Saturday, September 12, 2009

The Next Generation of Web Search

Nova Spivack published a couple of months ago, an interesting post entitled The Next Generation of Web Search -- Search 3.0, where he explains why he thinks the next generation of Web search is coming sooner than expected. According him, we will see several shifts in the way people search, and the way major search engines provide search functionality to consumers.

To explain his point of view, he separates the fases of web in decades:

- Web 1.0, the first decade of the Web (1989 - 1999), was characterized by a distinctly desktop-like search paradigm. The overriding idea was that the Web is a collection of documents, not unlike the folder tree on the desktop, that must be searched and ranked hierarchically. Relevancy was considered to be how closely a document matched a given query string.

- Web 2.0, the second decade of the Web (1999 - 2009), ushered in the beginnings of a shift towards social search. In particular blogging tools, social bookmarking tools, social networks, social media sites, and microblogging services began to organize the Web around people and their relationships.

- In the coming third decade of the Web, Web 3.0 (2009 - 2019), there will be another shift in the search paradigm. This is a shift to from the past to the present, and from the social to the personal.Established search engines like Google rank results primarily by keyword (semantic) relevancy. Social search engines rank results primarily by activity and social value (Digg, Twine 1.0, etc.). But the new search engines of the Web 3.0 era will also take into account two additional factors when determining relevancy: timeliness, and personalization.

He considers these two themes, present and personal, will define the next great search experience, but we need to make progress on a number of fronts:

- Search engines need better ways to understand what content is, without having to do extensive computation. The best solution for this is to utilize metadata and the methods of the emerging semantic web.

- Metadata reduces the need for computation in order to determine what content is about -- it makes that explicit and machine-understandable. Metadata makes a dramatic difference in search of the larger non-real-time Web as well.

- In addition to metadata, search engines need to modify their algorithms to be more personalized. Instead of a "one-size fits all" ranking for each query, the ranking may differ for different people depending on their varying interests and search histories.

- To provide better search of the present, search has to become more realtime. To this end, rankings need to be developed that surface not only what just happened now, but what happened recently and is also trending upwards and/or of note. Realtime search has to be more than merely listing search results chronologically. There must be effective ways to filter the noise and surface what's most important effectively. Social graph analysis is a key tool for doing this, but in addition, powerful statistical analysis and new visualizations may also be required to make a compelling experience.

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