Microsoft Technology Licensing, LLC

Adish Singla

Ryen William White

Eric J Horvitz

Peter Bailey

Nikhil Dandekar

Jeff Huang

Zheng Chen

Ahmed Hassan Awadallah

Weizhu Chen

Semiha Ece Kamar Eden

Puneet Agrawal

Vijay Krishnan

Sree Hari Nagaralu

Search and browse trails are temporally-ordered sequences of web pages visited by a user during post-search query navigation beginning with a page associated with one of the search results. The trails can provide useful information for a number of search-related purposes. For example, these trails can be used to leverage the post-query behavior of other users to help the current user search more effectively and allow them to make more informed search interaction decisions. The trails can also be used to establish search results and refine search result rankings, select and evaluate deeplinks, and recommend multi-step trails as an alternative to or enhancement for existing search result presentation techniques.

Establishing search results and deeplinks using trails

A stochastic privacy service provider may provide users with a guaranteed upper bound on a probability that personal data will be accessed while enabling the services to collect data that can be used to enhance its services. Users may receive incentives to become participants in a stochastic privacy program. The stochastic privacy provider may employ one or more probabilistic and decision-theoretic methods to determine which participants' personal data should be sought while guaranteeing that the probability of personal data being accessed is smaller than the mutually agreed upon probability of access. The probability of access may be on a per time basis. The stochastic privacy provider may access coalescenses of the personal data of sets of multiple people, where a maximum probability is given for accessing statistical summaries of personal data computed from groups of people that are of at least some guaranteed size.

Stochastic privacy

The claimed subject matter includes a system and method for attribution of search activity in multi-user settings. The method includes training a classifier to distinguish between machines that are single-user and multi-user based on activity logs of an identified machine. The identified machine is determined to be multi-user based on the classifier. A number of users is estimated for the identified machine. Activity of the users is clustered based on the number of users estimated. A similarity function is learned for the number of users estimated. The method also includes assigning new activity to one of the users based on the clustering, and the similarity function.

Attribution of activity in multi-user settings

The techniques discussed herein consider a degree of noise associated with user clicks performed during search sessions. The techniques then generate a model that characterizes click noise so that search engines can more accurately infer document relevance.

Click noise characterization model

Systems, methods, and computer-readable storage media for identifying queries having a version-intent and presenting search results in accordance with the version-intent are provided. Subject-versions associated with retrievable documents are identified and associated therewith as subject-version tags. When a search query is received, it is determined whether the query has a version-intent indicative of a particular version associated with a subject of the query. Documents are retrieved that satisfy the input query and ranked for presentation based upon the particular subject-version indicated by the version-intent. The ranked documents are then presented. Also presented is a user-manipulatable tool, manipulation of which permits a user to indicate that information concerning a different subject-version is desired. Upon receiving such indication, the search results are changed such that documents having subject-version tags indicative of the newly-indicated version-intent are ranked higher and receive more prominent placement than documents lacking such subject-version tags.

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Seattle, WA, United States of America

Adish Singla