"Let your hook always be cast. In the pool where you least expect it, there will be fish."
Serendipity refers to an accidental yet desirable discovery. It plays a surprising role. Alexander Fleming's failure to disinfect cultures of bacteria before leaving for vacation led to the discovery of penicillin. Archimedes figured out how to measure the volume of irregularly shaped objects by taking a bath. And Columbus found America by sailing for India. LSD, Uranus, Viagra, safety glass, infrared radiation, microwave ovens, inkjet printers, Corn Flakes, and chocolate chip cookies are all accidental discoveries. It's amazing what we find while searching for something else. In fact, search and serendipity often travel together. Discovery requires that we move beyond what we know. Ironically, the most frustrating journeys can lead to the best and least expected destinations. As the dictionary evangelist Erin McKean remarked, "Serendipity is when you find things you weren't looking for because finding what you are looking for is so damned difficult."
This reveals a tension in search between relevance and interestingness. The most interesting (and valuable) results aren't always the most relevant. Often, the best answers lie just beyond the edges of what we know to seek. In this ambiguity lies the promised land of personalization, collaborative filtering, recommender systems, and discovery engines. But software algorithms take us only so far. We must also rely on an idiosyncratic mix of subscriptions and memberships that veil the signal in noise. And, of course, exposure to the right publications and discussions is only half the puzzle. Insight requires that we make strange connections that bridge contexts and categories. It's easy to copy from competitors, but mapping an e-commerce feature to the unique requirements of mobile social search requires vision and ingenuity. Colorful stories and surprising examples pave the path to serendipity, but our minds are the real engines of discovery.
Earlier, we defined the primary categories of search as web, e-commerce, enterprise, desktop, mobile, social, and real time. Mostly, we labor within one of these boxes, but that doesn't mean we can't learn from the others. For instance, IBM offers an inspiring case study in applying ideas across categories. In the earliest days of Web 2.0, folks at IBM saw the potential of social software to transform knowledge management within the enterprise. Long before Enterprise 2.0 entered our vocabulary, researchers at IBM had rolled out Dogear, an enterprise-class social bookmarking and tagging tool similar to Delicious. The intranet team also deployed blogs, wikis, and the award-winning BluePages employee directory. While executives at other firms fretted about the dangers of tagging, IBM's w3 was successfully increasing the productivity, collaboration, and innovation of its massive global workforce. The IBM team also launched an enterprise social search project to harness all that social data to improve search and social networking. Unfortunately, the first attempt was a flop. As shown in Figure 5-1 users could find people, discussions, and news relevant to their queries simply by clicking the tabs along the top of each result set. But nobody clicked the tabs. Site analytics suggested a very low interest in this social content.
To its credit, the team didn't quit. Instead, it tried again. In the second version, shown in Figure 5-2 the interface brought sample social content right to the surface. Users got a glimpse of relevant and popular content in blogs, wikis, forums, news, and the employee directory. Suddenly, clickthroughs skyrocketed. Users were intensely interested in this social content. They simply hadn't known what they were missing. With this success in its pocket, the team began integrating social data, such as ratings and tag frequency, directly into the algorithms for enterprise search. A page that's been bookmarked, for instance, receives a boost. Similar to Google's PageRank, this form of social search improves both relevance and user satisfaction. Together, these advances in enterprise social search make IBM a better business and a better place to work.
Of course, not everyone has the team and technology of IBM. It's vital to fit the engine to the institution. At Washtenaw Community College, for example, it made sense to go with Google. By selecting an easy-to-deploy solution, its small web services team reserved the time and budget necessary to invest in an information architecture and visual identity overhaul. Plus, it integrated search into the new framework quite nicely, and managed to add Best Bets or "Featured Results" for many of the most common queries.
Most of the examples we encounter on a daily basis don't come with a backstory. But that doesn't mean we can't learn from each one, provided we allow for the differences. For instance, the faceted search of Cisco.com, shown in Figure 5-4 offers insights for folks working on intranets. In particular, the document type and task facets are eminently transferable.
In the local category, Citysearch, shown in Figure 5-5 offers a great example of actionable results. Since the options appear on result rollover, they don't clutter the screen, but are easy to discover.
In real time, Twitter, shown in Figure 5-6 provides dynamic updates, letting us see how many matching tweets have been posted since we started searching. Results are presented within the framework of the main user interface, so people can stay in the flow and keep on tweeting on.
In contrast, TweetDeck is a desktop application that makes it easy to organize updates into multiple columns by source, group, format, and search string. Users can modify the refresh rate of each column and opt for visual and/or audio notifications.
Research products designed for libraries, universities, and other institutional subscribers represent an interesting but well-hidden category of search applications. Access generally requires membership, subscription, or an onsite visit to a public or college library. Historically, these research products have been designed for the librarians who purchase them rather than for the students and faculty who use them, but a shift toward user-centered design is underway.
ProQuest Smart Search, shown in Figure 5-8 is an innovation designed for end users. Since most folks aren't comfortable extracting controlled vocabulary terms from a thesaurus, ProQuest developed technology that analyzes a user's query, maps the terms to the controlled vocabulary, and then offers suggestions for related topics and publications.
LexisNexis Academic, a massive law, business, and news database, adds its own twist to the faceted navigation model with a flexible panel, shown in Figure 5-9 that users can drag to the right to accommodate long facet values, such as the titles of law reviews, journals, and cases.
EBSCO's visual search interface to the PsycINFO database, shown in Figure 5-10, offers an attractive alternative to a traditional faceted navigation display. It may not be the right approach for most mainstream search applications, but it does inspire us to think outside the box.
In short, whether we're focused on web, e-commerce, enterprise, desktop, mobile, social, or real time, we can still borrow patterns and spark ideas by looking outside our category.