Search is a wicked problem, with no apparent universal solution in sight. Different technologies and approaches to search exist side by side, serving a multitude of business goals and user needs. In my work with search user experience I find it important to understand the particular strengths and weaknesses of search concepts like Best Bet and Faceted Search, since part of my job is to correctly align goals and needs with available technology. A poor choice of technologies or design patterns could very well cripple the entire user experience. But how do I know which concepts will work?
To answer my own question, I’ve spent some time the last few days putting together a topology of search concepts, which I’m thrilled to share with you now. I’ve made a scatter plot of a handful of search technologies and patterns, with the purpose of revealing some basic structure and similarities. The main elements of this topology are the set of search concepts, the 2 dimensions of the scatter plot, and the descriptions of each quadrant. You can see the result so far in the illustration at the top of this page.
This is still work in progress, so I’m eager to get feedback from you. I may have left things out, plotted things the wrong way, or described things poorly. Whatever you have to say, please leave a comment on this post.
Here is how I define the 2 dimensions of the scatter plot for the purpose of this topology:
- Algorithm vs. User Powered – In the absence of a better name, positioning along the algorithm vs. user powered dimension reflects to what extent human or machine intelligence is responsible for retrieving precise and accurate information in response to the query.
- Information Accessibility – By implementing a search concept for a given information space, information accessibility is a measure of how much easier it becomes to find any document of interest within that space. If the time it takes (and/or the number of steps required) to retrieve a particular document goes down, the general information accessibility goes up. Read more about accessibility in information retrieval.
The scatter plot is divided into 4 quadrants, so that the concepts positioned within each quadrant share a common set of characteristics related to business value, user experience and technological capabilities. Here is how I describe these quadrants:
- Simple Search – When you know what you want, and you can express that need with a few keywords, the simple approach to search fits the bill. It’s by no means trivial to create simple experiences, but the general information accessibility is quite low for these concepts. Google, FAST/Microsoft, Exalead and Lucene are some of the champions of simple search.
- Superficial Search – The superficial approach to search leverages user behavior to feel the pulse of communities, and to surface popular and current material. Superficial search is often very efficient when you don’t need to dig deep into the information space. Amazon, Twitter, PostRank and Twingly are some of the champions of superficial search.
- Ingenious Search – With a model-knows-best approach, ingenious search relies on sophisticated algorithms to determine user intent and content semantics. Clever algorithms can be said to be cost effective, but are comparably mode difficult to implement and execute well. Autonomy, Powerset, Grokker and Wolfram Alpha are some of the champions of ingenious search.
- Diligent Search – The diligent approach to search favors human intellectual effort over clever algorithms. Given an initial search result, users are asked to further disambiguate their queries in order to effectively explore the information space. Endeca, eBay, Freebase and Apache Solr are some of the champions of diligent search.
I won’t get crossed if you say my matrix looks a bit like Gartner’s Magic Quadrant, but there’s at least one big difference. Contrary to Gartner’s quadrants, this matrix does not suggest any better-than/worse-than relationships between the data points. Each quadrant is just different from the others, and the search technologies and patterns found within simply serve different purposes, having their own particular strengths and weaknesses.
Whether you agree or disagree with my way of mapping the world of search concepts, please share your thoughts. I’ll make sure to give credit to everybody who contributed significantly when I publish the final result. Cheers!
You may also download a poster with both the diagram and the quadrant descriptions.