Search Should Work Like Magic

Thanks to Google, intranet users expect to be able to type in a word (or two) and find the page they are looking for, preferably in the first few results. This is not an unreasonable expectation. At the most fundamental level, search on an intranet is supposed to make it quick and easy for staff to find things, thereby saving them time and improving their productivity. This can be distilled down to a very simple concept: search should work like magic. As much as is possible, search should always give staff the information they need, somewhere in the first few results.

Robertson, James. Step Two (2006). Articles>Web Design>Intranets>Search

What to Include in Intranet Search Results

Intranet search often fails to meet the needs or expectations of users, with confusing and complex results provided for even the simplest searches.

Robertson, James. Step Two (2005). Articles>Web Design>Intranets>Search

Where's the Search? Re-Examining User Expectations of Web Objects

In 2001, Bernard determined that users were able to form a schema for the location of web objects on informational websites. The current study investigates whether users' expectations have changed since the 2001 study. Changes were found in the expected location of the site search engine, internal links, and advertisements.

Shaikh, A. Dawn and Kelsi Lenz. Usability News (2006). Articles>Web Design>Search>User Centered Design

Optimize Your AdWords Campaigns

Both AdWords and YSM are much more complicated beasts than the old banner networks ever were, and coming to grips with them can be a bit of a headache.

Oxer, Jonathan. Internet Vision Technologies (2008). Articles>Web Design>Marketing>Search Engine Optimization

Sphere: Balancing Power and Simplicity

The Sphere team had already put a lot of work into returning fresh, relevant search results, and had several ideas about how to evolve the standard search experience. Filtering results appropriately (to let users easily get at the exact result they were after) would be paramount. Deep context for results would also be offered, along with related items (from traditional media to podcasts).

Freitas, Ryan. Adaptive Path (2006). Articles>Web Design>Search

Semantics Continues to Not be RDF, But Enrichment, Classification and Taxonomy

Within the realm of computational semantics, there is still a fairly broad disconnect between triple pair semantics, the use of RDF (or turtle notation) to create atomic assertions, and the realm of semantics as reflected on the web. I do not expect this to change much in 2009, save perhaps that the gulf between the two will likely just get wider.

Cagle, Kurt. O'Reilly and Associates (2009). Articles>Web Design>Information Design>Search

Right to Reply: SEO's Glory Days Are Not Over

In a recent article on Netimperative, Mike Grehan examined if the traditional role of SEO was becoming outdated, given the rise of social media. In this article, Eliza Dashwood, Director of Sales and Marketing, Ambergreen Internet Marketing offers a counter-point to Mike’s argument.

Dashwood, Eliza. NetImperative (2009). Articles>Web Design>Search Engine Optimization>Search

Common Sense SEO (Search Engine Optimization) Checklist

I don’t “really” know anything about SEO. What I do know is the folks at Google and other big search engines are just human beings like us who have created and constantly tweak the search algorithms. Their goal is to give us what we want when searching, the best possible websites relevant to what we are searching for. So let’s set aside all the fancy technical stuff and just use some good ol’ common sense.

Coyier, Chris. CSS Tricks (2009). Articles>Web Design>Search Engine Optimization

Google's Search Engine Optimization Starter Guide  

Welcome to Google's Search Engine Optimization Starter Guide. This document first began as an effort to help teams within Google, but we thought it'd be just as useful to webmasters that are new to the topic of search engine optimization and wish to improve their sites' interaction with both users and search engines. Although this guide won't tell you any secrets that'll automatically rank your site first for queries in Google (sorry!), following the best practices outlined below will make it easier for search engines to both crawl and index your content.

Google (2008). Articles>Web Design>Search Engine Optimization

Search Words Versus Carewords

The words we use when we search are not always the words we like to read when we arrive at a website. Over the years, I have discovered that the way we think and the words we use when we search give strong clues as to what we want, but only clues. The words that will help us complete the task we came to the website to complete can be subtly-and sometimes substantially different-to the words we used when searching for it.

McGovern, Gerry. CMSwire (2009). Articles>Web Design>Search>User Centered Design

Choosing the Right Search Results Page Layout: Make the Most of Your Width

Page layout forms the foundation in presenting search results. Your layout decisions for search results pages will have tremendous impact on the user experience for your entire site. Choosing the right width for search results is important, and the optimal width for search results may be a great deal narrower than some people using big monitors would believe.

Nudelman, Greg. UXmatters (2009). Articles>Web Design>Search

Starting from Zero: Winning Strategies for No Search Results Pages

Search results pages are some of the most visited pages on typical e-commerce sites—to say nothing of a search engine like Google. Many articles appear each year about optimal search algorithms, database performance, and the like. In contrast, very few publications focus on improving the search experience from the customer’s perspective.

Nudelman, Greg. UXmatters (2009). Articles>Web Design>Search>Usability

Investigating Behavioral Variability in Web Search  

Understanding the extent to which people’s search behaviors differ in terms of the interaction flow and information targeted is important in designing interfaces to help World Wide Web users search more effectively. In this paper we describe a longitudinal log-based study that investigated variability in people’s interaction behavior when engaged in search-related activities on the Web. We analyze the search interactions of more than two thousand volunteer users over a five-month period, with the aim of characterizing differences in their interaction styles. The findings of our study suggest that there are dramatic differences in variability in key aspects of the interaction within and between users, and within and between the search queries they submit. Our findings also suggest two classes of extreme user--navigators and explorers--whose search interaction is highly consistent or highly variable. Lessons learned from these users can inform the design of tools to support effective Web-search interactions for everyone.

White, Ryen W. and Steven M. Drucker. WWW 2007 (2007). Articles>Web Design>Search>User Centered Design

Why We Search: Visualizing and Predicting User Behavior  

The aggregation and comparison of behavioral patterns on the WWW represent a tremendous opportunity for understanding past behaviors and predicting future behaviors. In this paper, we take a first step at achieving this goal. We present a large scale study correlating the behaviors of Internet users on multiple systems ranging in size from 27 million queries to 14 million blog posts to 20,000 news articles. We formalize a model for events in these time-varying datasets and study their correlation. We have created an interface for analyzing the datasets, which includes a novel visual artifact, the DTWRadar, for summarizing differences between time series. Using our tool we identify a number of behavioral properties that allow us to understand the predictive power of patterns of use.

Adar, Eytan, Daniel S. Weld, Brian N. Bershad and Steven D. Gribble. WWW 2007 (2007). Articles>Web Design>Search>Research

Web Object Retrieval  

The primary function of current Web search engines is essentially relevance ranking at the document level. However, myriad structured information about real-world objects is embedded in static Web pages and online Web databases. Document-level information retrieval can unfortunately lead to highly inaccurate relevance ranking in answering object-oriented queries. In this paper, we propose a paradigm shift to enable searching at the object level. In traditional information retrieval models, documents are taken as the retrieval units and the content of a document is considered reliable. However, this reliability assumption is no longer valid in the object retrieval context when multiple copies of information about the same object typically exist. These copies may be inconsistent because of diversity of Web site qualities and the limited performance of current information extraction techniques. If we simply combine the noisy and inaccurate attribute information extracted from different sources, we may not be able to achieve satisfactory retrieval performance. In this paper, we propose several language models for Web object retrieval, namely an unstructured object retrieval model, a structured object retrieval model, and a hybrid model with both structured and unstructured retrieval features. We test these models on a paper search engine and compare their performances. We conclude that the hybrid model is the superior by taking into account the extraction errors at varying levels.

Nie, Zaiqing, Yunxiao Ma, Shuming Shi, Ji-Rong Wen and Wei-Ying Ma. WWW 2007 (2007). Articles>Web Design>Information Design>Search

The Discoverability of the Web  

Previous studies have highlighted the high arrival rate of new content on the web. We study the extent to which this new content can be efficiently discovered by a crawler. Our study has two parts. First, we study the inherent difficulty of the discovery problem using a maximum cover formulation, under an assumption of perfect estimates of likely sources of links to new content. Second, we relax this assumption and study a more realistic setting in which algorithms must use historical statistics to estimate which pages are most likely to yield links to new content. We recommend a simple algorithm that performs comparably to all approaches we consider. We measure the overhead of discovering new content, de- fined as the average number of fetches required to discover one new page. We show first that with perfect foreknowledge of where to explore for links to new content, it is possible to discover 90% of all new content with under 3% overhead, and 100% of new content with 9% overhead. But actual algorithms, which do not have access to perfect foreknowl- edge, face a more difficult task: one quarter of new content is simply not amenable to efficient discovery. Of the re- maining three quarters, 80% of new content during a given week may be discovered with 160% overhead if content is recrawled fully on a monthly basis.

Dasgupta, Anirban, Arpita Ghosh, Ravi Kumar, Christopher Olston, Sandeep Pandey and Andrew Tomkins. WWW 2007 (2007). Articles>Web Design>Search>Information Design

Making $10,000 a Pixel: Optimizing Thumbnail Images in Search Results

In search results, the old adage a picture is worth a thousand words rings true. When it comes to making your search results more efficient to use, more relevant, and more attractive, images reign supreme. There is simply nothing else on your search results pages that can come close to offering the same potential as thumbnail images for dramatically increasing your conversion rates and revenues.

Nudelman, Greg. UXmatters (2009). Articles>Web Design>Graphic Design>Search

Ten Remarkably Effective Strategies for Driving Traffic

In the last six months, we've been lucky enough to help quite a few companies and websites drive significant traffic to their sites. Many of these campaigns have been constructed around the goal of building search engine rankings, as this is our primary business, but we've also found that our ability has given us great power in the fields of brand-awareness and marketing overall. Thus, the following ten processes are primarily about building traffic and through it, attention.

SEOmoz (2006). Articles>Web Design>Search Engine Optimization

Search Engine Ranking Factors

This document represents the collective wisdom of 37 leaders in the world of organic search engine optimization. Together, they have voted on the various factors that are estimated to comprise Google's ranking algorithm (the method by which the search engine orders results). The result is a resource of incredible value - although not every one of the estimated 200+ ranking elements are included, it is my opinion that 90-95% of the knowledge required about Google's algorithm is contained below.

SEOmoz (2008). Articles>Web Design>Search Engine Optimization

Designing for Faceted Search

Faceted search, or guided navigation, has become the de facto standard for e-commerce and product-related websites, from big box stores to product review sites. But e-commerce sites aren’t the only ones joining the facets club. Other content-heavy sites such as media publishers (e.g. Financial Times: ft.com), libraries (e.g. NCSU Libraries: lib.ncsu.edu/), and even non-profits (e.g. Urban Land Institute: uli.org) are tapping into faceted search to make their often broad-range of content more findable. Essentially, faceted search has become so ubiquitous that users are not only getting used to it, they are coming to expect it.

Lemieux, Stephanie. User Interface Engineering (2009). Articles>Web Design>Information Design>Search

Indexing the Web—It’s Not Just Google’s Business

Web databases do much more than passively store information. Part of their power comes from indexing records efficiently. An index serves as a map, identifying the precise location of a small piece of data in a much larger pile. For example, when I search for “web development,” Google identifies two hundred million results and displays the first ten—in a quarter of a second. But Google isn’t loading every one of those pages and scanning their contents when I perform my search: they’ve analyzed the pages ahead of time and matched my search terms against an index that only references the original content.

Mullican, Lyle. List Apart, A (2009). Articles>Web Design>Information Design>Search Engine Optimization

Your Website is a Satellite. Contextual Search is the Sun

The internet is more like the heliocentric model championed by Galileo, with search as the sun. It is an ever-growing collection of distribution channels, each with their own audience, revolving around an increasingly contextual search experience. It’s time to expand your perspective to account for this. But, like Galileo, you may have a hard time with the authorities as you start to act on this understanding.

Tipping Point Labs (2009). Articles>Web Design>Search>Search Engine Optimization

The Illusion of SEO vs. the Reality of Great Content

SEO techniques will increase your search rankings and SEM will get you traffic on the top search engines. But a boatload of quality content will also accomplish these things and prepare you for the more contextual future of search.

Tipping Point Labs (2009). Articles>Web Design>Content Management>Search Engine Optimization