Understand the two different faces of search: exploratory search and focalized search

Posted on March 26, 2010

3


To most people, there is just one type of search: key in a couple of keywords and then hope that you will find what you are looking for. Sometimes this works, but many times it does not. One of the reasons why users are disappointed with the results of a search engine is because there are actually just as many search approaches and search goals as there are different search engines and search techniques. Using the right tool for the right problem is essential to avoid disappointment.

When looking at search, we can very clearly differentiate search for: web, e-commerce, enterprise, desktop, mobile, social, and real time, discovery and information governance purposes.

In each of these different applications, search, relevance ranking, relevance feedback, user interaction, result navigation and document viewing have different purposes and also work differently under the hood.

Basically, we can differentiate two main types of search:

  1. Focalized Search
  2. Exploratory Search

Focalized search: a search where a user typically knows exactly what he or she is looking for, where a user typically searches data where the content is well known and where the user is primarily interested in just the best document or web site and not in the return of all the potentially relevant documents and web sites. Focalized search is also referred to as web-search or portal search.

Typical focalized search engines are almost all web-search engines, but also the open source java engine from Lucene. These engines work well for web portals, personal search, mobile or social search, but they are less suited for discovery, compliance, investigative, and legal search. Especially because the only return the best results and not all potentially relevant results, they lack advanced navigation and have often only have relevance feedback based on popularity or on other non-exhaustive techniques.

Also, these engines cannot handle wildcards, fuzzy searches and other “find more” or “find similar” search techniques very well (it may work, but often it becomes very slow on larger collections or it is limited to generative techniques that are not exhaustive).

Exploratory search, on the other hand, is a specialization of information exploration which represents the activities carried out by searchers who are either:

  1. unfamiliar with the domain of their goal (e.g. the need to learn about the topic in order to understand how to achieve their goal), or
  2. unsure about the ways to achieve their goals (either the technology or the process), or
  3. are even unsure about their goals in the first place.

This is exactly what is the case  in discovery, compliance, investigative, intelligence and information governance search applications.

Consequently, exploratory search covers a broader class of activities than typical information retrieval, such as investigating, evaluating, comparing, and synthesizing, where new information is sought in a defined conceptual area; exploratory data analysis is another example of an information exploration activity. Typically, therefore, such users generally combine querying and browsing strategies to foster learning and investigation.

Exploratory search techniques are breaking through in search applications, but the required additional information to use them effectively depends completely on content analytics, text-mining technology and advanced result navigation and visualization. Also, document based relevant feedback, taxonomy support and extensive meta-data management are essential tools.

Faceted search is one of the techniques used in exploratory search. It is sometimes also referred to as faceted navigation or faceted browsing. This is a technique for accessing a collection of information represented using a faceted classification, allowing users to explore by filtering available information. A faceted classification system allows the assignment of multiple classifications to an object, enabling the classifications to be ordered in multiple ways, rather than in a single, pre-determined, taxonomic order. Each facet typically corresponds to the possible values of a property common to a set of digital objects.

Facets are often derived by analysis of the text of an item using entity extraction techniques or from pre-existing fields in the database such as author, descriptor, language, and format. This approach permits existing web-pages, product descriptions or articles to have this extra metadata extracted and presented as a navigation facet.

Additional search tools such as content analytics, text-mining, auto-classification, faceted search, data visualization are absolutely essential for exploratory search.

In this context, I highly recommend the recent book on Search Patterns, which is a pleasure to read and which also addresses these different types of search in great detail.

Advertisements