Announcing a Tag-based Exploration and Search System

I’m pleased to announce, a tag-based exploration and search system for bookmarked content on the Web. The tagline for the project is “An interactive guide to what’s useful on the Web”, since all of the content has been socially vetted (i.e. someone found it useful enough to bookmark it.)

MrTaggy is an experiment in web search and exploration built on top of a PARC algorithm called TagSearch. Think of MrTaggy as a cross between a search engine and a recommendation engine: it’s a web browsing guide constructed from social tagging data. We have collected about 150 million bookmarks from around the Web.

Unlike most search engines, MrTaggy doesn’t index the text on a web page. Instead, it leverages the knowledge contained in the tags that people add to web pages when using social bookmarking services. Tags describe both the content and context of a web page, and we use that information to deliver relevant contents.

The problem with using social tags is that they contain a lot of noise, because people often use different words to mean the same thing or the same words to mean different things. The TagSearch algorithm is part of our ongoing research to reduce the noise while amplifying the information signal from social tags.

We also designed a novel search UI to explore the tag space. The Related Tags sidebar outlines the content landscape to help you understand the space. The relevance feedback capabilities enable you to tell the system both positive and negative cues about directions where you want to go. Try clicking on the Thumbs Up and Down to give feedback to MrTaggy about the tags or results that you liked, and see how your rating changes the result set on-the-fly. At the top of the result set, we have also provided top search results from Yahoo’s search engine when we think the results there might help you.

Enterprise Use

In addition to exploring TagSearch in the consumer space, we have also explored the use of TagSearch in the enterprise social tagging and intranet search systems. Surprisingly, the algorithm worked well even with a small amount of data (<50,000 bookmarks). For enterprise licensing of the underlying technology and API, contact Lawrence Lee, Director of Business Development, at lawrence.lee [at] parc [dot] com.

We would appreciate your feedback (comment on the blog here), or send them to mrtaggy [at] parc [dot] com, or submit at

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