Personalization based upon social value in online media
Embodiments are directed towards personalizing content to be provided to a user. A recommendation score may be determined for a piece of content for a user. The recommendation score may be based on a combination of an intrinsic value and/or a social value of the content to the user. The social value may be calculated based on a combination of an individual social value for each of the user's friends, which may be determined based on the combination of a social weight, an interest probability, and a recommendation score for the friend. Online personalization of content for a user may provide the user with the tools to be a valuable, appreciated member of the user's social network. By employing embodiments, as described herein, content may be determined and provided to a user, which may help the user gain the attention of their social circles.
1. A method for managing a plurality of content for display to a user, wherein at least one network computer enables actions to be performed, comprising:
identifying at least one social network associated with the user; and
for each of the at least one social network, determining a recommendation score for each of a plurality of content for the user, wherein the recommendation score for a content is based on at least a combination of individual recommendation scores for the content for each friend in an identified social network, wherein the individual recommendation score is recursively determined to a predetermined degree of separation from the user in the identified social network; and
providing a subset of the plurality of content to the user based on the recommendation score of each of the plurality of content for each of the at least one social network.
2. The method of claim 1 , wherein providing the subset of the plurality of content to the user further comprises:
ranking the plurality of content separately for each of the at least one social network based on the recommendation scores of each of the plurality of content; and
providing a predetermined number of highest ranking content for each social channel to the user.
3. The method of claim 1 , further comprising determining the subset of the plurality of content based on a weight of each of the at least one social network and the recommendations scores for the plurality of content for each of the at least one social network.
4. The method of claim 1 , wherein the recommendation score of content for a social network is based on a combination of an intrinsic value of the content to the user and a social value of the content that is based on the combination of the individual recommendation scores for each friend in the social network.
5. The method of claim 1 , wherein determining the recommendation score of content for a social network further comprises, weighting at least an intrinsic value based on an importance to the user of the user's personal interests in comparison to social interests of the social network.
6. The method of claim 1 , further comprising:
determining an intrinsic value of each of the plurality of content based on at least one action that was previously taken by the user on other content that is similar to corresponding content; and
employing the intrinsic value to determine the recommendation score.
7. The method of claim 1 , wherein the individual recommendation score for each respective friend is weighted based on at least an influence of the respective friend on the user.
8. The method of claim 1 , wherein the individual recommendation score for each respective friend is combined with an interest probability that both the respective friend and the user perform an action on content.
9. A network computer for managing a plurality of content for display to a user, comprising:
a memory for storing instructions; and
a processor that executes the instructions to enable actions, including:
identifying at least one social network associated with the user; and
for each of the at least one social network, determining a recommendation score for each of a plurality of content for the user, wherein the recommendation score for a content is based on at least a combination of individual recommendation scores for the content for each friend in an identified social network, wherein the individual recommendation score is recursively determined to a predetermined degree of separation from the user in the identified social network; and
providing a subset of the plurality of content to the user based on the recommendation score of each of the plurality of content for each of the at least one social network.
10. The network computer of claim 9 , wherein providing the subset of the plurality of content to the user further comprises:
ranking the plurality of content separately for each of the at least one social network based on the recommendation scores of each of the plurality of content; and
providing a predetermined number of highest ranking content for each social channel to the user.
11. The network computer of claim 9 , further comprising determining the subset of the plurality of content based on a weight of each of the at least one social network and the recommendation scores for the plurality of content for each of the at least one social network.
12. The network computer of claim 9 , wherein the recommendation score of content for a social network is based on a combination of an intrinsic value of the content to the user and a social value of the content that is based on the combination of the individual recommendation scores for each friend in the social network.
13. The network computer of claim 9 , wherein determining the recommendation score of content for a social network further comprises, weighting at least an intrinsic value based on an importance to the user of the user's personal interests in comparison to social interests of the social network.
14. The network computer of claim 9 , wherein the individual recommendation score for each respective friend is weighted based on at least an influence of the respective friend on the user.
15. The network computer of claim 9 , wherein the individual recommendation score for each respective friend is combined with an interest probability that both the respective friend and the user perform an action on content.
16. A processor readable non-transitory storage media that includes instructions for managing a plurality of content for display to a user, wherein execution of the instructions by a processor enables actions, comprising:
identifying at least one social network associated with the user; and
for each of the at least one social network, determining a recommendation score for each of a plurality of content for the user, wherein the recommendation score for a content is based on at least a combination of individual recommendation scores for the content for each friend in an identified social network, wherein the individual recommendation score is recursively determined to a predetermined degree of separation from the user in the identified social network; and
providing a subset of the plurality of content to the user based on the recommendation score of each of the plurality of content for each of the at least one social network.
17. The media of claim 16 , wherein providing the subset of the plurality of content to the user further comprises:
ranking the plurality of content separately for each of the at least one social network based on the recommendation scores of each of the plurality of content; and
providing a predetermined number of highest ranking content for each social channel to the user.
18. The media of claim 16 , further comprising determining the subset of the plurality of content based on a weight of each of the at least one social network and the recommendations scores for the plurality of content for each of the at least one social network.
19. The media of claim 16 , wherein the recommendation score of content for a social network is based on a combination of an intrinsic value of the content to the user and a social value of the content that is based on the combination of the individual recommendation scores for each friend in the social network.
20. The media of claim 16 , wherein determining the recommendation score of content for a social network further comprises, weighting at least an intrinsic value based on an importance to the user of the user's personal interests in comparison to social interests of the social network.
21. The media of claim 16 , wherein the individual recommendation score for each respective friend is weighted based on at least an influence of the respective friend on the user.
22. The media of claim 16 , wherein the individual recommendation score for each respective friend is combined with an interest probability that both the respective friend and the user perform an action on content.
23. A system for managing a plurality of content for display to a user, comprising:
a score determination computer that is operative to identify at least one social network associated with the user, and for each of the at least one social network, determine a recommendation score for each of a plurality of content for the user, wherein the recommendation score for a content is based on at least a combination of individual recommendation scores for the content for each friend in an identified social network, wherein the individual recommendation score is recursively determined to a predetermined degree of separation from the user in the identified social network; and
a content selection computer that is operative to provide a subset of the plurality of content to the user based on the recommendation score of each of the plurality of content for each of the at least one social network.
24. The system of claim 23 , wherein the content selection computer is operative to rank the plurality of content separately for each of the at least one social network based on the recommendation scores of each of the plurality of content, and to provide a predetermined number of highest ranking content for each social channel to the user.
25. The system of claim 23 , wherein the content selection computer is operative to determine the subset of the plurality of content based on a weight of each of the at least one social network and the recommendations scores for the plurality of content for each of the at least one social network.
26. The system of claim 23 , wherein the recommendation score of content for a social network is based on a combination of an intrinsic value of the content to the user and a social value of the content that is based on the combination of the individual recommendation scores for each friend in the social network.
27. The system of claim 23 , wherein the score determination computer is operative to weight at least an intrinsic value based on an importance to the user of the user's personal interest in comparison to social interests of a social network to determine the recommendation score of content for the social network.
28. The system of claim 23 , wherein the score determination computer is operative to determine an intrinsic value of each of the plurality of content based on at least one action that was previously taken by the user on other content that is similar to corresponding content, and to employ the intrinsic value to determine the recommendation score.
29. The system of claim 23 , wherein the individual recommendation score for each respective friend is weighted based on at least an influence of the respective friend on the user.
30. The system of claim 23 , wherein the individual recommendation score for each respective friend is combined with an interest probability that both the respective friend and the user perform an action on content.