Is Yahoo a technology company?

Can Yahoo beat Google on technology?
Written by Donna Bogatin, Contributor
Yahoo CEO Terry Semel is being skewered for allegedly not having “deep technical knowledge.”

In “Yahoo vs. Google: Why Panama is the wrong destination” I compare Yahoo and Google’s corporate histories and business models, rebuking a Wired story (“How Yahoo (Terry Semel) blew it”) assertion that it has “empirical evidence” proving that Yahoo trails Google because Semel lacks “an intuitive sense of the company's plumbing” and “doesn't know the right questions to ask about technology.

I ask: "Is Yahoo a media company, but Google a technology company?":

Google’s $150 billion market cap is fueled solely from advertising on the Web, Yahoo’s $38 billion market cap is also fueled from advertising on the Web.

Google CEO Eric Schmidt touts advertising potential to Wall Street, not technology, as does Yahoo CEO Terry Semel.

The Web advertising that supports both Yahoo and Google is technology intensive. Is Yahoo a technology company, as well as a media company?

[poll id=34]

From 2003 to 2005, Yahoo invested over a billion dollars in product development and operates a half-dozen R & D facilities worldwide:

We continually enhance and expand our existing offerings and develop new offerings to meet evolving user needs for technological innovation and a deeper more integrated user experience.

As a complement to our core engineering and production teams, we also have scientists working in our Yahoo Research Labs dedicated to developing novel algorithms and technology that empower users, businesses, advertisers, and publishers worldwide to maximize the social and economic potential of the Internet.  Yahoo! performs research to address fundamental problems facing users, such as: making their search for information on the Internet easier and more efficient; bringing them tools to help solve their problems. We also help advertisers connect with customers most likely to be interested in their products, maximizing the advertisers’ marketing investments and providing a better overall user experience.



Create and apply techniques based on information retrieval, Web analysis, graph theory, machine learning, data mining, recommender systems, and other areas. The next generation in search.

Machine Learning

Research in machine learning and data mining to discover scalable computational methods for finding useful models from massive amounts of data. Models can be used in a variety of tasks, including clustering/discovery, regression/prediction, and classification/ranking.


Study of online markets and communities through analysis, modeling, and mechanism design; Ccomputational aspects (complexity, combinatorics) of a number of economic/social systems including prediction markets, sponsored search auctions, recommender systems, and query incentive networks.

Media Experiences and Design

Invent social media and mobile media technology and applications that will enable people to create, describe, find, share, and remix media on the web; Fields of media technology, social software, context-aware computing, mobile computing, and user and design research.

Community Systems

Scientific foundations of online communities and social search….scalable, customizable infrastructure to power the next generation of online communities, and understand community structure, dynamics and evolution; and social search--using shared interactions to enhance and extend web search.


Topic Clustered RSS Reader

We have been experimenting with various clustering problems (small and large) for some time now. An interesting aspect of the clustering problem is identifying the correct number of clusters for a given dataset. This is particularly difficult when the dataset is dynamic.

To solve the problem we are using a variation of a single-linked clustering we developed. We have found it to be reasonably accurate when dealing with small scale clustering problems. This is an attempt to demonstrate and test the method we developed. We chose the RSS News as a test case because it not only allows us to demonstrate the results in a concise and effective way but also makes the testing easier due to the size of the problem. 


Visualization of the evolution of Flickr tags over time. An algorithm chooses the eight most popular tags for each day, along with interesting photos corresponding to each of these tags. TagLines provides via Flash two metaphors for visualization: the waterfall and river. In the waterfall metaphor, the top eight tags are displayed in eight rows, with font sizes proportional to their popularity intensity and one or more photos from Flickr corresponding to each tag. In the river metaphor, tags flow from right to left, displaying one photo from Flickr. Both visualizations step forward, one per day, and offer the ability to random access time by a bar displayed on the top of the screen.


Intent driven search. Mindset is an early demonstration of our ongoing machine learning research applied to the problem of web search. We trained an automated text classifier which determines whether any given web page is mostly "commercial" (e.g. it's main purpose is to sell you something) or not. We used new advanced algorithms recently developed at Yahoo! Research to train a classifier which is accurate and yet still fast enough to classify web pages on the fly as they show up in search results.


Yahoo on its research mission: “The mission of Yahoo Research is to develop the world-class science that will deliver the next generation of businesses to the company.”

Yahoo is committing capital and resources to that end.


Editorial standards