Abstract: Techniques for statistically estimating a rating or other “figure of merit” for a user are disclosed. According to one such technique, a first quantity of submissions that were submitted by a user is determined. A second quantity of submissions that (a) were submitted by the user and (b) obtained a particular rating from a rating mechanism also is determined. A user rating for the user is determined based at least in part on the first quantity, the second quantity, and a factor that is independent of both the first quantity and the second quantity—such as the probability that an answer submitted by any answerer in a population will obtain the particular rating from the rating mechanism. The influence that the second quantity has on the user rating relative to the influence that the factor has on the user rating may depend at least in part on the first quantity.

Abstract: Techniques for statistically estimating a rating or other “figure of merit” for a user are disclosed. According to one such technique, a first quantity of submissions that were submitted by a user is determined. A second quantity of submissions that (a) were submitted by the user and (b) obtained a particular rating from a rating mechanism also is determined. A user rating for the user is determined based at least in part on the first quantity, the second quantity, and a factor that is independent of both the first quantity and the second quantity—such as the probability that an answer submitted by any answerer in a population will obtain the particular rating from the rating mechanism. The influence that the second quantity has on the user rating relative to the influence that the factor has on the user rating may depend at least in part on the first quantity.

Abstract: Techniques are described herein for generating and displaying a confusion matrix wherein a data item belonging to one or more actual classes is predicted into a class. The classes in which the data item may be predicted (the “predicted classes”) are ranked according to a score that in one embodiment indicates the confidence of the prediction. If the data item is predicted into a class that is one of the top K ranked predicted classes, then the prediction is considered accurate and an entry is created in a cell of a confusion matrix indicating the accurate prediction. If the data item is not predicted into a class that is not one of the top K ranked predicted classes, then the prediction is considered inaccurate and an entry is created in a cell of a confusion matrix indicating the inaccurate prediction.

Abstract: Techniques for statistically estimating a rating or other “figure of merit” for a user are disclosed. According to one such technique, a first quantity of submissions that were submitted by a user is determined. A second quantity of submissions that (a) were submitted by the user and (b) obtained a particular rating from a rating mechanism also is determined. A user rating for the user is determined based at least in part on the first quantity, the second quantity, and a factor that is independent of both the first quantity and the second quantity—such as the probability that an answer submitted by any answerer in a population will obtain the particular rating from the rating mechanism. The influence that the second quantity has on the user rating relative to the influence that the factor has on the user rating may depend at least in part on the first quantity.

Abstract: Techniques for statistically estimating a rating or other “figure of merit” for a user are disclosed. According to one such technique, a first quantity of submissions that were submitted by a user is determined. A second quantity of submissions that (a) were submitted by the user and (b) obtained a particular rating from a rating mechanism also is determined. A user rating for the user is determined based at least in part on the first quantity, the second quantity, and a factor that is independent of both the first quantity and the second quantity—such as the probability that an answer submitted by any answerer in a population will obtain the particular rating from the rating mechanism. The influence that the second quantity has on the user rating relative to the influence that the factor has on the user rating may depend at least in part on the first quantity.

Abstract: Techniques for statistically estimating a rating or other “figure of merit” for a user are disclosed. According to one such technique, a first quantity of submissions that were submitted by a user is determined. A second quantity of submissions that (a) were submitted by the user and (b) obtained a particular rating from a rating mechanism also is determined. A user rating for the user is determined based at least in part on the first quantity, the second quantity, and a factor that is independent of both the first quantity and the second quantity—such as the probability that an answer submitted by any answerer in a population will obtain the particular rating from the rating mechanism. The influence that the second quantity has on the user rating relative to the influence that the factor has on the user rating may depend at least in part on the first quantity.

Abstract: A system is described for targeting data to a site referenced on a page based on a condition. The system may include a processor, a memory, and an interface. The memory may be operatively connected to the processor and the interface and may store a data, a site, a condition, and a page containing content. The interface may be operatively connected to the memory and the processor and may communicate the page to a user. The processor may identify the data, site, condition, and page containing content. The processor may add the data to the page if the content of the page satisfies the condition.

Abstract: A system and method are disclosed for identifying a keyword that is a novel concept or anomaly based on prior search results for the keyword. Advertisements may be sold for the keyword, or the keyword may be purchased or recommended for purchase based on anticipation of increased future searches on the keyword.

Abstract: Techniques are described herein for generating and displaying a confusion matrix wherein a data item belonging to one or more actual classes is predicted into a class. The classes in which the data item may be predicted (the “predicted classes”) are ranked according to a score that in one embodiment indicates the confidence of the prediction. If the data item is predicted into a class that is one of the top K ranked predicted classes, then the prediction is considered accurate and an entry is created in a cell of a confusion matrix indicating the accurate prediction. If the data item is not predicted into a class that is not one of the top K ranked predicted classes, then the prediction is considered inaccurate and an entry is created in a cell of a confusion matrix indicating the inaccurate prediction.

Abstract: A system is described for targeting data to a site referenced on a page based on a condition. The system may include a processor, a memory, and an interface. The memory may be operatively connected to the processor and the interface and may store a data, a site, a condition, and a page containing content. The interface may be operatively connected to the memory and the processor and may communicate the page to a user. The processor may identify the data, site, condition, and page containing content. The processor may add the data to the page if the content of the page satisfies the condition.

Abstract: A system and method are disclosed for identifying a keyword that is a novel concept or anomaly based on prior search results for the keyword. Advertisements may be sold for the keyword, or the keyword may be purchased or recommended for purchase based on anticipation of increased future searches on the keyword.

Abstract: Techniques for statistically estimating a rating or other “figure of merit” for a user are disclosed. According to one such technique, a first quantity of submissions that were submitted by a user is determined. A second quantity of submissions that (a) were submitted by the user and (b) obtained a particular rating from a rating mechanism also is determined. A user rating for the user is determined based at least in part on the first quantity, the second quantity, and a factor that is independent of both the first quantity and the second quantity—such as the probability that an answer submitted by any answerer in a population will obtain the particular rating from the rating mechanism. The influence that the second quantity has on the user rating relative to the influence that the factor has on the user rating may depend at least in part on the first quantity.

Abstract: Techniques are described for assigning, to target categories of a target scheme, items that have been obtained from a plurality of sources. In situations in which one or more of the sources has organized its information according to a source scheme that differs from the target scheme, the assignment may be based, in part, on an estimate of the probability that items from a particular source category should be assigned to a particular target category. Such probability estimates may be based on how many training set items associated with the particular source category have been assigned to the particular target category. Source categories may be grouped into clusters. The probability estimates may also be based on how many training set items within the cluster to which the particular source category has been mapped, have been assigned the particular target category.