1. A machine-implemented method comprising: receiving a request for keyword suggestions, the request including a seed keyword with which the keyword suggestions are to be generated; accepting, from two or more keyword suggestion tools, at least two heterogeneous sets of keyword suggestions for an online advertisement, wherein each set of keyword suggestions includes targeting keyword suggestions that are ranked and scored by a keyword suggestion tool that suggested the set of keyword suggestions, and wherein targeting keyword suggestions in each set of targeting keyword suggestions have been generated based on the seed keyword; for each heterogeneous sets of keyword suggestions accepted from the at least two or more keyword suggestion tools, determining, by one or more processors, a new normalized score for each of the targeting keyword suggestions in the heterogeneous set of keyword suggestions, wherein the new normalized score is computed based on a cardinal aspect of the targeting keyword suggestion and an ordinal aspect of the targeting keyword suggestion in the heterogeneous set of keyword suggestions, the cardinal aspect representing an absolute score corresponding to the targeting keyword suggestion and the ordinal aspect representing a rank of the targeting keyword suggestion in the heterogeneous set of keyword suggestions, and wherein the new normalized score for each targeting keyword suggestion in a particular set of heterogeneous keyword suggestions is defined as a sum of a first weight multiplied by the cardinal aspect and a second weight multiplied by the ordinal aspect; generating, by the one or more processors, an adjusted new score for each targeting keyword suggestion based on a result of a function of a new normalized score corresponding to the targeting keyword suggestion and trust factor of a keyword suggestion tool from which the targeting keyword was accepted, the trust factor representing a measure of reliability of the keyword suggestion tool; combining, by the one or more processors, the targeting keyword suggestions scored by a first keyword suggestion tool selected from the at least two or more keyword suggestion tools and the targeting keyword suggestions scored by a second suggestion tool selected from the at least two or more keyword suggestion tools using the new scores to generate a combined set of ordered and scored suggestions according to the adjusted new score for each targeting keyword suggestion; and providing the combined set of keyword suggestions to a user device.
1. A machine-implemented method comprising: receiving a request for keyword suggestions, the request including a seed keyword with which the keyword suggestions are to be generated; accepting, from two or more keyword suggestion tools, at least two heterogeneous sets of keyword suggestions for an online advertisement, wherein each set of keyword suggestions includes targeting keyword suggestions that are ranked and scored by a keyword suggestion tool that suggested the set of keyword suggestions, and wherein targeting keyword suggestions in each set of targeting keyword suggestions have been generated based on the seed keyword; for each heterogeneous sets of keyword suggestions accepted from the at least two or more keyword suggestion tools, determining, by one or more processors, a new normalized score for each of the targeting keyword suggestions in the heterogeneous set of keyword suggestions, wherein the new normalized score is computed based on a cardinal aspect of the targeting keyword suggestion and an ordinal aspect of the targeting keyword suggestion in the heterogeneous set of keyword suggestions, the cardinal aspect representing an absolute score corresponding to the targeting keyword suggestion and the ordinal aspect representing a rank of the targeting keyword suggestion in the heterogeneous set of keyword suggestions, and wherein the new normalized score for each targeting keyword suggestion in a particular set of heterogeneous keyword suggestions is defined as a sum of a first weight multiplied by the cardinal aspect and a second weight multiplied by the ordinal aspect; generating, by the one or more processors, an adjusted new score for each targeting keyword suggestion based on a result of a function of a new normalized score corresponding to the targeting keyword suggestion and trust factor of a keyword suggestion tool from which the targeting keyword was accepted, the trust factor representing a measure of reliability of the keyword suggestion tool; combining, by the one or more processors, the targeting keyword suggestions scored by a first keyword suggestion tool selected from the at least two or more keyword suggestion tools and the targeting keyword suggestions scored by a second suggestion tool selected from the at least two or more keyword suggestion tools using the new scores to generate a combined set of ordered and scored suggestions according to the adjusted new score for each targeting keyword suggestion; and providing the combined set of keyword suggestions to a user device. 2. The machine-implemented method of claim 1 wherein the ordinal aspect of a ranked targeting keyword suggestion is defined as: 1 rank targeting ⁢ ⁢ keyword ⁢ ⁢ suggestion ∑ for ⁢ ⁢ all ⁢ ⁢ targeting ⁢ ⁢ keyword ⁢ ⁢ suggestions ⁢ ⁢ of ⁢ ⁢ the ⁢ ⁢ heterogeneous ⁢ ⁢ set j ⁢ ⁢ 1 / rank targeting ⁢ ⁢ keyword ⁢ ⁢ suggestion j ⁢ ⁢ .