Tuesday, November 5, 2013
The Case for Slow Search
As discussed in a previous post, Web searchers expect search engines to return results instantaneously. To meet these expectations, search engines make many compromises to shave milliseconds off their response time. But it is ironic that a few milliseconds matter so much when over half of our interactions with a search engine involve multiple queries and take minutes or even hours. Just think, for example, of the last time you planned a vacation or researched a potential medical diagnosis. For these tasks, the quality of the experience – and not speed – is what matters.
While someone searching for a specific website or a straightforward fact will always want an immediate response, people invest a significant amount of time in more complex or exploratory search tasks. Not surprisingly, search engine response time, while important for all types of tasks, impacts different types of tasks differently. Millisecond differences in response time negatively impact navigational queries more than they do information queries.
In recent years a number of “slow movements” have emerged that advocate for reducing speed in exchange for increasing quality. You are probably familiar with the slow food movement, which proposes using traditional and regional food preparation as an alternative to fast food. Other examples include slow parenting, slow travel, and even slow science. Building on these movements, Kevyn, Ryen, Sue, and I are exploring the concept of slow search, where a nuanced notion of time is employed to create a high quality search experience. Slow search can be used to help users take the necessary time to learn as they search, understand their sources, and explore tangents, as well as to algorithmically identify high quality, relevant information. Much of our early efforts in understanding slow search has focused on how search engines might make use of additional time to produce better results.
Algorithmic slow search approaches are particularly valuable when people have intermittent, slow, or expensive network connections. In such cases it can be difficult for searchers to employ traditional search strategies, such as rapidly reformulating queries. You are probably familiar with a type of slow search, but call it “mobile search.” Because mobile phones have limited bandwidth, slower search processing times may be acceptable given most of the latency a searcher observes is caused by network latencies in fetching data to the device. Search engines designed to support search in rural regions already make use of additional time to help searchers limit the number of iterations necessary to find what they are looking for. Likewise, future space travelers may also appreciate slow search. It takes over 25 minutes for information to travel from Mars to Earth and back again. If a search engine were to take an additional few minutes to identify better results during the round trip, it is unlikely that the searcher would even notice the extra time invested.
Algorithmic slow search approaches can also be used to proactively identify content that a user is likely to search for in the future. For example, it is now possible to predict if an individual will resume a search task at a later date. Search engines can make use of the time between sessions to slowly produce high-quality search results that could then be presented immediately when a search task is resumed.
The question, of course, is how can a search engine use extra time to actually produce better results? We have invested so much effort into making search engines as fast as possible that it is almost impossible to imagine what search should look like without time as a constraint. But I am excited to try!
J. Teevan, K. Collins-Thompson, R. W. White, S. T. Dumais and Y. Kim. Slow Search: Information Retrieval without Time Constraints. HCIR 2013.