Ranking contextual signals for search personalization

One or more computer-storage media storing computer-useable instructions that, when used by a computing device, cause the computing device to perform a method for using multiple contextual signals to re-rank search results, the method comprising:

• receiving a set of non-contextual search results based on a search query received from a user;
• receiving contextual signals that provide user-specific features associated with the user;
• utilizing a machine-learning model that uses an algorithm to compare each of the user-specific features associated with the user to determine an importance and a relevance of each of the user-specific features in relation to the search query or documents that comprise the set of non-contextual search results;
• re-ranking the documents in the set of non-contextual search results based on the comparison of the user-specific features to generate a set of contextual search results;
• receiving feedback from the user regarding the documents in the set of contextual search results;
• and
• communicating the feedback to the machine-learning model, wherein the machine-learning model utilizes the feedback in its comparison of each of the user-specific features in determining the importance and the relevance of the user-specific features in relation to future search queries received from the user and other users.

One or more computer-storage media storing computer-useable instructions that, when used by a computing device, cause the computing device to perform a method for using multiple contextual signals to re-rank search results, the method comprising:

• receiving a set of non-contextual search results based on a search query received from a user;
• receiving a plurality of contextual signals that provide user-specific features, the plurality of contextual signals including a score indicating a relevancy of each of the user-specific features;
• determining that each of the plurality of contextual signals meets a minimum threshold for being trustworthy in relation to the user and the search query;
• converting the scores associated with the user-specific features into a common numerical space to generate normalized scores for the user-specific features;
• adjusting the normalized scores based on one or more of,
• a level of trustworthiness associated with a contextual signal provider that provided a particular user-specific feature, or
• a position bias effect that indicates an effect of positioning a particular document in a certain position;
• and
• re-ranking documents in the set of non-contextual search results based on the adjusted normalized scores.