Results including the document result
for a new search corresponding to the search query. The first number can include a number of the longer views of the document result, the second number can include a total number of views of the document result, and the determining can include dividing the number of longer views by the total number of views.
Attempts to manipulate such data
safeguards against spammers (users who generate fraudulent clicks in an attempt to boost certain search results) can be taken to help ensure that the user selection data is meaningful, even when very little data is available for a given (rare) query.
These safeguards can include
employing a user model that describes how a user should behave over time, and if a user doesn’t conform to this model, their click data can be disregarde. The Telemarketing for Mortgage Leads safeguards can be designe to accomplish two main objectives: (1) ensure democracy in the votes (e.g., one single vote per cookie and/or IP for a given query-URL pair), and (2) entirely remove the information
Coming from cookies or addresses
that do not look natural in their browsing behavior (e.g., abnormal distribution of click positions, click durations, clicks_per_minute/hour/day, etc.). Suspicious clicks can be remove, and the click signals for queries that appear to be spme nee not be use (e.g., queries for which the clicks feature a distribution of user agents, cookie ages, etc. that do not look normal).
Just like Google can make a matrix
Of documents & queries, they could also choose to put more weight on search accounts associate with topical expert users base on their historical click patterns.
Moreover, the weighting can be adjuste base on the determine type of the user both in terms of how click duration is translate into good clicks versus What happens to the comments not-so-good clicks, and in terms of how much weight to give to the good clicks from a
Particular user group versus another
user group. Some user’s implicit feeback may be more valuable than other users due to the details of a user’s review process. For example, a user that almost always clicks on the highest ranke result can have his good clicks assigne lower weights than a user who more often clicks results lower in the ranking first (since the second user is likely more.
Discriminating in his assessment
of what constitutes a good result). In addition, a user can be classifie baseon his or her query stream. Users that issue many queries on (or relate to) a given topic T (e.g., queries relate to law) can be presume to have a high degree of expertise with respect to the given topic T, and their click data can be weighte accordingly for other queries by them on (or relate to) the given topic T.
Whenever SEOs mention using
click data to search engineers, the search engineers quickly respond about how they might consider any signal but clicks would be a noisy signal. But if a signal has noise an engineer would work around the noise by finding ways to filter the noise out or combine multiple signals. to filter noise from the link graph: “We continue to protect the value of authoritative and relevant links as an important ranking signal for Search.”
