An online system stores objects representing potential transactions of an enterprise. The online system uses machine learning techniques to predict likelihood of success for a potential transaction object. The online system stores historical data describing activities associated with potential transaction objects and uses the stored data as training dataset for a predictor model. The online system extracts features describing potential transaction objects and provides these as input to the predictor model for predicting the likelihood of success of a given potential transaction. The online system may use predictions of likelihood of success of potential transactions to identify a set of potential transactions that should be acted upon to maximize the benefit the enterprise within a time interval, for example, by the end of the current month.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for determining feature weights for ranking search results, the method comprising: storing, by a system, data describing a plurality of potential transaction objects, each potential transaction object representing a potential transaction associated with an enterprise; storing historical data describing user actions associated with each of the plurality of potential transaction objects; storing a predictor model based on the stored historical data, the predictor model configured to determine a score for a potential transaction object, the score indicating a likelihood of success of a transaction based on the potential transaction object; receiving a set of input potential transaction objects, each input potential transaction object representing a potential transaction associated with the enterprise; for each of the set of input potential transaction objects: extracting a set of features based on data associated with the potential transaction object, the set of features comprising features describing user interactions associated with the potential transaction object; and determining, by the predictor model, a score for the potential transaction object, the score indicating a likelihood of success of a transaction based on the potential transaction object within a given time interval; ranking the set of potential transaction objects based on the scores of the potential transaction objects; and sending information describing the ranked set of potential transaction objects to a client device. 2. The method of claim 1, wherein each object is associated with an amount associated with a potential transaction, the method further comprising: determining aggregate information based on the set of objects, the aggregate information describing an aggregate amount at the end of the time interval; and wherein sending information describing the ranked set of objects to a client device comprises sending the aggregate information. 3. The method of claim 2, wherein the amount represents a total amount associated with a subset of objects, each object in the subset having a score within a predetermined range. 4. The method of claim 1, wherein the set of features comprises a feature indicating a rate of interactions associated with a potential transaction associated with the object, the interactions performed within a predetermined time interval. 5. The method of claim 1, wherein the set of features comprises a feature indicating a rate of updates to the object performed within a predetermined time interval. 6. The method of claim 1, wherein the set of features comprises a feature indicating a total number of updates to the object performed since the object was created. 7. The method of claim 1, wherein the set of features comprises a feature indicating a time since the last update was performed on the object. 8. The method of claim 1, wherein the set of features comprises a feature indicating a category, the category mapping to one or more stages of the potential transaction object. 9. The method of claim 8, wherein the set of features comprises a feature indicating a number of times the category of the object changed in a predetermined time interval. 10. The method of claim 8, wherein the set of features comprises a feature indicating a number of days since the object was in the category. 11. The method of claim 1, wherein the set of features comprises a feature indicating a number of days spent by the object in each category. 12. The method of claim 1, further comprising: selecting recommendations of objects based on the ranking, the recommendations corresponding to objects with high scores; and wherein sending information describing the ranked set of objects to a client device comprises sending the recommendations of objects. 13. The method of claim 1, wherein the system is a multi-tenant system storing data for a plurality of tenants, each tenant representing an enterprise. 14. The method of claim 13, wherein the predictor model is for a particular tenant of the multi-tenant system, the method further comprising: selecting training data for training the predictor model based on historical data of the particular tenant. 15. The method of claim 14, wherein the predictor model is a first predictor model and the particular tenant is a first tenant, the method further comprising: training a second predictor model based on stored historical data of a second tenant. 16. A computer readable non-transitory storage medium storing instructions for: storing, by a system, data describing a plurality of potential transaction objects, each potential transaction object representing a potential transaction associated with an enterprise; storing historical data describing user actions associated with each of the plurality of potential transaction objects; storing a predictor model based on the stored historical data, the predictor model configured to determine a score for a potential transaction object, the score indicating a likelihood of success of a transaction based on the potential transaction object; receiving a set of input potential transaction objects, each input potential transaction object representing a potential transaction associated with the enterprise; for each of the set of input potential transaction objects: extracting a set of features based on data associated with the potential transaction object, the set of features comprising features describing user interactions associated with the potential transaction object; and determining, by the predictor model, a score for the potential transaction object, the score indicating a likelihood of success of a transaction based on the potential transaction object within a given time interval; ranking the set of potential transaction objects based on the scores of the potential transaction objects; and sending information describing the ranked set of potential transaction objects to a client device. 17. The computer readable non-transitory storage medium of claim 16, wherein each object is associated with an amount associated with a potential transaction, further storing instructions for: determining aggregate information based on the set of objects, the aggregate information describing an aggregate amount at the end of the time interval; and wherein sending information describing the ranked set of objects to a client device comprises sending the aggregate information. 18. The computer readable non-transitory storage medium of claim 16, further storing instructions for: selecting recommendations of objects based on the ranking, the recommendations corresponding to objects with high scores; and wherein sending information describing the ranked set of objects to a client device comprises sending the recommendations of objects. 19. A computer-implemented system comprising: a computer processor; and a computer readable non-transitory storage medium storing instructions thereon, the instructions when executed by a processor cause the processor to perform the steps of: storing, by a system, data describing a plurality of potential transaction objects, each potential transaction object representing a potential transaction associated with an enterprise; storing historical data describing user actions associated with each of the plurality of potential transaction objects; storing a predictor model based on the stored historical data, the predictor model configured to determine a score for a potential transaction object, the score indicating a likelihood of success of a transaction based on the potential transaction object; receiving a set of input potential transaction objects, each input potential transaction object representing a potential transaction associated with the enterprise; for each of the set of input potential transaction objects: extracting a set of features based on data associated with the potential transaction object, the set of features comprising features describing user interactions associated with the potential transaction object; and determining, by the predictor model, a score for the potential transaction object, the score indicating a likelihood of success of a transaction based on the potential transaction object within a given time interval; ranking the set of potential transaction objects based on the scores of the potential transaction objects; and sending information describing the ranked set of potential transaction objects to a client device. 20. The computer system of claim 19, wherein each object is associated with an amount associated with a potential transaction, wherein the computer readable non-transitory storage medium further stores instructions for: determining aggregate information based on the set of objects, the aggregate information describing an aggregate amount at the end of the time interval; and wherein sending information describing the ranked set of objects to a client device comprises sending the aggregate information.