantic Web of San Diego
The SEO San Diego Meetup
The SEM San Diego Meetup
http://www.meetup.com/InternetMarketingSanDiego/events/222788495/
User experience drives search engines, and hence their results. Search Engine Result Presentation/Placements naturally follow that route.
This means that search results are no longer exclusively based on just ranking criteria. Amongst other critical factors is understanding the notion of 'ordering vs ranking', the impact of context and many others.
What IA, UX and SEO Can Learn from Each OtherIan Lurie
Google has become the arbiter how users experience a website. Their data-driven determinants of what constitute good UX directly influence how a site is found. This is wrong because people, not machines, should determine experience; Google does not tell the SEO or UX community what data is used to measure experience and many elements of experience cannot be measured.This presentation reveals why Google uses UX signals to determine placement in search results and how to create a customer pleasing and highly visible user experience for your website.
Birds Bears and Bs:Optimal SEO for Today's Search EnginesMarianne Sweeny
In February of 2012, Google began launching the Panda Update (bears), the first of many steps away from a link-based model of relevance to a user experience model of relevance. This bearish focus on relevance use algorithms to determine a positive user experience focused on click-through (does the user select the result), bounce rate (does the user take action once they arrive at the landing page) and conversion (does the landing page satisfy the user’s information need). Content and information design became the foundation for relevance. Sadly, no one at Google told the content strategists, user experience professionals and information architects about their new influence on search engine performance. In April of 2012, Google followed up with the Penguin update (birds), a direct assault on link building, a mainstay of traditional search engine optimization (SEO). The Penguin algorithm evaluates the context and quality of links pointing to a site. Website found to be “over optimized” with low quality links are removed from Google’s index. Matt Cutts, GOogle Webmaster and the public face of Google, summed this up best: “And so that’s the sort of thing where we try to make the web site, uh Google Bot smarter, we try to make our relevance more adaptive so that people don’t do SEO, we handle that...” Sadly, Google is short on detail about how they are handling SEO, what constitutes adaptive relevance and how user experience professionals, information architects and content strategists can contribute thought-processing biped wisdom to computational algorithmic adaptive relevance so that searchers find what they are looking for even when they do not know that that is. This presentation will provide a brief introduction to the inner workings of information retrieval, the foundation of all search engines, even Google. On this foundation, I will dive deep into the Bs of how to optimize Web sites for today’s search technology: Be focused, Be authoritative, Be contextual and Be engaging. Birds (Penguin), Bears (Panda) & Bees: Optimal SEO will provide insight into recent search engine changes, proscriptive optimization guidance for usability and content strategy and foresight into the future direction of search.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
Information is overloaded in the Internet due to the unstable growth of information and it makes information search as complicate process. Recommendation System (RS) is the tool and largely used nowadays in many areas to generate interest items to users. With the development of e-commerce and information access, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Web recommendation systems assist the users to get the exact information and facilitate the information search easier. Web recommendation is one of the techniques of web personalization, which recommends web pages or items to the user based on the previous browsing history. But the tremendous growth in the amount of the available information and the number of visitors to web sites in recent years places some key challenges for recommender system. The recent recommender systems stuck with producing high quality recommendation with large information, resulting unwanted item instead of targeted item or product, and performing many recommendations per second for millions of user and items. To avoid these challenges a new recommender system technologies are needed that can quickly produce high quality recommendation, even for a very large scale problems. To address these issues we use two recommender system process using fuzzy clustering and collaborative filtering algorithms. Fuzzy clustering is used to predict the items or product that will be accessed in the future based on the previous action of user browsers behavior. Collaborative filtering recommendation process is used to produce the user expects result from the result of fuzzy clustering and collection of Web Database data items. Using this new recommendation system, it results the user expected product or item with minimum time. This system reduces the result of unrelated and unwanted item to user and provides the results with user interested domain.
Ranking in Google Since The Advent of The Knowledge GraphBill Slawski
A Two Person Panel Discussion/Presentation by Bill Slawski and Barbara Starr On June 23, 2015
The Lotico Semantic Web of San Diego
The SEO San Diego Meetup
The SEM San Diego Meetup
http://www.meetup.com/InternetMarketingSanDiego/events/222788495/
User experience drives search engines, and hence their results. Search Engine Result Presentation/Placements naturally follow that route.
This means that search results are no longer exclusively based on just ranking criteria. Amongst other critical factors is understanding the notion of 'ordering vs ranking', the impact of context and many others.
Semantic search helps business people find answers to pressing questions by wading through oceans of information to find nuggets of meaningful information. In this presentation we’ll discuss how semantic search and content analysis technologies are starting to appear in the marketplace today. We’ll provide a recap of what semantic search is and what the key benefits are, then we’ll answer the following questions:
• Is semantic search a feature, an application, or enterprise system?
• How can I add semantic search to my existing work processes?
• Will I need to replace my existing content technologies?
• What will I need to do to prepare my content for semantic search?
• Is semantic search just for documents or can I search my data too?
• Can I use semantic search to find information on the internet and other public data sources?
• Are there standards to consider?
Extracting and Reducing the Semantic Information Content of Web Documents to ...ijsrd.com
Ranking and optimization of web service compositions represent challenging areas of research with significant implication for realization of the "Web of Services" vision. The semantic web, where the semantics information is indicated using machine-process able language such as the Web Ontology Language (OWL) "Semantic web service" use formal semantic description of web service functionality and enable automated reasoning over web service compositions. These semantic web services can then be automatically discovered, composed into more complex services, and executed. Automating web service composition through the use of semantic technologies calculating the semantic similarities between outputs and inputs of connected constituent services, and aggregate these values into a measure of semantics quality for the composition. It propose a novel and extensible model balancing the new dimensions of semantic quality ( as a functional quality metric) with QoS metric, and using them together as a ranking and optimization criteria. It also demonstrates the utility of Genetic Algorithms to allow optimization within the context of a large number of services foreseen by the "Web of Service" vision. To reduce the semantics of the web documents then to support semantic document retrieval by using Network Ontology Language (NOL) and to improve QoS as a ranking and optimization.
An introductory presentation about the current state of personalization in (Web) search for Bibliotekarforbundet's series of 'gå-hjem-møder'. Presented on May 17, 2016 at Aalborg University Copenhagen.
My presentation at the Semantic Technology and Business Conference in San Jose on August 19, 2014, with Barbara Starr (Her slides are separate, and cover a vast array of semantic tools and approaches for assessing and understanding your pages).
`A Survey on approaches of Web Mining in Varied Areasinventionjournals
There has been lot of research in recent years for efficient web searching. Several papers have proposed algorithm for user feedback sessions, to evaluate the performance of inferring user search goals. When the information is retrieved, user clicks on a particular URL. Based on the click rate, ranking will be done automatically, clustering the feedback sessions. Web search engines have made enormous contributions to the web and society. They make finding information on the web quick and easy. However, they are far from optimal. A major deficiency of generic search engines is that they follow the ‘‘one size fits all’’ model and are not adaptable to individual users.
Quest Trail: An Effective Approach for Construction of Personalized Search En...Editor IJCATR
Personalized search refers to search experiences that are tailored specifically to an individual's interests by incorporating information about the individual beyond specific query provided. Especially people working in a software development organization (analysts, developers, testers, maintenance team members), find it increasingly difficult to get relevant results to their searches. We propose methods to personalize searches by resolving the ambiguity of query terms, and increase the relevance of search results in order to match the user’s interests. Difficulty in web searches has given rise to the need for development of personalized search engines. Personalized search engines create user profiles to capture the users’ personal preferences and as such identify the actual goal of the input query. Since users are usually reluctant to explicitly provide their preferences due to the extra manual effort involved, the search engine faces the entire burden of predicting the user’s preferences and intentions behind a query in order to yield more relevant search results. In this paper we define a QUEST to be the objective of user’s search; here we combine quest level analysis of user’s search logs and semantic analysis of the user’s query in order to personalize user’s search results. Most personalization methods focus on the creation of one single profile for a user and apply the same profile to all of the user’s queries. Hence we propose a personalized search for a software development organization by creating QUEST or domain based profile rather than individual user based profile.
This is a high-level summary of three important ways to help people find information. The slides were presented at Vera Rhoades' information architecture class at the University of Maryland.
Structured data and metadata evaluation methodology for organizations looking...Emily Kolvitz
The current state of findability on the web for many organizations is incipient. Search Engine Optimization (SEO) techniques change frequently and remain much a mystery to many companies. The one variable in the equation of web findability that remains a staple is good quality metadata under the hood of the website.
This research methodology will allow for :
An assessment of findability maturity on the web from an image-centric viewpoint
Help improve findability on the web by establishing a baseline for where your organization is at in terms of structured data content and visualize gaps or areas for improvement from a search engine neutral perspective
If you think you need a search application, there are some useful first steps to take:
* validating that full-text search is the right technology
* producing sets of ideal results you'd like to return for a range of queries
* considering the value of supplementing a basic search result list with document clustering
* producing more specific requirements and investigating technology options
Search on the Web is a daily activity for many people throughout the world
Search and communication are most popular uses of the computer
Applications involving search are everywhere
The field of computer science that is most involved with R&D for search is information retrieval (IR)
Search Solutions 2011: Successful Enterprise Search By DesignMarianne Sweeny
When your colleagues say they want Google, they don’t mean the Google Search Appliance. They mean the Google Search user experience: pervasive, expedient and delivering the information that they need. Successful enterprise search does not start with the application features, is not part of the information architecture, does not come from a controlled vocabulary and does not emerge on its own from the developers. It requires enterprise-specific data mining, enterprise-specific user-centered design and fine tuning to turn “search sucks” into search success within the firewall. This presentation looks at action items, tools and deliverables for Discovery, Planning, Design and Post Launch phases of an enterprise search deployment.
Team of Rivals: UX, SEO, Content & Dev UXDC 2015Marianne Sweeny
The search engine landscape has changed dramatically and now relies heavily on user experience signals to influence rank in search results. In this presentation, I explore search engine methods for evaluating UX in a machine readable fashion and present a framework for successful cross-discipline collaboration.
Semantic search helps business people find answers to pressing questions by wading through oceans of information to find nuggets of meaningful information. In this presentation we’ll discuss how semantic search and content analysis technologies are starting to appear in the marketplace today. We’ll provide a recap of what semantic search is and what the key benefits are, then we’ll answer the following questions:
• Is semantic search a feature, an application, or enterprise system?
• How can I add semantic search to my existing work processes?
• Will I need to replace my existing content technologies?
• What will I need to do to prepare my content for semantic search?
• Is semantic search just for documents or can I search my data too?
• Can I use semantic search to find information on the internet and other public data sources?
• Are there standards to consider?
Extracting and Reducing the Semantic Information Content of Web Documents to ...ijsrd.com
Ranking and optimization of web service compositions represent challenging areas of research with significant implication for realization of the "Web of Services" vision. The semantic web, where the semantics information is indicated using machine-process able language such as the Web Ontology Language (OWL) "Semantic web service" use formal semantic description of web service functionality and enable automated reasoning over web service compositions. These semantic web services can then be automatically discovered, composed into more complex services, and executed. Automating web service composition through the use of semantic technologies calculating the semantic similarities between outputs and inputs of connected constituent services, and aggregate these values into a measure of semantics quality for the composition. It propose a novel and extensible model balancing the new dimensions of semantic quality ( as a functional quality metric) with QoS metric, and using them together as a ranking and optimization criteria. It also demonstrates the utility of Genetic Algorithms to allow optimization within the context of a large number of services foreseen by the "Web of Service" vision. To reduce the semantics of the web documents then to support semantic document retrieval by using Network Ontology Language (NOL) and to improve QoS as a ranking and optimization.
An introductory presentation about the current state of personalization in (Web) search for Bibliotekarforbundet's series of 'gå-hjem-møder'. Presented on May 17, 2016 at Aalborg University Copenhagen.
My presentation at the Semantic Technology and Business Conference in San Jose on August 19, 2014, with Barbara Starr (Her slides are separate, and cover a vast array of semantic tools and approaches for assessing and understanding your pages).
`A Survey on approaches of Web Mining in Varied Areasinventionjournals
There has been lot of research in recent years for efficient web searching. Several papers have proposed algorithm for user feedback sessions, to evaluate the performance of inferring user search goals. When the information is retrieved, user clicks on a particular URL. Based on the click rate, ranking will be done automatically, clustering the feedback sessions. Web search engines have made enormous contributions to the web and society. They make finding information on the web quick and easy. However, they are far from optimal. A major deficiency of generic search engines is that they follow the ‘‘one size fits all’’ model and are not adaptable to individual users.
Quest Trail: An Effective Approach for Construction of Personalized Search En...Editor IJCATR
Personalized search refers to search experiences that are tailored specifically to an individual's interests by incorporating information about the individual beyond specific query provided. Especially people working in a software development organization (analysts, developers, testers, maintenance team members), find it increasingly difficult to get relevant results to their searches. We propose methods to personalize searches by resolving the ambiguity of query terms, and increase the relevance of search results in order to match the user’s interests. Difficulty in web searches has given rise to the need for development of personalized search engines. Personalized search engines create user profiles to capture the users’ personal preferences and as such identify the actual goal of the input query. Since users are usually reluctant to explicitly provide their preferences due to the extra manual effort involved, the search engine faces the entire burden of predicting the user’s preferences and intentions behind a query in order to yield more relevant search results. In this paper we define a QUEST to be the objective of user’s search; here we combine quest level analysis of user’s search logs and semantic analysis of the user’s query in order to personalize user’s search results. Most personalization methods focus on the creation of one single profile for a user and apply the same profile to all of the user’s queries. Hence we propose a personalized search for a software development organization by creating QUEST or domain based profile rather than individual user based profile.
This is a high-level summary of three important ways to help people find information. The slides were presented at Vera Rhoades' information architecture class at the University of Maryland.
Structured data and metadata evaluation methodology for organizations looking...Emily Kolvitz
The current state of findability on the web for many organizations is incipient. Search Engine Optimization (SEO) techniques change frequently and remain much a mystery to many companies. The one variable in the equation of web findability that remains a staple is good quality metadata under the hood of the website.
This research methodology will allow for :
An assessment of findability maturity on the web from an image-centric viewpoint
Help improve findability on the web by establishing a baseline for where your organization is at in terms of structured data content and visualize gaps or areas for improvement from a search engine neutral perspective
If you think you need a search application, there are some useful first steps to take:
* validating that full-text search is the right technology
* producing sets of ideal results you'd like to return for a range of queries
* considering the value of supplementing a basic search result list with document clustering
* producing more specific requirements and investigating technology options
Search on the Web is a daily activity for many people throughout the world
Search and communication are most popular uses of the computer
Applications involving search are everywhere
The field of computer science that is most involved with R&D for search is information retrieval (IR)
Search Solutions 2011: Successful Enterprise Search By DesignMarianne Sweeny
When your colleagues say they want Google, they don’t mean the Google Search Appliance. They mean the Google Search user experience: pervasive, expedient and delivering the information that they need. Successful enterprise search does not start with the application features, is not part of the information architecture, does not come from a controlled vocabulary and does not emerge on its own from the developers. It requires enterprise-specific data mining, enterprise-specific user-centered design and fine tuning to turn “search sucks” into search success within the firewall. This presentation looks at action items, tools and deliverables for Discovery, Planning, Design and Post Launch phases of an enterprise search deployment.
Team of Rivals: UX, SEO, Content & Dev UXDC 2015Marianne Sweeny
The search engine landscape has changed dramatically and now relies heavily on user experience signals to influence rank in search results. In this presentation, I explore search engine methods for evaluating UX in a machine readable fashion and present a framework for successful cross-discipline collaboration.
Delivered at Enterprise Search and Discovery 2015, this presentation takes a look at the search landscape users enjoy outside the firewall and the expectations it fosters inside. It presents contemporary user research on enterprise search behavior and uses these findings to make recommendations to enhance enterprise search effectiveness.
Research on Document Indexing in the Search Engines. The main theme of Informational retrieval is to send the exact response of a user for specific Query.
The information search retrieval is a very big process, to achieve this concept we need to develop an application with more effect and we have to use techniques like Document indexing, page ranking, clustering technique. Among all of these Document index is plays avital role while searching why since instead of searching hundreds of thousands of documents it will directly go to the particular index and will give the output here. Here our achievement mainly is indexing, the clear meaning of the indexing is storing an index is to optimize speed and performance in finding the appropriate/corresponding document for the user searched query.
My conclusion is the context based index approach is used in the query retrieval, this is mainly from the source document. Instead of searching every page on server, finding technically is better. Due to this we can save our time, we can reduce the burden of server.
Research Report on Document Indexing-Nithish KumarNithish Kumar
Research on Document Indexing in the Search Engines. The main theme of Informational retrieval is to send the exact response of a user for specific Query.
The information search retrieval is a very big process, to achieve this concept we need to develop an application with more effect and we have to use techniques like Document indexing, page ranking, clustering technique. Among all of these Document index is plays avital role while searching why since instead of searching hundreds of thousands of documents it will directly go to the particular index and will give the output here. Here our achievement mainly is indexing, the clear meaning of the indexing is storing an index is to optimize speed and performance in finding the appropriate/corresponding document for the user searched query.
My conclusion is the context based index approach is used in the query retrieval, this is mainly from the source document. Instead of searching every page on server, finding technically is better. Due to this we can save our time, we can reduce the burden of server.
While we have been busy trying to "define the damn thing" IA or answering the age old question of who rules, UX, IxDA or IA, the search engines have been busily transitioning to a machine mediated experience model for ranking. This means that SEO is now the responsibility of UX/IA whether we like it or not. This presentation lays out how search engines evaluate user experience and how we can influence this evaluation with an optimized design.
Cross discipline collaboration benefits from group think, a consolidation of soft system methodology and user focused design that all starts with design thinking that sees clients, designers, developers and information architects working together to address user problems and needs. As with any great adventure, design thinking starts with exploration and discovery.This presentation examines the high level tenants of system thinking, expands the scope of user thinking to include tools and devices that users employ to find out designs and delve into the specifics of design thinking, its methods and outcomes.
Mike King examines the state of the SEO industry and talks through knowing information retrieval will help improve our understanding of Google. This talk debuted at MozCon
This is a presentation that I did for the Enterprise Search Summit West 2008 that has been amended for a Web Project Management class at the University of Washington
What is the current status quo of the Semantic Web as first mentioned by Tim Berners Lee in 2001?
Not only 10 blue links can drive you traffic anymore, Google has added many so called Knowlegde cards and panels to answer the specific informational need of their users. Sounds complicated, but it isn’t. If you ask for information, Google will try to answer it within the result pages.
I'll share my research from a theoretical point of view through exploring patents and papers, and actual testing cases in the live indices of Google. Getting your site listed as the source of an Answer Card can result in an increase of CTR as much as 16%. How to get listed? Come join my session and I'll shine some light on the factors that come into play when optimizing for Google's Knowledge graph.
Personalized Search at Sandia National LabsLucidworks
Clay Pryor, R&D S&E, Computer Science & Ryan Cooper, Sandia National Labs. Presentation from ACTIVATE 2019, the Search and AI Conference hosted by Lucidworks. http://www.activate-conf.com
Search Me: Designing Information Retrieval ExperiencesJoe Lamantia
This case study reviews the methods and insights that emerged from an 18-month effort to coordinate and enhance the scattered user experiences of a suite of information retrieval tools sold as services by an investment ratings agency. The session will share a method for understanding user needs in diverse information access contexts; review a collection of information retrieval patterns such as enterprise search and information access, service design, and product and platform management; and consider the impact of organizational and cultural factors on design decisions.
Bearish SEO: Defining the User Experience for Google’s Panda Search LandscapeMarianne Sweeny
The search sun shifted in March 2011 when Google started rolling out the beginning of the Panda update. Instead of using the famous PageRank, a link-based relevance calculation, Panda rests on a machine interpretation of user experience to decide which sites are most relevant to a searchers quest for knowledge. This means that IA and UX practitioners need to start thinking about the machine implications of the way they structure information on the web, and think ahead about the human implications for how search engines present their sites in response to searcher queries. Bearish SEO will present real, actionable methods for content providers, information architects and user experience designers to directly influence search engine discoverability. Need is an experience. It is a state of being. The goal for this presentation is to ensure that user experience professionals become an integral part of designing search experience.
Connection and Context: ROI of AI for Digital MarketingMarianne Sweeny
This presentation explores the intersection of emerging AI technology with SEO, UX, content strategy and digital marketing with prescriptive guidance on how to influence machine learning for the right outcomes.
This presentation looks at new methodologies of keyword research to meet the linguistic and semantic sophistication that is Web search today. Search engines are changing and SEO must change with them to meet the challenge of getting the right visitors to the site.
Finding, or not finding, information is consistently the most called out issue in the enterprise. Technology companies spend millions developing features that remain idle because, while everyone is concerned about optimizing enterprise search, no one is doing anything about it. The PM cuts the budget because "the devs will do it." The IA/UX architects do not have the specific expertise. The developers want to do it but do not have appropriate guidance.
This is a call-to-action for developers and ITpros to make sure that they get what they need to make search in the enterprise work. Because, after the interactive marketing agency has left the building, they are the ones that will be hearing "search sucks" directed at them.
At the 2011 Polish IA Summit, I examine big changes in optimizing for search engines.
We now know that Google is not infallible (seems that companies are easily able to game the PR system) or t all knowing (seems it takes a competitor with a friend at the New York Times to reveal said PR gaming). We also found out that Google can be capricious with blanket suppression of content from certain sites regardless of whether users find it relevant.
This presentation looks at search optimization tools ant tactics that work regardless of these changes and how to keep the site optimized.
Search engines have changed a lot over the last 15 years and optimizing Websites for them must keep up. This presentation looks at the search landscape and present strategies and tactics for optimizing for today's search.
Uw Digital Communications Social Media Is Not SearchMarianne Sweeny
I had the pleasure of speaking to one of the Digital Communication classes at the University of Washington on my favorite topic, why social media will never replace search as an information finding medium. Those students were wicked smart and I walked away learning a lot myself.
Enterprise Search Share Point2009 Best Practices FinalMarianne Sweeny
This presentation examines features and benefits in Microsoft Office SharePoint Server (MOSS) 2007 enteprise search. It contains configuration guidance, code snippets, tips and tricks.
SEO and IA: The Beginning of a Beautiful FriendshipMarianne Sweeny
Search technology and IA have developed on parallel tracks over the last many years. I propose that they join forces in creating an enhanced user information finding experience and present specific opportunities for deeper IA engagement.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
Understanding User Behavior with Google Analytics.pdfSEO Article Boost
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
Tracking User Engagement:
Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
Conversion Rate Optimization:
Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
Italy Agriculture Equipment Market Outlook to 2027harveenkaur52
Agriculture and Animal Care
Ken Research has an expertise in Agriculture and Animal Care sector and offer vast collection of information related to all major aspects such as Agriculture equipment, Crop Protection, Seed, Agriculture Chemical, Fertilizers, Protected Cultivators, Palm Oil, Hybrid Seed, Animal Feed additives and many more.
Our continuous study and findings in agriculture sector provide better insights to companies dealing with related product and services, government and agriculture associations, researchers and students to well understand the present and expected scenario.
Our Animal care category provides solutions on Animal Healthcare and related products and services, including, animal feed additives, vaccination
Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
5. Throughout time, we have codified our existence and stored information using text. Humans are text-based
info-vores and recent studies from Google show a strong user preference for text over imagery.
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6. Our first “search engines” were librarians, people just like us who were trained in how to organize, store and
retrieve needed information. They did not rely on cookies to extract personal information from which they
would “predict” what we wanted. They di d not need to because they could ask in way that we understood and
conclude what we wanted based on our answers.
Nice librarians gave us cookies of the other kind but we had to eat them outside.
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A spider returns information about each word on each page it crawls.
This information is stored in the index where it is compressed based on grammatical requirements such as
stemming[taking the word down to its most basic root] and stop words [common articles and others stipulated by
the company]. A complete copy of the Web page may be stored in the search engine’s cache.
This index is then inverted so that lookup is done on the basis of record contents and not the document ID.
With brute force calculation, the system pulls each record from the inverted index [mappingof words to where they
appear in document text]. This is recall or all documents in the corpus with text instances that match your the
term(s).
The “secret sauces” for each search engine are algorithms that sort order the recall results in a meaningfulfashion.
This is precision or the number of documents from recall that are relevant to your query term(s).
All search engines use a common set of values to refine precision. If the search term used in the title of the
document, in heading text, formattedin any way, or used in link text, the document is considered to be more
relevant to the query. If the query term(s) are used frequently throughout the document, the document is considered
to be more relevant.
An example the complexity involved in refinement of results is Term Frequency - Inverse Document Frequency [TF-
IDF] weighting. Here the raw term frequency (TF) of a term in a document by the term's inverse document
frequency (IDF) weight [frequency of occurrence in a particular document multipliedthe number of documents
containingthe term divided by the number of documents in the entire corpus. [caveat emptor: high-level, low-level,
level-playing-fieldmath are not my strong suits].
8. Implicit Collection Tools
Softwareagents
Logins
Enhanced proxy servers
Cookies
Session IDs
Gathered without user awareness from behavior to:
Query context inferred
Profile inferred
Less accurate
Requires a lot of data
Maximum precision: 58%
Advantages: more data, better data (easier for system to consume and rationalize)
Disadvantage: user has no control over what is collected
Explicit Collection Tools
HTML forms
Explicit user feedback interaction (early Google personalization with More Like This)
Provided by user with knowledge
More accurate as user shares more about query intent and interests
Maximum precision: 63%
Advantage: User has more control over personal and private information
Disadvantage: compliance, users have a hard time expressing interests, burdensome on user to fill out forms, false
info from user
Resource: Jaime Teevan MS Research
(http://courses.ischool.berkeley.edu/i141/f07/lectures/teevan_personalization.pdf)
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9. In 2002, Google acquired personalization technology Kaltix and founder Sep Kamver who has been head of Google
personalizationsince. Defines personalization: “product that can use information given by the user to provide tailored, more
individualized experience”
Query Refinement
System adds terms based on past information searches
Computessimilarity between query and user model
Synonym replacement
Dynamic query suggestions - displayed as searcher enters query
Results Re-ranking
Sorted by user model
Sorted by Seen/Not Seen
Personalizationof results set
Calculationof information from 3 sources
User: previous search patterns
Domain: countries, cultures, personalities
GeoPersonalization:location-based results
Metrics used for probability modeling on future searches
Active: user actions in time
Passive: user toolbar information (bookmarks), desktop information (files), IP location, cookies
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11. In January 2002, 52% of all Americans used search engines. In February 2012 that figure grew to 73% of all
Americans.
On any given day in early 2012, more than half of adults using the internet use a search engine (59%). That is double
the 30% of internet users who were using search engines on a typical day in 2004.
Moreover, users report generally good outcomes and relatively high confidence in the capabilities of search engines:
• 91% of search engine users say they always or most of the time find the information they are seeking
when they use search engines
• 73% of search engine users say that most or all the informationthey find as they use search engines is
accurate and trustworthy
• 66% of search engine users say search engines are a fair and unbiased source of information
• 55% of search engine users say that, in their experience, the quality of search results is getting better
over time, while just 4% say it has gotten worse
• 52% of search engine users say search engine results have gotten more relevant and useful over time,
while just 7% report that results have gotten less relevant.
Resource: Pew Internet Trust Study of Search engine behavior
http://www.pewinternet.org/Reports/2012/Search-Engine-Use-2012/Summary-of-findings.aspx
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12. Resource: Pew Internet Trust Study of Search engine behavior
http://www.pewinternet.org/Reports/2012/Search-Engine-Use-2012/Summary-of-findings.aspx
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13. How to search:
56% constructed poor queries
55% selected irrelevant results 1 or more times
Get Lost in data:
33% had difficulty navigating/orientingsearch results
28% had difficulty maintainingorientation on a website
Discernment
36% did not go beyond the first 3 search results
91% did not go beyond the first page of search results
Resource: Using the Internet: Skill Related Problems in User Online Behavior; van Deursen & van Dijk; 2009
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16. Based on academic citation model
1998 named one of the top 100 Websites by PC Magazine “uncanny knack for returning extremely relevant results”
Ranking based on number of links to the page
Random Surfer (spider follows “randomly selected links) examines all of the links and follows one to destination,
does that at destination
Random Surfer authority score: % of time random surfer would spend visiting the page (added to the hyperlink
score)
Restart probability = 15%, surfer does not select a link and instead “jumps” to another page
First introduction of “loose authority” determined by adding up the “authority” scores of the pages linking in
Discountedpages linking to each other (black hat link ring)
Complications:
Assumes link vote of authority, does not consider commercial value of links
Ability to link limited to subset of users
Orphan pages
Users no longer “surf” randomly
Does not scale
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17. Quality of links more important than quantity of links
Segmentationof corpus into broad topics
Selectionof authority sources within these topic areas
Hilltopwas one of the first to introduce the concept of machine-mediated“authority” to combat the human
manipulationof results for commercial gain (using link blast services, viral distribution of misleading links. It is used
by all of the search engines in some way, shape or form.
Hilltopis:
Performed on a small subset of the corpus that best represents nature of the whole
Authorities: have lots of unaffiliatedexpert document on the same subject pointing to them
Pages are ranked according to the number of non-affiliated“experts” point to it – i.e. not in the same site or
directory
Affiliationis transitive [if A=B and B=C then A=C]
The beauty of Hilltop is that unlike PageRank, it is query-specific and reinforces the relationship between the
authority and the user’s query. You don’t have to be big or have a thousand links from auto parts sites to be an
“authority.” Google’s 2003 Florida update, rumored to contain Hilltop reasoning, resulted in a lot of sites with
extraneous links fall from their previously lofty placements as a result.
Photo: Hilltop Hohenzollern Castle in Stuttgart
18. Consolidationof 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 submittingquery
ComputesPR based on a set of representationaltopics [augmentsPR with content analysis]
Topic derived from the Open Source directory
Uses a set of ranking vectors: Pre-query selection of topics + at-query comparison of the similarity of query to topics
Creator now a Senior Engineer at Google
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19. Indexing infrastructure
Made it easier for engineers to “add signals” that impact ranking
Pre announced and open to public testing
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22. SEO always reverse engineering the algorithms
SE Update – tactic, tactic, tactic
SE Update – tactic, tactic, tactic
SE Update – tactic, tactic, tactic
UX
Drawing on white boards while singing Kumbaya
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23. Vince update 2009
http://searchenginewatch.com/article/2288128/Vince-The-Google-Update-We-Should-Be-Talking-About
Big brands can afford better sites
Big brands spend more $$ in adwords
“The internet is fast becoming a "cesspool" where false information thrives, Google CEO Eric Schmidt said
yesterday. Speaking with an audience of magazine executives visiting the Google campus here as part of
their annual industry conference, he said their brands were increasingly important signals that content can be
trusted. …Brands are the solution, not the problem," Mr. Schmidt said. "Brands are how you sort out the
cesspool….Brand affinity is clearly hard wired," he said. "It is so fundamentalto human existence that it's not
going away. It must have a genetic component.” Eric Schmidt, Google, October 2008
http://www.seobook.com/google-branding
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24. About content: quality and freshness
About agile: frequent iterations and small fixes
About UX: or so it seems (Vanessa Fox/Eric Enge: Cllick-through, Bounce Rate, Conversion)
Panda 1.0: Google’s first salvo against “spam” (shallow, thin content sites) in the form of content
duplicationand low value original content (i.e. “quick, give me 200 words on Brittany Spear’s vacation in
the Maldives”) – biggest target was content farms – Biggest Impact: keyword optimizationand link building.
Panda 2.1: Having unique content not enough – quality factors introduced (some below)
Trustworthiness: with my credit card information
Uniqueness: is this saying what I’ve found somewhere else
Origination: does the person writing the content have “street cred,” do I believe that this is
an authoritativeresource on this topic
Display: does the site look professional, polished
Professional: is the content well constructed, well edited and without grammatical or
spelling errors
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25. And sort of blames SEO for it (not outright but in a passive/aggressive) kind of way
2007 Google Patent: Methods and Systems for IdentifyingManipulatedArticles (November 2007)
Manipulation:
Keyword stuffing(article text or metadata)
Unrelatedlinks
Unrelatedredirects
Auto-generatedin-links
Guestbookpages (blog post comments)
Followedup: Google Patent: Content Entity Management (May 2012)
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26. Entity=anythingthat can be tagged as being associated with certain documents, e.g. Store, news source, product
models, authors, artists, people, places thing
The entity processing unit looks at “candidate strings and compares to query log to extract: most clicked entity,
most time spent by user)
Query logs (this is why they took away KW data – do not want us to reverse engineer as we have in past)
User Behavior information: user profile, access to documents seen as related to original document, amount of time
on domain associated with one or more entities, whole or partial conversions that took place
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35. Selection: Do they pick you from the results
Engagement: Do they do anything once they get to your page that would indicate it is relevant to their query
(informationneed)
Content: Is the content of high quality
Links: Baked in legacy relevance: Are they contextually relevant? From Authority Resources? Earned, not purchased.
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37. KWICinfluences selection
Placement influences selection
Recent changes make larger, reduce characters
Matt Cutts on the importance of well crafted <title> and description http://www.youtube.com/watch?v=THYguer_JrM
“Think about maximizing your click through – compelling, something that invites clicks, then think about conversion
rates…Title and description can absolutely maximize click through rate…What matters is how much you get clicked
on and how often you take those clicked on visits and convert those to whatever you really want.”
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38. Littleinfluence on relevance ranking
Demonstratedinfluence on selection
Informationscent to take them to the page
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39. Legacy newspaper structure of “the fold.”
Proto-typicality: user mental models
Visual complexity: ratio of images to text favors text
10/11/2014
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41. VISUAL COMPLEXITY& PROTOTYPICALITY
The results show that both visual complexity and proto-typicality play crucial roles in the process of forming an
aestheticjudgment. It happens within incredibly short timeframes between 17 and 50 milliseconds. By
Comparison, the average blink of an eye takes 100 to 400 milliseconds.
In other words, users strongly prefer website designs that look both simple (low complexity)
and familiar (high prototypicality). That means if you’re designing a website, you’ll want to consider both factors.
Designs that contradict what users typically expect of a website may hurt users’ first impression and damage
their expectations.
August 2012
Resource: http://googleresearch.blogspot.com/2012/08/users-love-simple-and-familiar-designs.html
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42. Flat structure that allows for proximity relevance and cross-walk to other directories
Topicality hubs: Sections of the site that focus on high-level entity (topic, subject) with increasing granularity
Click Distance: the further from an authority page, the less important it must be
URL Depth: the further from the homepage, the less important it must be
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43. Put the sidewalks where the footprints are
Resource: Stuart Brand: How Buildings Learn
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44. This is an actual notificationfrom a real Google Webmaster Account. The algorithms have determined that the
content quality on this site is low. You do not want to get one of these because by the time you get it, you’ve already
dropped a few PAGES in search results.
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46. This client invests a lot of time and effort in their News & Events directory
Customers are viewing the utility pages (Contact, etc) and the product justification/ROIsection.
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51. “As we’ve mentioned previously, we’ve heard complaints from users that if they click on a result and it’s
difficult to find the actual content, they aren’t happy with the experience. Rather than scrolling down the
page past a slew of ads, users want to see content right away. So sites that don’t have much content
“above-the-fold” can be affected by this change.”
http://googlewebmastercentral.blogspot.com/2012/01/page-layout-algorithm-improvement.html
If you’ll recall, this is the Google update that specifically looks at how much content a page has “above
the fold”. The idea is that you don’t want your site’s content to be pushed down or dwarfed by ads and
other non-content material….“Rather than scrolling down the page past a slew of ads, users want to see
content right away. So sites that don’t have much content “above-the-fold” can be affected by this
change. If you click on a website and the part of the website you see first either doesn’t have a lot of
visible content above-the-fold or dedicates a large fraction of the site’s initial screen real estate to ads,
that’s not a very good user experience. Such sites may not rank as highly going forward.”
http://www.webpronews.com/google-updated-the-page-layout-algorithm-last-week-2014-02
Resources
http://searchenginewatch.com/article/2328573/Google-Refreshes-Page-Layout-Algorithm
http://www.seobythesea.com/2011/12/10-most-important-seo-patents-part-3-classifying-web-blocks-with-
linguistic-features/
http://www.seobythesea.com/2008/03/the-importance-of-page-layout-in-seo/
http://searchenginewatch.com/article/2140407/Googles-New-Page-Layout-Update-Targets-Sites-With-
Too-Many-Ads
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52. Each page has an H1 heading (that is not an image unless with text overlay)
Each page has a lead off (introduction) paragraph that call out the story focus
Rest of content follows. Longer content uses headings to break up text (for scanning)
and sub-topic focus areas
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57. Mom and creampuffs
The search engines think that we’re superfluous because we don’t “get search” That’s what I’m here to end. I
want you to “get search.” We are information professionals, not mice! We’re going to use every neuron,
synapsis and gray cell to fight back.
We will shift from trying to optimize search engine behavior to optimizing what the search engines consume,
move from search engine optimizationto information optimization
We will Focus
We will be Collaborative
We will get Connected
We will stay Current
Because we are user experience professionals, not Matt Cutts, Sergey Brin or Larry Page.
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