Current Research is Driving Search UI

1) Search UI research method: task success measures
Task-based studies give the user a set of structured tasks to perform, while researchers track the results using cameras, eyetracking, note-taking or other methods.

Yahoo is concentrating on the context of the search, and moving the user from "to do" to "done". This involves learning where search fits into various tasks, and making the right sort of information available for each context. http://www.seroundtable.com/archives/018046.html

"Yahoo is taking a task-based approach to its search strategy, improving results to focus on the user's task stage, according to Andrew Tomkins, chief scientist for search at Yahoo" & "the next generation of search will be about understanding the task a user has in mind and changing the way search operates to get those things done." http://searchenginewatch.com/showPage.html?page=3628767

My take: Yahoo's approach ties in closely to naturalistic ideas of information availability, wayfinding and information scent. The methods we use to complete tasks, or to browse information, are still only partially understood at best. In some ways the experience of information discovery is almost like a dream where one minute we are taking a meal and the next hovering over a pond: we perceive something, and then just as we begin to comprehend it that thing may evolve into something else and take us somewhere which seems unrelated. Yet upon careful analysis we see that the first thing and the second were related in our consciousness. Perhaps we have a favorite restaurant that overlooks a pond - hence the connection between eating and a small body of water. Likewise, we still have much to learn by tracing and understanding connections among the seemingly serendipitous steps a user takes to find information online. Web search should strive for improved 'flow': present-day systems have a great utility, but they generally fail to inspire. Google's holiday logo drawings and Live search's new interactive hotspots are examples of serendipitous interfaces which can be further leveraged through personalization.

2) Search UI research method: eyetracking/heat maps
Eyetracking uses devices that track user eye movements as they attempt search tasks, usually in a lab setting. Heat maps are graphic renderings of where the users' eyes tend to focus on the page. These maps created using the data from eyetracking studies.

Assumptions about page layouts are evolving as researchers study users of pages with more varied layouts. "In the past, the definition of SERP [results page] real estate was fairly static: top and to the left. In the future, it seems it will be far more difficult to define." http://searchengineland.com/070921-070852.php

Another study found that eyetracking data supports current efforts to know more about user intent. "There are five specific patterns consumers tend to use depending on where they are in the sales/educational cycle" http://www.marketingsherpa.com/sample.cfm?contentID=3152

My take: humans have evolved to make the most of sparse information, creating a gestalt (whole) out of bits and pieces. Most researchers, when applying heatmap information to the page, assume that only the hot areas really matter. Yet, the periphery creates context and atmosphere. One possible approach is to array categorically related material in peripheral areas. This will help the user (perhaps subconsciously) to become more aware of concepts that may be related to the search. Just as ads may be effective even though they are never clicked, images and phrases placed peripherally in a reasonably clean layout may have a profound, if secondary impact, when compared the treasured "top hit". Further experimentation may uncover a better use for secondary results page areas than just a set of very similar results. Certainly the success of contextual advertising suggests there is more going on across the page than we might initially think.

3) Search UI research method: modeling expert behavior/expert systems
The expert systems approach attempts to reproduce knowledge in the form of very sophisticated heuristics (rules of thumb). Classic expert systems studies used chess as an research subject: expert players were found to have internalized 'chunks' of knowledge which gave them a superior ability to look ahead and respond in the game, when compared to the brute algorithmic strategies typically employed by a less experienced player.

One group is exploring online behavior modeling which would leverage groups of users with common profiles. "Imagine a world where your computer would generate a profile, a meme map about you based on your interactions with the web and refine your experience based on this map." http://youlicit.wordpress.com/2007/09/27/expert-systems-personalized-recommendations/

Steve Spalding envisions systems whereby "search engines would aggregate and generalize these user profiles and use them to personalize and enhance search". http://howtosplitanatom.com/news/how-to-explain-expert-systems/

My take: Search engines will begin to rank users by their effectiveness at finding what they are looking for, and the results of expert searches will be linked to their queries. Less experienced searchers will then be more likely to find what they are looking for earlier in their search path. For example, an expert searcher looking for "Las Vegas Hotels" may gravitate quickly to a reputable local guide with hotel reviews. Knowing this, the engine will subsequently rank that guide's pages higher than it otherwise would for queries related to "Las Vegas Hotels". Later users are then able to benefit from the expert user's ability to rapidly discriminate among sites.

4) Search UI research method: ethnographic field studies
Ethnographic study relies on rich qualitative data collected in the field, often in a naturalistic manner. Data may take the form of interviews, journals, film, etc.

Yahoo's Life Series explored relationships between life events and search habits. "people experiencing such events share certain common characteristics including a desire for trusted information sources, heavy reliance on research prior to making decisions and increased time spent on research during their life event." http://docs.yahoo.com/docs/pr/release1267.html

Opinion leader findings appear to drive much of Yahoo's current efforts (including UI-related initiatives such as Search Monkey). "Brand advocates are adventurous opinion leaders who are socially well-connected, express their opinions and viewpoints and continually discover new content online." http://createwithcontext.com/media/yahoo-summit.pdf

My take: Yahoo is using rich qualitative data to uncover the dynamics behind how people search. This highlights the present difference between Google and Yahoo. Google is focused on having the best algorithm, and being able to accomplish the most using the largest data set while using the least reasonable amount of processing. Google has more or less perfected the mechanistic model of search, and it shows. Yahoo, on the other hand, is chasing game-changing innovation through a dual effort: allowing radical collaboration (SearchMonkey, BOSS) and gaining better understanding of the human side of the search equation. Yahoo's approach is human-centered, and resembles that of the successful inventor: discover a market need that has not been addressed, and then find a way to fill that need.