Abstract: Method, system, and programs for measuring user engagement with content items. In one example, a query is received. A set of content items related to the query is obtained. A presentation of at least one content item of the set of content items is provided on a user interface. A user activity related to the at least one content item is determined. An amount of time between a time at which the presentation of the at least one content item is provided on the user interface and a time at which the user activity occurred is determined. A score associated with the content item is determined based on the amount of time. Information related to user engagement with the set of content items is generated based on the score.

Abstract: Method, system, and programs for measuring user engagement with content items. In one example, a query is received. A set of content items related to the query is obtained. A presentation of at least one content item of the set of content items is provided on a user interface. A user activity related to the at least one content item is determined. An amount of time between a time at which the presentation of the at least one content item is provided on the user interface and a time at which the user activity occurred is determined. A score associated with the content item is determined based on the amount of time. Information related to user engagement with the set of content items is generated based on the score.

Abstract: One or more systems and/or methods of generating a query-goal-mission structure for a set of queries are provided. A set of queries may be evaluated to identify query information for the queries within the set of queries. The queries may be evaluated as query pairs to determine common goal probabilities (e.g., likelihood two queries correspond to a particular goal, such as to identify vacation planning information) for the query pairs. Responsive to the common goal probabilities for the query pairs exceeding a goal probability threshold, the query pairs may be grouped into goal clusters. The goal clusters may be evaluated as goal cluster pairs to determine common mission probabilities. Responsive to the common mission probabilities for the goal cluster pairs exceeding a mission probability threshold, the goal clusters may be grouped into mission clusters. The mission clusters and the goal clusters may be utilized to generate a query-goal-mission structure.

Abstract: Method, system, and programs for measuring user engagement with content items. In one example, a query is received. A presentation of a content item related to the query is provided on a user interface. A user activity (e.g., related to manipulation of the content item) is determined. A score associated with the content item is determined based on the user activity. Information related to user engagement with the content item is generated based on the score.

Abstract: One or more client devices, systems, and/or methods of improving mobile searches are provided. A feature (e.g., “snow boots”), generated on a client device, is identified. The feature is evaluated to identify a goal (e.g., finding snow boots) associated with the feature. A mission (e.g., finding outdoor apparel), associated with the goal, is identified. A query (e.g., “outdoor stores near Akron”) associated with a second goal (e.g., find outdoor apparel store) associated with the mission is identified using a model generated utilizing a machine learning method trained using a query-goal-mission structure. A query recommendation (e.g., “outdoor apparel store”) comprising the query is presented to the user. A user satisfaction metric, associated with the query, the query recommendation, a result generated by the query recommendation, etc. is determined based upon user interaction with the query recommendation and the result. The model may be tuned based upon the user satisfaction metric.

Abstract: Method, system, and programs for measuring user engagement with content items. In one example, a query is received. A set of content items related to the query is obtained. A presentation of at least one content item of the set of content items is provided on a user interface. A user activity related to the at least one content item is determined. An amount of time between a time at which the presentation of the at least one content item is provided on the user interface and a time at which the user activity occurred is determined. A score associated with the content item is determined based on the amount of time. Information related to user engagement with the set of content items is generated based on the score.

Abstract: Methods, systems and programming for evaluating user satisfaction with respect to a user session are presented. In one example, one or more queries in a use session are received from a user. Information about one or more user activities is obtained. Each user activity is related to manipulation of a content item associated with one of the one or more queries. A score associated with the user session is computed based at least partially on the one or more user activities. User satisfaction with respect to the user session is determined based on the score.

Abstract: Method, system, and programs for measuring user engagement with content items. In one example, a query is received. A set of content items related to the query is obtained. A presentation of at least one content item of the set of content items is provided on a user interface. A user activity related to the at least one content item is determined. An amount of time between a time at which the presentation of the at least one content item is provided on the user interface and a time at which the user activity occurred is determined. A score associated with the content item is determined based on the amount of time. Information related to user engagement with the set of content items is generated based on the score.

Abstract: One or more systems and/or methods of generating a query-goal-mission structure for a set of queries are provided. A set of queries may be evaluated to identify query information for the queries within the set of queries. The queries may be evaluated as query pairs to determine common goal probabilities (e.g., likelihood two queries correspond to a particular goal, such as to identify vacation planning information) for the query pairs. Responsive to the common goal probabilities for the query pairs exceeding a goal probability threshold, the query pairs may be grouped into goal clusters. The goal clusters may be evaluated as goal cluster pairs to determine common mission probabilities. Responsive to the common mission probabilities for the goal cluster pairs exceeding a mission probability threshold, the goal clusters may be grouped into mission clusters. The mission clusters and the goal clusters may be utilized to generate a query-goal-mission structure.

Abstract: One or more systems and/or methods of generating a query-goal-mission structure for a set of queries are provided. A set of queries may be evaluated to identify query information for the queries within the set of queries. The queries may be evaluated as query pairs to determine common goal probabilities (e.g., likelihood two queries correspond to a particular goal, such as to identify vacation planning information) for the query pairs. Responsive to the common goal probabilities for the query pairs exceeding a goal probability threshold, the query pairs may be grouped into goal clusters. The goal clusters may be evaluated as goal cluster pairs to determine common mission probabilities. Responsive to the common mission probabilities for the goal cluster pairs exceeding a mission probability threshold, the goal clusters may be grouped into mission clusters. The mission clusters and the goal clusters may be utilized to generate a query-goal-mission structure.

Abstract: The present teaching relates to method, system, and programs for training a ranking model for ranking content items. In one example, a set of content items is obtained. A plurality types of online user activities performed with respect to the set of content items are obtained. For each of the set of content items, a plurality of user engagement scores are determined. Each of the plurality of user engagement scores is determined based on a corresponding one of the plurality types of online user activities. For each of the set of content items, an aggregated score is calculated based on the plurality of user engagement scores to generate aggregated scores. A ranking model is trained based on the aggregated scores.

Abstract: One or more client devices, systems, and/or methods of improving mobile searches are provided. A feature (e.g., “snow boots”), generated on a client device, is identified. The feature is evaluated to identify a goal (e.g., finding snow boots) associated with the feature. A mission (e.g., finding outdoor apparel), associated with the goal, is identified. A query (e.g., “outdoor stores near Akron”) associated with a second goal (e.g., find outdoor apparel store) associated with the mission is identified using a model generated utilizing a machine learning method trained using a query-goal-mission structure. A query recommendation (e.g., “outdoor apparel store”) comprising the query is presented to the user. A user satisfaction metric, associated with the query, the query recommendation, a result generated by the query recommendation, etc. is determined based upon user interaction with the query recommendation and the result. The model may be tuned based upon the user satisfaction metric.

Abstract: One or more systems and/or methods of generating a query-goal-mission structure for a set of queries are provided. A set of queries may be evaluated to identify query information for the queries within the set of queries. The queries may be evaluated as query pairs to determine common goal probabilities (e.g., likelihood two queries correspond to a particular goal, such as to identify vacation planning information) for the query pairs. Responsive to the common goal probabilities for the query pairs exceeding a goal probability threshold, the query pairs may be grouped into goal clusters. The goal clusters may be evaluated as goal cluster pairs to determine common mission probabilities. Responsive to the common mission probabilities for the goal cluster pairs exceeding a mission probability threshold, the goal clusters may be grouped into mission clusters. The mission clusters and the goal clusters may be utilized to generate a query-goal-mission structure.

Abstract: Methods, systems and programming for evaluating query suggestions quality. In one example, a plurality of query suggestions are provided in a ranking to a user. A user activity with respect to one of the plurality of query suggestions is detected. A position of the one of the plurality of query suggestions in the ranking is determined. A quality measure of the plurality of query suggestions is calculated based, at least in part, on the user activity and the position of the one of the plurality of query suggestions.

Abstract: Method, system, and programs for measuring user engagement with content items. In one example, a query is received. A set of content items related to the query is obtained. A presentation of at least one content item of the set of content items is provided on a user interface. A user activity related to the at least one content item is determined. An amount of time between a time at which the presentation of the at least one content item is provided on the user interface and a time at which the user activity occurred is determined. A score associated with the content item is determined based on the amount of time. Information related to user engagement with the set of content items is generated based on the score.

Abstract: Method, system, and programs for measuring user engagement with content items. In one example, a query is received. A presentation of a content item related to the query is provided on a user interface. A user activity (e.g., related to manipulation of the content item) is determined. A score associated with the content item is determined based on the user activity. Information related to user engagement with the content item is generated based on the score.

Abstract: Methods, systems and programming for evaluating user satisfaction with respect to a user session are presented. In one example, one or more queries in a use session are received from a user. Information about one or more user activities is obtained. Each user activity is related to manipulation of a content item associated with one of the one or more queries. A score associated with the user session is computed based at least partially on the one or more user activities. User satisfaction with respect to the user session is determined based on the score.

Abstract: Methods, systems and programming for evaluating query suggestions quality. In one example, a plurality of query suggestions are provided in a ranking to a user. A user activity with respect to one of the plurality of query suggestions is detected. A position of the one of the plurality of query suggestions in the ranking is determined. A quality measure of the plurality of query suggestions is calculated based, at least in part, on the user activity and the position of the one of the plurality of query suggestions.

Abstract: Method, system, and programs for identifying a target metric. In one example, at least one first type of metric computed based on a first period associated with a first length of time is measured for each of a plurality of users. At least one second type of metric computed based on a second period associated with a second length of time is measured for each of the plurality of users. The second length of time is larger than the first length of time. Correlations between each of the at least one first type of metric and each of the at least one second type of metrics are computed with respect to the plurality of users. A target metric is identified from the at least one first type of metric based on the correlations. The target metric is correlated with the at least one second type of metric.

Abstract: Method, system, and programs for measuring user engagement. In one example, a type of user activities with respect to a plurality pieces of content in a content stream is detected. A first depth measure with respect to the content stream is obtained based on the detected type of user activities. A function is generated based on the first depth measure and a second depth measure with respect to the content stream. A user engagement score is calculated for each of the plurality pieces of content in the content stream based on the function.