Using combined coefficients for viral action optimization in an on-line social network

1. A computer-implemented method comprising:

learning a first logistic regression model to obtain a first coefficient vector, the first coefficient vector being with respect to probability of a viewer interaction with an item presented to the viewer via a news feed web page;

learning a second logistic regression to obtain a second coefficient vector, the second coefficient vector being with respect to probability of a viewer performing a viral action on an item presented to the viewer via a news feed web page;

executing simulations each time varying a value of a tradeoff parameter that indicates importance of viral actions as compared to interactions;

constructing a tradeoff curve based on the result of the simulations;

selecting a value of the tradeoff parameter to be used in the calculating of the combined coefficient based on information indicated by the tradeoff curve;

calculating a combined coefficient based on the first coefficient vector, the second coefficient vector, and the tradeoff parameter;

using at least one processor, executing a personalization model to obtain a respective relevance rank for each item in an inventory of activities identified as potentially of interest to a focus viewer, using the combined coefficient and respective feature vectors, a feature vector is constructed with respect to the focus viewer and a given item from the inventory of activities using signals characterizing the focus viewer and the given item; and

ordering the items in the inventory for presentation to the focus viewer based on the respective relevance ranks of the items in the inventory.

2. The method of claim 1 , comprising constructing a news feed web page that includes items in the inventory ordered based on the respective relevance ranks.

3. The method of claim 1 , comprising causing presentation of the news feed web page on a display device of the focus viewer.

4. The method of claim 1 , wherein the learning of the first logistic regression model to obtain the first coefficient vector is using historical data obtained in the on-line social network system.

5. The method of claim 1 , wherein the learning of the second logistic regression model to obtain the second coefficient vector is using historical data obtained in the on-line social network system.

6. The method of claim 1 , wherein a viral action is an action that results in an additional item being included in an inventory of updates for another member in the on-line social network system.

7. The method of claim 1 , wherein an interaction is any action by a viewer with respect to an item presented to the viewer via a news feed web page.

8. The method of claim 1 , wherein the executing of simulation comprises utilizing a random session where a viewer is presented with a feed that includes randomly chosen activities.

9. A computer-implemented system comprising:

one or more processors; and

a non-transitory computer readable storage medium comprising instructions that when executed by the one or processors cause the one or more processors to perform operations comprising:

learning a first logistic regression model to obtain a first coefficient vector, the first coefficient vector being with respect to probability of a viewer interaction with an item presented to the viewer via a news feed web page;

learning a second logistic regression to obtain a second coefficient vector, the second coefficient vector being with respect to probability of a viewer performing a viral action on an item presented to the viewer via a news feed web page;

executing simulations each time varying a value of a tradeoff parameter that indicates importance of viral actions as compared to interactions;

constructing a tradeoff curve based on the result of the simulations;

selecting a value of the tradeoff parameter to be used in the calculating of the combined coefficient based on information indicated by the tradeoff curve; and

calculating a combined coefficient based on the first coefficient vector, the second coefficient vector, and the tradeoff parameter;

executing a personalization model to obtain a respective relevance rank for each item in an inventory of activities identified as potentially of interest to a focus viewer, using the combined coefficient and respective feature vectors, a feature vector is constructed with respect to the focus viewer and a given item from the inventory of activities using signals characterizing the focus viewer and the given item; and

ordering the items in the inventory for presentation to the focus viewer based on the respective relevance ranks of the items in the inventory.

10. The system of claim 9 , comprising constructing a news feed web page that includes items in the inventory ordered based on the respective relevance ranks.

11. The system of claim 9 , comprising causing presentation of the news feed web page on a display device of the focus viewer.

12. The system of claim 9 , wherein the learning of the first logistic regression model to obtain the first coefficient vector is using historical data obtained in the on-line social network system.

13. The system of claim 9 , wherein the learning of the second logistic regression model to obtain the second coefficient vector is using historical data obtained in the on-line social network system.

14. The system of claim 9 , wherein a viral action is an action that results in an additional item being included in an inventory of updates for another member in the on-line social network system.

15. The system of claim 9 , wherein an interaction is any action by a viewer with respect to an item presented to the viewer via a news feed web page.

16. The system of claim 9 , wherein the executing of simulation comprises utilizing a random session where a viewer is presented with a feed that includes randomly chosen activities.

17. A machine-readable non-transitory storage medium having instruction data executable by a machine to cause the machine to perform operations comprising:

learning a first logistic regression model to obtain a first coefficient vector, the first coefficient vector being with respect to probability of a viewer interaction with an item presented to the viewer via a news feed web page;

learning a second logistic regression to obtain a second coefficient vector, the second coefficient vector being with respect to probability of a viewer performing a viral action on an item presented to the viewer via a news feed web page;

executing simulations each time varying a value of a tradeoff parameter that indicates importance of viral actions as compared to interactions;

constructing a tradeoff curve based on the result of the simulations;

selecting a value of the tradeoff parameter to be used in the calculating of the combined coefficient based on information indicated by the tradeoff curve;

calculating a combined coefficient based on the first coefficient vector, the second coefficient vector, and the tradeoff parameter;

executing a personalization model to obtain a respective relevance rank for each item in an inventory of activities identified as potentially of interest to a focus viewer, using the combined coefficient and respective feature vectors, a feature vector is constructed with respect to the focus viewer and a given item from the inventory of activities using signals characterizing the focus viewer and the given item; and

ordering the items in the inventory for presentation to the focus viewer based on the respective relevance ranks of the items in the inventory.