- Nov 5, 2023
- 2,118
- 481
Just sumbled up on this article from an old forum, sharing it here, perhaps this technology is already implemented in latest years on Google too, amybe could be useful to someone.
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Oddly enough, very few SEOs and webmasters know about BrowseRank, Microsoft's user behavior tracking technology. Although this development is almost 5 years old, it is quite relevant, and when working on a website (studying web statistics) it is worth keeping it in mind.
In addition to creating its own page ranking algorithm, Microsoft points out the drawbacks of Google’s PageRank. For example, there is a loop hole in that people can increase the page rank of their website by creating link farms. These consist of pointless web pages that point to a website in order to increase its importance and appear higher in search results. Microsoft feels that this can lead to deceiving results and pages that are not relevant to the the search keywords. Instead, Microsoft argues that monitoring user behavior on websites is a better alternative. However, there is always room for improvement for both page ranking algorithms. It is even suggested that a combination of the two algorithms could prove to have better performance.
CLICK GRAPH INSTEAD OF REFERENCE GRAPH
The main difference between BrowseRank and PageRank is the graph structure.
User web surfing graph In PageRank technology, the nodes of the graph are documents, and the edges are links. In BrowseRank technology, edges are formed not by links, but by clicks (transitions). In addition, meta-information about the duration of the session within the document is stored.
TWO SIGNIFICANT ADVANTAGES OF BROWSERANK ARE OBVIOUS:
significantly better resistance to link spam,
taking into account the duration of the session allows you to evaluate the usefulness of the document for the visitor.
The behavioral graph more accurately reflects the web surfing process, and therefore it is more useful for calculating the importance of documents. More visits to a page and more time spent means more importance of the page.
THE PRIMARY SOURCE OF USER BEHAVIOR DATA IS BROWSER BARS
All leading search engines have plugins that monitor the activity of a large share of the audience.
Example of click data To process data on user behavior, it is proposed to use “Markov chains” with continuous time. Experimental data demonstrates the superiority of the BrowseRank algorithm over the PageRank and TrustRank algorithms in determining document importance, spam control, and ranking.
Essentially, the data being collected can be represented by a record like a URL; TIME; TYPE [input | click]. There are two ways to navigate to a document: via a link from another document (click), or by typing the url in the browser address bar (input). Transition data retrieval mechanism:
Session segmentation.
A new session is initiated if there is a pause of 30 minutes or more from the moment of previous activity, or if the name of the site is entered in the address bar.
Formation of url pairs.
Within each session, pairs of urls are created from adjacent records. The url pair means that the transition was made using a link.
Formation of the initial distribution.
In each session, segmented by transition type, the first url is entered directly by the user. We consider such URLs to be “trusted” and call this traffic “green”. When processing data on user behavior, we consider transitions to these URLs to be a consequence of random distribution. Normalization by the frequency of visits to these documents gives the initial probabilities of visiting the corresponding pages.
Retrieving session duration.
For each pair of urls, the session duration of the first url is calculated by a simple date difference. If the url was the last one in the session, two options are possible. For time-segmented sessions, the duration of viewing the last url is calculated based on data on views of other pages. For sessions segmented by type, the time to view the last url is calculated based on the start time of the next session.
RESULTS OF USING BROWSERANK
Microsoft conducted two studies. The first was carried out at the site level to identify important sites and suppress spam. The second experiment was conducted at the document level to test BrowseRank to improve rankings.
Behavioral Graph ExponentThe first experiment used a dataset of approximately 3 billion records containing approximately 950 million unique URLs. It is interesting that the distribution of the number of views over viewing time is perfectly described by the classical exponential function. In this experiment, page-by-page data were not used; they were aggregated at the site level. The resulting behavioral graph consisted of 5.6 million nodes and 53 million edges.
Comparison of TOP-20 results when using different algorithms. An interesting sample is the TOP-20 sites obtained by ranking using three different algorithms. It can be seen that BrowseRank perfectly ranked MySpace, Youtube, Facebook and other sites that are characterized by long session times. Please note, these are 2008 data. Obviously, in this era of social networks and communication services, the difference between Pagerank and BrowseRank would be even more significant.
Using a random sample of 10 thousand sites, manually marked by assessors, it was shown that BrowserRank is more effective than TrustRank and PageRank in solving the problem of filtering spam sites.
______________
Oddly enough, very few SEOs and webmasters know about BrowseRank, Microsoft's user behavior tracking technology. Although this development is almost 5 years old, it is quite relevant, and when working on a website (studying web statistics) it is worth keeping it in mind.
In addition to creating its own page ranking algorithm, Microsoft points out the drawbacks of Google’s PageRank. For example, there is a loop hole in that people can increase the page rank of their website by creating link farms. These consist of pointless web pages that point to a website in order to increase its importance and appear higher in search results. Microsoft feels that this can lead to deceiving results and pages that are not relevant to the the search keywords. Instead, Microsoft argues that monitoring user behavior on websites is a better alternative. However, there is always room for improvement for both page ranking algorithms. It is even suggested that a combination of the two algorithms could prove to have better performance.
CLICK GRAPH INSTEAD OF REFERENCE GRAPH
The main difference between BrowseRank and PageRank is the graph structure.
User web surfing graph In PageRank technology, the nodes of the graph are documents, and the edges are links. In BrowseRank technology, edges are formed not by links, but by clicks (transitions). In addition, meta-information about the duration of the session within the document is stored.
TWO SIGNIFICANT ADVANTAGES OF BROWSERANK ARE OBVIOUS:
significantly better resistance to link spam,
taking into account the duration of the session allows you to evaluate the usefulness of the document for the visitor.
The behavioral graph more accurately reflects the web surfing process, and therefore it is more useful for calculating the importance of documents. More visits to a page and more time spent means more importance of the page.
THE PRIMARY SOURCE OF USER BEHAVIOR DATA IS BROWSER BARS
All leading search engines have plugins that monitor the activity of a large share of the audience.
Essentially, the data being collected can be represented by a record like a URL; TIME; TYPE [input | click]. There are two ways to navigate to a document: via a link from another document (click), or by typing the url in the browser address bar (input). Transition data retrieval mechanism:
Session segmentation.
A new session is initiated if there is a pause of 30 minutes or more from the moment of previous activity, or if the name of the site is entered in the address bar.
Formation of url pairs.
Within each session, pairs of urls are created from adjacent records. The url pair means that the transition was made using a link.
Formation of the initial distribution.
In each session, segmented by transition type, the first url is entered directly by the user. We consider such URLs to be “trusted” and call this traffic “green”. When processing data on user behavior, we consider transitions to these URLs to be a consequence of random distribution. Normalization by the frequency of visits to these documents gives the initial probabilities of visiting the corresponding pages.
Retrieving session duration.
For each pair of urls, the session duration of the first url is calculated by a simple date difference. If the url was the last one in the session, two options are possible. For time-segmented sessions, the duration of viewing the last url is calculated based on data on views of other pages. For sessions segmented by type, the time to view the last url is calculated based on the start time of the next session.
RESULTS OF USING BROWSERANK
Microsoft conducted two studies. The first was carried out at the site level to identify important sites and suppress spam. The second experiment was conducted at the document level to test BrowseRank to improve rankings.
Behavioral Graph ExponentThe first experiment used a dataset of approximately 3 billion records containing approximately 950 million unique URLs. It is interesting that the distribution of the number of views over viewing time is perfectly described by the classical exponential function. In this experiment, page-by-page data were not used; they were aggregated at the site level. The resulting behavioral graph consisted of 5.6 million nodes and 53 million edges.
Comparison of TOP-20 results when using different algorithms. An interesting sample is the TOP-20 sites obtained by ranking using three different algorithms. It can be seen that BrowseRank perfectly ranked MySpace, Youtube, Facebook and other sites that are characterized by long session times. Please note, these are 2008 data. Obviously, in this era of social networks and communication services, the difference between Pagerank and BrowseRank would be even more significant.
Using a random sample of 10 thousand sites, manually marked by assessors, it was shown that BrowserRank is more effective than TrustRank and PageRank in solving the problem of filtering spam sites.