Search_subm06.pdf (application/pdf Object) TheSocialCV.com. Search. Search literature. IA Heuristics for Search Systems. Sep 02, 2004: IA Heuristics for Search Systems Another day, another project, another set of IA heuristics. A client asked me to kick the tires of their search system, so I decided to expand on the search aspects of the information architecture heuristics that we came up with a couple weeks back. This time, I tried to align and categorize these guidelines with some common steps users take when searching a site. This semi-sequence goes like this: Locating search: Where is it?
Scoping search: What will be searched? Query entry: How can I search it? It might go without saying that these search heuristics are really geared to semi-structured text, not data; looking for ideas and concepts is a different undertaking than hunting for facts and figures (more on why they're different). OK, without further ado... Locating Search Is the search interface located where you'd expect it to be?
Scoping Search Does it communicate what content is being searched? Query Entry Retrieval Results Query Refinement 1. 2. Designing The Holy Search Box: Examples And Best Practices - Smashing Magazine. Advertisement By Smashing Magazine Editorial and György Fekete On content-heavy websites, the search box is often the most frequently used design element. From a usability point of view, irritated users use the search function as a last option when looking for specific information on a website. If a website’s content is not organized properly, an efficient search engine is not only helpful but crucial, even for basic website navigation.
In fact, search is the user’s lifeline to mastering complex websites1. The best designs offer a simple search box on the home page and play down advanced search and scoping. In practice, websites tend to grow over time, adding new content and, more importantly for us, adding new navigation options, such as additional content sections. When content organization appears to be a mess and it seems nearly impossible to find information, users are very unlikely to decide to browse the available sections of the website. When to Use Search? Search Box Showcase 1. Internal Site Search Analysis: Simple, Effective, Life Altering! Understanding of your site visitors’ intent is one of the most delightful parts of web data analysis. In this article, we’ll learn five ways to analyze your internal site-search data—data that’s easy to get, to understand, and to act on. But let’s take a step back. Why should you care about this in the first place?
Good question. In the good old days, people dutifully used site navigation at the left, right, or top of a website. Now when people show up at a website, many of them ignore our lovingly crafted navigational elements and jump to the site search box. There’s also one more (really important) reason, just in case you need a bit more convincing. All the search and clickstream data you have (from Google Analytics, Omniture, WebTrends, etc.) is missing one key ingredient: Customer intent. Your internal site-search data contains that missing ingredient: intent. Internal site-search data is easy to access and analyze, no matter which web analytics tool you use.
Basics first. Fig. 1. FAST SBP Usability Handbook.pdf (application/pdf Object)
Mobile search. Belkin. Anomalous States of Knowledge as a Basis for Information RetrievalN.J.Belkin The Author Nicholas J. Belkin is currently a Professor of Information Science, and Director of the Ph.D. Program at the School of Communication, Information and Library Studies at Rutgers University. He has many awards and honors under his belt, including but not limited to the SCILS Excellence in Research Award (2001) and the Lucille Kelling Henderson Memorial Lecturer which he received here at the School of Information and Library Science, University of North Carolina at Chapel Hill in 1997.
For more detailed information, including his current research and publications please visit: ________________________________________________________________________ The Article Introduction: Belkin proposes that a search begins with a problem and a need to solve it - the gap between these is defined as the information need. 7 Steps of the ASK Model: 1. Limitations of ASK: 5-05. Actionable results at Citysearch. Search Patterns: Design for Discovery. Evaluating the Usability of Search Forms Using Eyetracking: A Practical Approach. By Matteo Penzo Published: January 23, 2006 “The usability of forms is often massively important to the overall usability of a Web site.”
In this article, I’ll present findings from eyetracking tests we did to evaluate the best solutions for label placement in Web forms. Today, forms are the primary—often the only—way users have of sending data to Web sites. So, the usability of forms is often massively important to the overall usability of a Web site. We conducted these evaluations in the Consultechnology eyetracking lab. How We Tested We held three different rounds of test sessions. Patterns in the Test Results “Strong patterns emerged in the test results.
Even though some unanticipated results came to our attention during the tests, strong patterns emerged in the test results. Gaze plots showed the very different behaviors of rookies and pros when using search forms, as follows: Figure 1—Gaze plot showing the use of Google search by a rookie user Testing Search Forms Google Amazon and eBay. Testing Search – philosophe. How do you go about testing your site’s search functionality? First, start by identifying what can be tested. Some characteristics of search engines and systems are general, common to most versions, and I’ve identified some of these below. If your search has any special modifications or search strategies unique to your site/company, I recommend you get a handle on these common points first, before exploring how your search extends or pushes any informational retrieval boundaries.
Please note that all of the information on this page is aimed specifically at search against product catalogues. Accuracy The accuracy of a search system is its ability to find all the matching items in the information collection, usually a product database; in other words, if you search on the word metonymy, the query should correctly find every instance of this word. Test: Comparison of query results for back-end and front-end Test: Consistency of results over time Search Performance Test: Back-end Time Precision. A Structural Look at Search – philosophe.
As you examine the many possible characteristics of searches, it should become clear that many of these characteristics describe different aspects of search. If you look at search as being a transaction, where a user creates what is essentially a question, asks that question of a source of information, and then gets back an answer, then you’ll see that search involves layers of communication.
Comparing characteristics or functions across layers is usually not be very useful. For example, the statement “this search is stemmed” refers to an entirely different layer of search than does the statement “this search has multiple input fields”; both statements are important descriptions, but the characteristics are unrelated. it’s like the difference between saying that you’re looking for a red book, and saying that you’re looking for a book about dogs.
This essay presents a structural view of search, setting the stage for a discussion of the various types of search. How do I use the form? 8 Quick Ways to Fix Your Search Engine. Over the past year, I’ve evaluated the search experiences on a number of popular content sites. With the help of author and interface designer Darcy DiNucci, I picked apart the search and result designs from sites like Apple.com, NASA.gov, SchwabFoundation.org, and a variety of others. We focused on content sites, rather than e-commerce or Web applications, and we avoided general Web search engines entirely. Our finding, not surprisingly, is that almost every site’s search engine could use improvement. We also found that most organizations’ Web teams couldn’t really affect the quality of their search results — they were stuck tweaking search technologies that had already been purchased and installed.
Often, the most dramatic change they could make was in the design of the search and results interfaces. In some cases, as the old saying goes, this was like putting lipstick on a pig. But cleaning things up does help users find answers to their queries. 1. 2. 3. Check your search logs. 4. IA Heuristics for Search Systems. Exploring The Shift In Search Behaviors With Microsoft’s Jacquelyn Krones. Jacquelyn Krones (Photo: Annie Laurie Malarkey) I first met Jacquelyn Krones, a Senior Product Manager from Microsoft, at a search show. A mutual friend on the Bing team, Product Manager Stefan Weitz, introduced her to me and said, “You have to meet Jacquelyn. You speak the same language.” Stefan was right. Jacquelyn started talking about the research project she was then actively engaged in. She explained about the ethnographic approach Microsoft was taking to understanding search behavior in a broader context.
In today’s column, I wanted to share parts of that conversation with you. Qualitative research explores gray, murky areas to uncover insights impossible through more quantitative methodologies. As another fan of qualitative research, Ball State’s Michael Holmes, Director of Insight and Research at the Center for Media Design, once said, “Quantitative is essential for refinement and optimization of what you’re currently doing, but it won’t drive reinvention.”
Searchtools: site search, relevance heuristics, open information extraction, rebel search, and more links. Solr Powered ISFDB – Part #11: Using DisMax.