Auto-detection of historical search context
First Claim
1. A volatile memory, non-volatile memory, optical disk, or hard drive storing computer-executable instructions which, when executed by a computer, cause the computer to perform acts comprising:
- representing historical user behaviors of a user using a set of features, the set of features including click features and query features;
training a classifier using training data comprising values for the features to identify a plurality of historical search contexts, wherein the plurality of historical search contexts include;
a first historical search context having first values for the features, wherein the first values include;
first click feature values for the click features, the first click feature values representing multiple first clicks entered by the user on multiple different first uniform resource locators (URLs) as part of the first historical search context, andfirst query feature values for the query features, the first query feature values representing multiple different first queries entered by the user as part of the first historical search context,a second historical search context having second values for the features, wherein the second values include;
second click feature values for the click features, the second click feature values representing multiple second clicks entered by the user on multiple different second URLs as part of the second historical search context, andsecond query feature values for the query features, the second query feature values representing multiple different second queries entered by the user as part of the second historical search context;
representing current user behavior during a current user session using third values for the features, wherein the third values include current click feature values for the click features and current query feature values for the query features, the current click feature values representing multiple current clicks entered by the user on multiple different current URLs during a current session and the current query feature values representing multiple different current queries entered by the user during the current session;
during the current user session, determining that the current user behavior is relatively more similar to the first historical search context than the second historical search context, wherein the determining comprises;
using the first click feature values, the second click feature values, and the current click feature values to analyze similarity of the multiple different current URLs clicked by the user during the current session to the multiple different first URLs clicked by the user as part of the first historical search context and the multiple different second URLs clicked by the user as part of the second historical search context, andusing the first query feature values, the second query feature values, and the current query feature values to analyze similarity of the multiple current queries entered by the user during the current session to the multiple different first queries entered by the user as part of the first historical search context and the multiple different second queries entered by the user as part of the second historical search context; and
surfacing the first historical search context as part of the current session for the user.
3 Assignments
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Accused Products
Abstract
Architecture that automatically detects historical search contexts as well as behaviors related to a search query. Machine learning and hand-authored rules are employed to automatically identify search contexts. Historical information likely to be useful in the current context is surfaced. When a user enters a search query or executes another search behavior, past behaviors are exposed which are contextually related to the current behavior. The architecture also provides automatic discovery of historical contexts, features related to the contexts, and training or authoring of a system for classifying behavior into contexts, using some combination of the machine learning and/or hand-authored rules. A runtime system classifies the current user behavior into a context and surfaces contextual information to the user.
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Citations
20 Claims
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1. A volatile memory, non-volatile memory, optical disk, or hard drive storing computer-executable instructions which, when executed by a computer, cause the computer to perform acts comprising:
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representing historical user behaviors of a user using a set of features, the set of features including click features and query features; training a classifier using training data comprising values for the features to identify a plurality of historical search contexts, wherein the plurality of historical search contexts include; a first historical search context having first values for the features, wherein the first values include; first click feature values for the click features, the first click feature values representing multiple first clicks entered by the user on multiple different first uniform resource locators (URLs) as part of the first historical search context, and first query feature values for the query features, the first query feature values representing multiple different first queries entered by the user as part of the first historical search context, a second historical search context having second values for the features, wherein the second values include; second click feature values for the click features, the second click feature values representing multiple second clicks entered by the user on multiple different second URLs as part of the second historical search context, and second query feature values for the query features, the second query feature values representing multiple different second queries entered by the user as part of the second historical search context; representing current user behavior during a current user session using third values for the features, wherein the third values include current click feature values for the click features and current query feature values for the query features, the current click feature values representing multiple current clicks entered by the user on multiple different current URLs during a current session and the current query feature values representing multiple different current queries entered by the user during the current session; during the current user session, determining that the current user behavior is relatively more similar to the first historical search context than the second historical search context, wherein the determining comprises; using the first click feature values, the second click feature values, and the current click feature values to analyze similarity of the multiple different current URLs clicked by the user during the current session to the multiple different first URLs clicked by the user as part of the first historical search context and the multiple different second URLs clicked by the user as part of the second historical search context, and using the first query feature values, the second query feature values, and the current query feature values to analyze similarity of the multiple current queries entered by the user during the current session to the multiple different first queries entered by the user as part of the first historical search context and the multiple different second queries entered by the user as part of the second historical search context; and surfacing the first historical search context as part of the current session for the user. - View Dependent Claims (4, 5, 6, 7, 8, 9, 10, 11, 12)
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2. A method performed by at least one computing device, the method comprising:
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receiving a current search query from a user during a current user session having associated current user behavior; analyzing a search history for one or more historical search contexts by applying similarity analysis to the current search query and historical queries in the search history; detecting an individual historical search context to use as a current search context by relating tagged historical user behaviors associated with the individual historical search context to the current user behavior that is associated with the current user session; presenting the current search context for user interaction, including presenting individual historical queries from the detected individual historical search context; and responsive to the user hovering over a first one of the individual historical queries for the detected individual historical search context, presenting individual URLs that were historically clicked on for the first individual historical query that is being hovered over. - View Dependent Claims (3)
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13. A system comprising:
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at least one processing unit; and one or more physical storage media storing computer-executable instructions which, when executed by the at least one processing unit, cause the at least one processing unit to; represent historical user behaviors of a user using a set of features, the set of features including click features and query features; train a classifier using training data comprising values for the features to identify a plurality of historical search contexts, wherein the plurality of historical search contexts include; a first historical search context having first values for the features, wherein the first values include; first click feature values for the click features, the first click feature values representing multiple first uniform resource locators (URLs) clicked on by the user as part of the first historical search context, and first query feature values for the query features, the first query feature values representing multiple first queries entered by the user as part of the first historical search context, a second historical search context having second values for the features, wherein the second values include; second click feature values for the click features, the second click feature values representing multiple second URLs clicked on by the user as part of the second historical search context, and second query feature values for the query features, the second query feature values representing multiple second queries entered by the user as part of the second historical search context; represent current user behavior during a current user session using third values for the features, wherein the third values include current click feature values for the click features and current query feature values for the query features, the current click feature values representing multiple current URLs clicked on by the user during a current session and the current query feature values representing multiple current queries entered by the user during the current session; during the current user session, determine that the current user behavior is relatively more similar to the first historical search context than the second historical search context, wherein the determining comprises analyzing similarity of; the current click feature values to the first click feature values and the second click feature values, and the current query feature values to the first query feature values and the second query feature values; and surface the first historical search context as part of the current session for the user. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
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Specification