A method of recognizing artist ambiguity is performed at a server system having one or more processors and memory storing one or more programs for execution by the one or more processors. The method includes generating a feature vector that represents a first artist identifier of a plurality of artist identifiers in a first dataset. The feature vector includes a first feature indicating whether the first artist identifier matches multiple artist entries in one or more second datasets that are distinct from the first dataset. The method also includes determining, based at least in part on the first feature of the feature vector, a probability that the first artist identifier is associated with two or more different real-world artists, and providing a report that specifies the first artist identifier as potentially ambiguous in accordance with a determination that the probability satisfies a predetermined condition.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. (canceled) 2. A method of recognizing artist ambiguity, comprising: at a server system having one or more processors and memory storing one or more programs for execution by the one or more processors: generating a feature vector that represents a first artist identifier of a plurality of artist identifiers in a first dataset, the feature vector including a first feature indicating whether the first artist identifier matches multiple artist entries in one or more second datasets that are distinct from the first dataset; determining, based at least in part on the first feature of the feature vector, a probability that the first artist identifier is associated with two or more different real-world artists; and in accordance with a determination that the probability satisfies a predetermined condition, providing a report that specifies the first artist identifier as potentially ambiguous. 3. The method of claim 2, wherein the first artist identifier uniquely identifies a real-world artist in the first dataset. 4. The method of claim 2, wherein: the feature vector includes a second feature, distinct from the first feature, that indicates whether the first artist identifier matches multiple artist entries in one or more third datasets that are distinct from the first dataset and the one or more second datasets; and the probability that the first artist identifier is associated with two or more different real-world artists is determined based at least in part on the first feature and the second feature. 5. The method of claim 2, further comprising, at the server system: after determining that the probability satisfies the predetermined condition, performing a disambiguating routine for the first artist identifier. 6. The method of claim 5, wherein performing the disambiguating routine comprises: creating a new artist identifier; and associating at least a portion of media items that were previously associated with the first artist identifier with the new artist identifier. 7. The method of claim 6, further comprising, at the server system: receiving a user input identifying the first artist identifier as ambiguous; wherein the disambiguating routine is performed in response to the user input identifying the first artist identifier as ambiguous. 8. The method of claim 6, further comprising identifying the at least a portion of media items to be associated with the new artist identifier based at least in part on information from a dataset that is distinct from the first dataset. 9. The method of claim 2, wherein the report is provided to a user, the method further comprising, at the server system: after providing the report to the user, receiving a new artist identifier from the user; and associating at least a portion of media items that were previously associated with the first artist identifier with the new artist identifier. 10. The method of claim 2, wherein the first feature is an integer value that indicates a total number of the artist entries in the one or more second datasets that match the first artist identifier. 11. The method of claim 2, wherein: the feature vector includes a second feature selected from the group consisting of: (i) an indicator of whether a number of countries of registration of media items associated with the first artist identifier satisfies a country threshold; (ii) an indicator of whether a number of characters in the first artist identifier satisfies a character threshold; (iii) an indicator of whether a number of record labels associated with the first artist identifier satisfies a label threshold; (iv) an indicator of whether the first artist identifier is associated with albums in at least two different languages; and (v) an indicator of whether a difference between an earliest release date and a latest release date of media items associated with the first artist identifier satisfies a time-span threshold; and the probability that the first artist identifier is associated with two or more different real-world artists is determined based at least in part on the first feature and the second feature. 12. The method of claim 11, wherein the first feature and the second feature are binary values. 13. The method of claim 11, wherein: the second feature is the indicator of whether the number of countries of registration of media items associated with the first artist identifier satisfies the country threshold; and the method further comprises, at the server system, determining the country threshold based on artist metadata from a training dataset that includes a first set of artist identifiers that are known to be unambiguous and a second set of artist identifiers that are known to be ambiguous. 14. The method of claim 11, wherein: the second feature is the indicator of whether the number of characters in the first artist identifier exceeds the character threshold; and the method further comprises, at the server system, determining the character threshold based on artist metadata from a training dataset that includes a first set of artist identifiers that are known to be unambiguous and a second set of artist identifiers that are known to be ambiguous. 15. The method of claim 11, wherein: the second feature is the indicator of whether the number of record labels associated with the first artist identifier exceeds the label threshold; and the method further comprises, at the server system, determining the label threshold based on artist metadata from a training dataset that includes a first set of artist identifiers that are known to be unambiguous and a second set of artist identifiers that are known to be ambiguous. 16. The method of claim 11, wherein: the second feature is the indicator of whether a difference between an earliest release date and a latest release date of media items associated with the first artist identifier exceeds a time-span threshold; and the method further comprises, at the server system, determining the time-span threshold based on artist metadata from a training dataset that includes a first set of artist identifiers that are known to be unambiguous and a second set of artist identifiers that are known to be ambiguous. 17. The method of claim 2, wherein determining the probability includes applying a statistical classifier to the feature vector to calculate the probability. 18. The method of claim 17, wherein the statistical classifier is a naïve Bayes classifier. 19. The method of claim 17, wherein the statistical classifier is a logistic regression classifier. 20. A server system comprising: one or more processors; and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for: generating a feature vector that represents a first artist identifier of a plurality of artist identifiers in a first dataset, the feature vector including a first feature indicating whether the first artist identifier matches multiple artist entries in one or more second datasets that are distinct from the first dataset; determining, based at least in part on the first feature of the feature vector, a probability that the first artist identifier is associated with two or more different real-world artists; and in accordance with a determination that the probability satisfies a predetermined condition, providing a report that specifies the first artist identifier as potentially ambiguous. 21. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions which, when executed by a server system with one or more processors, cause the server system to: generate a feature vector that represents a first artist identifier of a plurality of artist identifiers in a first dataset, the feature vector including a first feature indicating whether the first artist identifier matches multiple artist entries in one or more second datasets that are distinct from the first dataset; determine, based at least in part on the first feature of the feature vector, a probability that the first artist identifier is associated with two or more different real-world artists; and in accordance with a determination that the probability satisfies a predetermined condition, provide a report that specifies the first artist identifier as potentially ambiguous.