Post by account_disabled on Mar 7, 2024 5:23:08 GMT -5
Number of Longer Views by the Total Number of Views. Attempts to Manipulate Such Data May Not Work. 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 Doesnt Conform to This Model Their Click Data Can Be Disregarded. The Safeguards Can Be Designed to Accomplish Two Main Objectives Ensure Democracy in the Votes E.g. One Single Vote Per Cookie Andor Ip for a Given Queryurl Pair and Entirely Remove the Information Coming From Cookies or Ip Addresses.
That Do Not Look Natural in Their Browsing Behavior E.g. Click Durations Clicks_per_minutehourday Etc.. Suspicious Clicks Can Be Removed and the Click Signals for Queries That Appear to Be Spmed Need Not Be Used E.g. Queries for Which the Bahamas Mobile Number List Clicks Feature a Distribution of User Agents Cookie Ages Etc. That Do Not Look Normal. And Just Like Google Can Make a Matrix of Documents Queries They Could Also Choose to Put More Weight on Search Accounts Associated With Topical Expert Users Based on Their Historical Click Patterns. Moreover the Weighting Can Be Adjusted Based on the Determined Type of the User Both in Terms of How Click Duration is Translated Into Good Clicks Versus Notsogood Clicks and in Terms of How Much Weight to Give to the.
Good Clicks From a Particular User Group Versus Another User Group. Some Users Implicit Feedback May Be More Valuable Than Other Users Due to the Details of a Users Review Process. For Example a User That Almost Always Clicks on the Highest Ranked Result Can Have His Good Clicks Assigned 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 Classified Based on His or Her Query Stream. Users That Issue Many Queries on or Related to a Given Topic T E.g. Queries Related to Law Can Be Presumed to Have a High Degree of Expertise With Respect to the Given Topic T and Their Click Data Can Be Weighted Accordingly for .
That Do Not Look Natural in Their Browsing Behavior E.g. Click Durations Clicks_per_minutehourday Etc.. Suspicious Clicks Can Be Removed and the Click Signals for Queries That Appear to Be Spmed Need Not Be Used E.g. Queries for Which the Bahamas Mobile Number List Clicks Feature a Distribution of User Agents Cookie Ages Etc. That Do Not Look Normal. And Just Like Google Can Make a Matrix of Documents Queries They Could Also Choose to Put More Weight on Search Accounts Associated With Topical Expert Users Based on Their Historical Click Patterns. Moreover the Weighting Can Be Adjusted Based on the Determined Type of the User Both in Terms of How Click Duration is Translated Into Good Clicks Versus Notsogood Clicks and in Terms of How Much Weight to Give to the.
Good Clicks From a Particular User Group Versus Another User Group. Some Users Implicit Feedback May Be More Valuable Than Other Users Due to the Details of a Users Review Process. For Example a User That Almost Always Clicks on the Highest Ranked Result Can Have His Good Clicks Assigned 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 Classified Based on His or Her Query Stream. Users That Issue Many Queries on or Related to a Given Topic T E.g. Queries Related to Law Can Be Presumed to Have a High Degree of Expertise With Respect to the Given Topic T and Their Click Data Can Be Weighted Accordingly for .