Abstract: A system of content/query feature based data structure retrieval of predicted values is provided. The system can create a data structure having a plurality of rows corresponding to individual content/query features and a plurality of columns corresponding to individual predicted values. The processors can obtain a set of session features associated with a selection by a computing device in response to a query, and a set of content/query features associated with the selection of the content item. The processors can retrieve, from the data structure, a set of predicted values for each of the set of content/query features. The processors can generate, for each of the set of content/query features, a set of aggregate predicted values for each of the set of content/query features, and can include the set of aggregate predicted values in the data structure.

Abstract: A system of content/query feature based data structure retrieval of predicted values is provided. The system can create a data structure having a plurality of rows corresponding to individual content/query features and a plurality of columns corresponding to individual predicted values. The processors can obtain a set of session features associated with a selection by a computing device in response to a query, and a set of content/query features associated with the selection of the content item. The processors can retrieve, from the data structure, a set of predicted values for each of the set of content/query features. The processors can generate, for each of the set of content/query features, a set of aggregate predicted values for each of the set of content/query features, and can include the set of aggregate predicted values in the data structure.

Abstract: User queries are received, with each query requesting a service from a server. Overlapping experiments are performed on at least a portion of the queries, with each experiment modifying one or more parameters associated with the queries or parameters associated with processing of the queries, and with the experiments organized into layers. Two or more experiments in different layers are allowed to be performed on the same query, and for any given layer, at most one experiment is allowed to be performed on the same query.

Abstract: A system obtains a first parameter (QP1) associated with a quality of an advertisement among multiple advertisements, where the first quality parameter (QP1) does not include a click through rate (CTR). The system functionally combines the first quality parameter (QP1) with at least one other parameter and uses the functional combination to filter, rank or promote the advertisement among the multiple advertisements.

Abstract: User queries are received, with each query requesting a service from a server. Overlapping experiments are performed on at least a portion of the queries, with each experiment modifying one or more parameters associated with the queries or parameters associated with processing of the queries, and with the experiments organized into layers. Two or more experiments in different layers are allowed to be performed on the same query, and for any given layer, at most one experiment is allowed to be performed on the same query.

Abstract: User queries are received, with each query requesting a service from a server. Overlapping experiments are performed on at least a portion of the queries, with each experiment modifying one or more parameters associated with the queries or parameters associated with processing of the queries, and with the experiments organized into layers. Two or more experiments in different layers are allowed to be performed on the same query, and for any given layer, at most one experiment is allowed to be performed on the same query.

Abstract: A system obtains ratings associated with a first set of advertisements hosted by one or more servers, where the ratings indicate a quality of the first set of advertisements. The system observes multiple different first user actions associated with user selection of advertisements of the first set of advertisements and derives a statistical model using the observed first user actions and the obtained ratings. The system further observes second user actions associated with user selection of a second advertisement hosted by the one or more servers and uses the statistical model and the second user actions to estimate a quality of the second advertisement.

Abstract: Methods, systems, and apparatuses for online monitoring systems to determine offline advertising effectiveness. Information related to an advertisement for a product is received. The advertisement is presented through offline media. The information includes an advertising schedule. Communications related to the product that are received from the consumers in the market at the time the advertisement is presented, are detected. Data responsive to the detected communications are collected; the data represent a number of consumers in the market. A statistical correlation between the collected data and the advertising schedule is determined and provided to the advertiser as a report of the effectiveness of the advertising.

Abstract: A system obtains a first parameter (QP1) associated with a quality of an advertisement among multiple advertisements, where the first quality parameter (QP1) does not include a click through rate (CTR). The system functionally combines the first quality parameter (QP1) with at least one other parameter and uses the functional combination to filter, rank or promote the advertisement among the multiple advertisements.

Abstract: A system obtains a first parameter (QP1) associated with a quality of an advertisement among multiple advertisements, where the first quality parameter (QP1) does not include a click through rate (CTR). The system functionally combines the first quality parameter (QP1) with at least one other parameter and uses the functional combination to filter, rank or promote the advertisement among the multiple advertisements.

Abstract: A system obtains a first parameter (QP1) associated with a quality of an advertisement among multiple advertisements, where the first quality parameter (QP1) does not include a click through rate (CTR). The system functionally combines the first quality parameter (QP1) with at least one other parameter and uses the functional combination to filter, rank or promote the advertisement among the multiple advertisements.

Abstract: A system obtains ratings associated with a first set of advertisements hosted by one or more servers, where the ratings indicate a quality of the first set of advertisements. The system observes multiple different first user actions associated with user selection of advertisements of the first set of advertisements and derives a statistical model using the observed first user actions and the obtained ratings. The system further observes second user actions associated with user selection of a second advertisement hosted by the one or more servers and uses the statistical model and the second user actions to estimate a quality of the second advertisement.

Abstract: A system provides one or more advertisements to users in response to search queries and logs user behavior associated with user selection of the one or more advertisements. The system also logs features associated with selected ones of the one or more advertisements, or associated with the search queries. The system further uses a statistical model and the logged user behavior to estimate quality scores associated with the selected advertisements and aggregates the estimated quality scores. The system predicts the quality of another advertisement using the aggregated quality scores.

Abstract: Apparatus and method for summarizing an original large data set with a representative data set. The data elements in both the original data set and the representative data set have the same variables, but there are significantly fewer data elements in the representative data set. Each data element in the representative data set has an associated weight, representing the degree of compression. There are three steps for constructing the representative data set. First, the original data elements are partitioned into separate bins. Second, moments of the data elements partitioned in each bin are calculated. Finally, the representative data set is generated by finding data elements and associated weights having substantially the same moments as the original data set.

Abstract: A method provides for mining information from large volumes of data regarding transactions. The method provides for inferring a behavioral characteristic of a party to the transaction based on a large volume of data concerning a multitude of parties. That inferred characteristic may be dynamic in nature.

Abstract: A cluster interface is generated which represents similarities of semantics between segments of code with respect to both the physical constructs of the code and the underlying operations performed by the code. The generated interface represents one or more code segments. Code segments to be analyzed are received by a computer system. Statistical internal information is extracted from each code segment. An external metric is generated which is based on the extracted statistical information. An interface display is created from the external metric which represents similarity relationships between the inputted code segments based on the extracted statistical information.