Technologies are described herein for ordering messages. A computer-implemented method for ordering of messages may include collecting training data related to users of a messaging system, generating a label for a message, generating feature values based on the training data and the message, and training a relevance model based on the feature values using a machine learning algorithm. The relevance model is configured to rank new message candidates received by the messaging system for display by the messaging system.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, the method comprising: collecting training data related to users of a messaging system, the training data being related to at least one message stored on the messaging system; generating at least one label for the at least one message, the at least one label being associated with a relevancy of the at least one message to users of the messaging system; generating a plurality of feature values based on the training data and the at least one message, the plurality of feature values indicating a numerical measure of relevancy of the at least one message to the users of the messaging system; and training a relevance model based on the plurality of feature values and the at least one label using a machine learning algorithm, the relevance model configured to rank new message candidates received by the messaging system. 2. The method of claim 1, wherein the training data comprises at least one measure of user interaction with the at least one message. 3. The method of claim 2, wherein the measure of user interaction includes time-decayed measures of responses to and interactions with the at least one message. 4. The method of claim 2, wherein the measure of user interaction includes at least one of: an amount of time a user reviews the at least one message; a number of responses to the at least one message; a number of re-postings of the at least one message; a value representing whether any user liked or disliked the at least one message; an age of the at least one message; a time at which the at least one message was sent; a number of interactions between a user creating the at least one message and other users of the messaging system; a number of interactions between the user creating the at least one message and groups of users of the messaging system; a number of interactions between a user viewing the at least one message and other users of the messaging system; and a number of interactions between the user viewing the at least one message and groups of users of the messaging system. 5. The method of claim 4, wherein the numerical measure for each feature value of the plurality of feature values is based on the measure of user interaction with the at least one message. 6. The method of claim 1, wherein the at least one label is a numerical value associated with a measure of the relevancy of the at least one message. 7. The method of claim 6, wherein the measure of the relevancy of the at least one message includes at least one of: a number of times a user replies to the at least one message; and a number of times a user likes the at least one message. 8. The method of claim 1, further comprising: receiving a new message for display through the messaging system; and displaying the new message based on a ranking obtained through the relevance model. 9. The method of claim 1, further comprising: receiving a new message for display through the messaging system; generating feature values for the new message; applying the feature values for the new message to the relevance model; and ranking or displaying the new message based on application of the feature values for the new message. 10. The method of claim 1, wherein the machine learning algorithm includes at least one of a boosted decision tree, a gradient boosted decision tree, a RankNet algorithm, a LambdaRank algorithm, or a LambdaMART algorithm. 11. The method of claim 10, wherein the machine learning algorithm includes a plurality of decision trees based on a plurality of feature values, and wherein each decision tree of the plurality of decision trees includes a plurality of ranking leaves determining a rank for a particular set of feature values. 12. A computer-implemented method, the method comprising: collecting training data related to users of a messaging system, the training data being related to messages stored on the messaging system; generating a plurality of feature values based on the training data, the plurality of feature values indicating a numerical measure of relevancy of the messages to the users of the messaging system; training a relevance model based on the plurality of feature values using a machine learning algorithm, the relevance model configured to rank new message candidates received by the messaging system; ranking the new message candidates based on the relevance model to create a set of ranked messages; and outputting the ranked messages through the messaging system. 13. The method of claim 12, wherein the training data comprises measures of user interactions with the messages. 14. The method of claim 13, wherein the measures of user interactions includes at least one of: an amount of time a user reviews a message; a number of responses to a message; a number of re-postings of a message; a value representing whether any user liked or disliked a message; an age of a message; a time at which a message was sent; a number of interactions between a user creating a message and other users of the messaging system; a number of interactions between the user creating a message and groups of users of the messaging system; a number of interactions between a user viewing a message and other users of the messaging system; and a number of interactions between the user viewing a message and groups of users of the messaging system. 15. The method of claim 14, wherein the numerical measure for each feature value of the plurality of feature values is based on the measure of user interactions with the at least one message. 16. The method of claim 12, wherein ranking the new message candidates comprises: receiving a new message for display through the messaging system; generating feature values for the new message; applying the feature values for the new message to the relevance model; and ranking the new message based on application of the feature values for the new message. 17. The method of claim 12, wherein the machine learning algorithm includes at least one of a boosted decision tree, a gradient boosted decision tree, a RankNet algorithm, a LambdaRank algorithm, or a LambdaMART algorithm. 18. The method of claim 17, wherein the machine learning algorithm includes a plurality of decision trees based on a plurality of feature values, and wherein each decision tree of the plurality of decision trees includes a plurality of ranking leaves determining a rank for a particular set of feature values. 19. A computer, comprising: a processor; and a computer-readable storage medium in communication with the processor, the computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by the processor, cause the processor to collect training data related to users of a messaging system, the training data being related to messages stored on the messaging system, generate a plurality of feature values based on the training data, the plurality of feature values indicating a numerical measure of relevancy of the messages to the users of the messaging system, train a relevance model based on the plurality of feature values using a machine learning algorithm, the relevance model configured to rank new message candidates received by the messaging system, receive a new message candidate for display through the messaging system, generating feature values for the new message candidate, rank the new message candidate based on the relevance model and the feature values for the new message candidate to create a message rank, and output the new message candidate and the message rank through the messaging system. 20. The computer of claim 19, wherein the machine learning algorithm includes at least one of a boosted decision tree, a gradient boosted decision tree, a RankNet algorithm, a LambdaRank algorithm, or a LambdaMART algorithm.