Techniques for predicting a user response to the e-mail content are described. According to various embodiments, member email interaction data associated with a particular member and email content data describing a particular email content item is accessed. The data is then encoded into one or more feature vectors and assembled to thereby generate an assembled feature vector. Thereafter, a prediction modeling process is performed, based on the assembled feature vector and a trained prediction model, to predict a likelihood of the particular member performing a particular user action on the particular email content item.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: accessing, via one or more data sources, data including email content data describing a particular email content item and member email interaction data describing a particular member's interactions with various email content; encoding the data accessed from the external data sources into one or more feature vectors, and assembling the one or more feature vectors to thereby generate an assembled feature vector; and performing prediction modeling, by a machine including a memory and at least one processor, based on the assembled feature vector and a trained prediction model, to predict a likelihood of the particular member performing a particular user action on the particular email content item. 2. The method of claim 1, wherein the email content data specifies an email type, and wherein the email type is at least one of a network connection update e-mail, a news update e-mail, a jobs update e-mail, an influencer post update e-mail, a company update e-mail, a group update e-mail, a university update e-mail, and a digest e-mail. 3. The method of claim 1, wherein the member email interaction data indicates a quantity of various email types transmitted to the particular member, a quantity of clicks submitted by the particular member in conjunction with the various email types, and a quantity of email unsubscribe requests submitted by the particular member in conjunction with the various email types. 4. The method of claim 1, wherein the user action is any one of a click response, a non-click response, a hover response, and a conversion response. 5. The method of claim 1, further comprising: accessing, via one or more data sources, member site interaction data describing the particular member's interaction with various features or content of an online social network service; and encoding the member site interaction data into the assembled feature vector. 6. The method of claim 5, wherein the member site interaction data indicates a quantity of various user actions performed by the particular member in association with the various features or content of the online social network service. 7. The method of claim 1, further comprising: accessing, via one or more data sources, member profile data describing the particular member; and encoding the member profile data into the assembled feature vector. 8. The method of claim 7, wherein the member profile data includes at least one of age, location, industry, current job, employer, experience, skills, education, school, endorsements, seniority level, company size, and connections are associated with the particular member. 9. The method of claim 1, wherein the prediction model is any one of a logistic regression model, a Naïve Bayes model, a support vector machines (SVM) model, a decision trees model, and a neural network model. 10. The method of claim 1, wherein the prediction module performs a training operation to refine coefficients of a logistic regression model, based on training set data comprising the assembled feature vector. 11. The method of claim 1, further comprising: identifying similar members of the online social network service that are similar to the particular member; accessing at least one of member profile data, member email interaction data, and member site interaction data associated with the one or more similar members; and inserting at least one of the member profile data, the member email interaction data, and the member site interaction data associated with the one or more similar members into the assembled feature vector. 12. The method of claim 11, wherein the similar members are identified by: accessing member profile data associated with the particular member; and determining a match between member profile data associated with the similar members and the accessed member profile data associated with the particular member. 13. The method of claim 1, further comprising: identifying member connections that are connected to the particular member via the online social network service; accessing at least one of member profile data, member email interaction data, and member site interaction data associated with the one or more member connections; and inserting at least one of the member profile data, the member email interaction data, and the member site interaction data associated with the one or more member connections into the assembled feature vector. 14. The method of claim 1, further comprising: determining that the likelihood of the particular member performing the particular user action on the particular email content item is lower than a predetermined threshold; and reducing a distribution of the particular email content item to the particular member. 15. The method of claim 14, wherein the reducing further comprises: updating email preference settings associated with the particular member, the updated email preference settings specifying a reduced quantity or frequency of the particular email content item for distribution to the particular member. 16. The method of claim 14, wherein the reducing further comprises at least one of: reducing a quantity of the particular email content item included in an e-mail for distribution to the particular member; reducing a frequency of the particular email content item for distribution to the particular member; temporarily preventing the particular email content item from being distributed to the particular member; and unsubscribing the particular member from a distribution of the particular email content item. 17. The method of claim 1, further comprising: determining that the likelihood of the particular member performing the particular user action on the particular email content item is greater than a predetermined threshold; and increasing a distribution of the particular email content item to the particular member. 18. The method of claim 17, wherein the increasing further comprises at least one of: increasing a quantity of the particular email content item included in an e-mail for distribution to the particular member; and increasing a frequency of the particular email content item for distribution to the particular member. 19. A system comprising: a machine including a memory and at least one processor; a source module, executable by the machine, configured to: access, via one or more data sources, data including member email interaction data associated with a particular member and email content data describing a particular email content item; and encode the data accessed from the external data sources into one or more feature vectors, and assembling the one or more feature vectors to thereby generate an assembled feature vector; and a prediction module configured to perform prediction modeling, by a machine including a memory and at least one processor, based on the assembled feature vector and a trained prediction model, to predict a likelihood of the particular member performing a particular user action on the particular email content item. 20. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising: accessing, via one or more data sources, data including member email interaction data associated with a particular member and email content data describing a particular email content item; encoding the data accessed from the external data sources into one or more feature vectors, and assembling the one or more feature vectors to thereby generate an assembled feature vector; and performing prediction modeling, by a machine including a memory and at least one processor, based on the assembled feature vector and a trained prediction model, to predict a likelihood of the particular member performing a particular user action on the particular email content item.