A machine may be configured to blend connection recommendation streams. For example, the machine, based on a member identifier of a member of a SNS, accesses a list of other members of the SNS and a list of guests (e.g., non-members). The machine identifies a member probability value representing a likelihood of the member inviting another member to connect via the social graph of the member, and identifies a guest probability value representing a likelihood of the member inviting a guest to connect via the social graph. The machine generates a blended list of other members and guests based on the member probability values, the guest probability values, and a coefficient value selected to control a presence of a type of connections in the blended list. The machine generates recommendations for the member to invite people included in the blended list to connect with the member via the social graph.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: based on a member identifier of a particular member of a Social Networking Service (SNS), accessing a first list of other members of the SNS and a second list of guests, the first list of members including one or more other members who are potential connections of the particular member via a social graph of the particular member, the second list of guests including one or more guests who are not members of the SNS and who are potential connections of the particular member via the social graph; for each of the one or more other members, identifying a member probability value that represents a likelihood of the particular member inviting another member of the one or more other members to connect via the social graph of the particular member; for each of the one or more guests, identifying a guest probability value that represents a likelihood of the particular member inviting a guest of the one or more guests to connect via the social graph of the particular member; generating, using one or more hardware processors, a blended list of other members and guests for the particular member based on the member probability values associated with the one or more other members, the guest probability values associated with the one or more guests, and a coefficient value selected to control a presence of a type of connections in the blended list; and generating one or more connection recommendations for the particular member to invite one or more people included in the blended list to connect with the particular member via the social graph. 2. The method of claim 1, wherein the social graph is a first social graph of the particular member, the method further comprising: selecting the one or more members from a second social graph of an actual connection of the particular member on the SNS based on identifying the one or more members in the second social graph who have a member profile feature in common with the particular member; and generating the first list of other members of the SNS based on selected one or more other members. 3. The method of claim 1, further comprising: accessing data pertaining to past behavior and activity associated with the particular member; and generating the second list of guests based on the data pertaining to the past behavior and activity associated with the particular member. 4. The method of claim 1, further comprising: determining that the particular member sent an email message to one or more contacts; determining that the one or more contacts are not members of the SNS; and generating the second list of guests based on the determining that the particular member sent an email message to the one or more contacts, and the determining that the one or more contacts are not members of the SNS. 5. The method of claim 1, wherein the generating of the blended list includes: for each of the one or more guests, generating a weighted guest probability value based on multiplying the coefficient value and the guest probability value, and ordering identifiers of the one or more other members and identifiers of the one or more guests in the blended list based on a ranking of the member probability values and the weighted guest probability values, the method further comprising: causing a presentation of the one or more connection recommendations in a user interface of a device associated with the particular member based on the blended list. 6. The method of claim 5, wherein the ordering includes: comparing the member probability value associated with the other member and the weighted guest probability value, and ranking the member probability value and the weighted guest probability value in a decreasing order based on the comparing of the member probability value and the weighted guest probability value, wherein the causing of the presentation of the one or more connection recommendations includes causing a presentation of a first connection recommendation for the other member and a second recommendation for the guest based on the ranking of the member probability value and the weighted guest probability value. 7. The method of claim 1, wherein the member probability value is a function of a set of member profile features. 8. The method of claim 1, wherein the member probability value is a function of a set of member activity features. 9. The method of claim 1, wherein the coefficient is selected for a segment of the members of the SNS based on a member profile feature common to the segment. 10. A system comprising: one or more hardware processors; and a machine-readable medium for storing instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations comprising: based on a member identifier of a particular member of a Social Networking Service (SNS), accessing a first list of other members of the SNS and a second list of guests, the first list of members including one or more other members who are potential connections of the particular member via a social graph of the particular member, the second list of guests including one or more guests who are not members of the SNS and who are potential connections of the particular member via the social graph; for each of the one or more other members, identifying a member probability value that represents a likelihood of the particular member inviting another member of the one or more other members to connect via the social graph of the particular member; for each of the one or more guests, identifying a guest probability value that represents a likelihood of the particular member inviting a guest of the one or more guests to connect via the social graph of the particular member; generating a blended list of other members and guests for the particular member based on the member probability values associated with the one or more other members, the guest probability values associated with the one or more guests, and a coefficient value selected to control a presence of a type of connections in the blended list; and generating one or more connection recommendations for the particular member to invite one or more people included in the blended list to connect with the particular member via the social graph. 11. The system of claim 10, wherein the social graph is a first social graph of the particular member, and wherein the operations further comprise: selecting the one or more members from a second social graph of an actual connection of the particular member on the SNS based on identifying the one or more members in the second social graph who have a member profile feature in common with the particular member; and generating the first list of other members of the SNS based on selected one or more other members. 12. The system of claim 10, wherein the operations further comprise: accessing data pertaining to past behavior and activity associated with the particular member; and generating the second list of guests based on the data pertaining to the past behavior and activity associated with the particular member. 13. The system of claim 10, wherein the operations further comprise: determining that the particular member sent an email message to one or more contacts; determining that the one or more contacts are not members of the SNS; and generating the second list of guests based on the determining that the particular member sent an email message to the one or more contacts, and the determining that the one or more contacts are not members of the SNS. 14. The system of claim 10, wherein the generating of the blended list includes: for each of the one or more guests, generating a weighted guest probability value based on multiplying the coefficient value and the guest probability value; and ordering identifiers of the one or more other members and identifiers of the one or more guests in the blended list based on a ranking of the member probability values and the weighted guest probability values, and wherein the operations further comprise: causing a presentation of the one or more connection recommendations in a user interface of a device associated with the particular member based on the blended list. 15. The system of claim 14, wherein the ordering includes: comparing the member probability value associated with the other member and the weighted guest probability value, and ranking the member probability value and the weighted guest probability value in a decreasing order based on the comparing of the member probability value and the weighted guest probability value, wherein the causing of the presentation of the one or more connection recommendations includes causing a presentation of a first connection recommendation for the other member and a second recommendation for the guest based on the ranking of the member probability value and the weighted guest probability value. 16. The system of claim 10, wherein the member probability value is a function of a set of member profile features. 17. The system of claim 10, wherein the member probability value is a function of a set of member activity features. 18. The system of claim 10, wherein the coefficient is selected for a segment of the members of the SNS based on a member profile feature common to the segment. 19. A non-transitory machine-readable medium comprising instructions that, when executed by one or more hardware processors, cause the one or more hardware processors to perform operations comprising: based on a member identifier of a particular member of a Social Networking Service (SNS), accessing a first list of other members of the SNS and a second list of guests, the first list of members including one or more other members who are potential connections of the particular member via a social graph of the particular member, the second list of guests including one or more guests who are not members of the SNS and who are potential connections of the particular member via the social graph; for each of the one or more other members, identifying a member probability value that represents a likelihood of the particular member inviting another member of the one or more other members to connect via the social graph of the particular member; for each of the one or more guests, identifying a guest probability value that represents a likelihood of the particular member inviting a guest of the one or more guests to connect via the social graph of the particular member; generating a blended list of other members and guests for the particular member based on the member probability values associated with the one or more other members, the guest probability values associated with the one or more guests, and a coefficient value selected to control a presence of a type of connections in the blended list; and generating one or more connection recommendations for the particular member to invite one or more people included in the blended list to connect with the particular member via the social graph. 20. The non-transitory machine-readable medium of claim 19, wherein the generating of the blended list includes: for each of the one or more guests, generating a weighted guest probability value based on multiplying the coefficient value and the guest probability value; and ordering identifiers of the one or more other members and identifiers of the one or more guests in the blended list based on a ranking of the member probability values and the weighted guest probability values, and wherein the operations further comprise: causing a presentation of the one or more connection recommendations in a user interface of a device associated with the particular member based on the blended list.