Skip to main content

Google Panda


Google Panda

Google Panda is a change to the Google's search results ranking algorithm /as on February 2011.Google's Panda has received several updates since the original rollout in February 2011, and the effect went global in April 2011.

Google's new Panda machine-learning algorithm is named after engineer Navneet Panda. Basic goal was to look for similarities between websites people found to be high quality and low quality.


Major Objective: The change aimed to lower the rank of "low-quality sites", and return higher-quality sites near the top of the search results.

The Panda process

Google Panda was built through an algorithm update that used artificial intelligence. Human quality testers rated thousands of websites based on measures of quality, including design, trustworthiness, speed and whether or not they would return to the website. Google has introduced many new ranking factors and some of the older ranking factors like PageRank have been downgraded in importance.

Google Panda is updated from time to time and the algorithm is run by Google on a regular basis.On April 24, 2012 the 'Penguin' update was released, which affected a further 3.1% of all English language search queries, highlighting the ongoing volatility of search rankings.

How Panda different from other algorithms?

Google Panda impacts an entire site's ranking or specific section rather than just the individual pages on a site. In addition to other changes, Panda seems to focus on the date of a web page.

Some experts think this has adversely impacted sites with lots of "evergreen content". Because evergreen content usually has an older publication date, Panda seems to reduce its visibility in search results. For searchers looking for in-depth information, many of these evergreen posts are great sources of knowledge on a topic.

Source:http://en.wikipedia.org/wiki/Google_Panda
Labels:

Comments

Post a Comment

Popular posts from this blog

Advantages and Disadvantages of EIS Advantages of EIS Easy for upper-level executives to use, extensive computer experience is not required in operations Provides timely delivery of company summary information Information that is provided is better understood Filters data for management Improves to tracking information Offers efficiency to decision makers Disadvantages of EIS System dependent Limited functionality, by design Information overload for some managers Benefits hard to quantify High implementation costs System may become slow, large, and hard to manage Need good internal processes for data management May lead to less reliable and less secure data
1 comment
Read more

Inter-Organizational Value Chain

The value chain of   a company is part of over all value chain. The over all competitive advantage of an organization is not just dependent on the quality and efficiency of the company and quality of products but also upon the that of its suppliers and wholesalers and retailers it may use. The analysis of overall supply chain is called the value system. Different parts of the value chain 1.  Supplier     2.  Firm       3.   Channel 4 .   Buyer
Post a Comment
Read more

Big-M Method and Two-Phase Method

Big-M Method The Big-M method of handling instances with artificial  variables is the “commonsense approach”. Essentially, the notion is to make the artificial variables, through their coefficients in the objective function, so costly or unprofitable that any feasible solution to the real problem would be preferred, unless the original instance possessed no feasible solutions at all. But this means that we need to assign, in the objective function, coefficients to the artificial variables that are either very small (maximization problem) or very large (minimization problem); whatever this value,let us call it Big M . In fact, this notion is an old trick in optimization in general; we  simply associate a penalty value with variables that we do not want to be part of an ultimate solution(unless such an outcome is unavoidable). Indeed, the penalty is so costly that unless any of the  respective variables' inclusion is warranted algorithmically, such variables will never be p
Post a Comment
Read more