Communications channels in media systems

1. A computing device comprising a processor and a memory, the memory including instructions such that the processor is programmed to:

receive, first user indicia of interest for media content determined at least in part from first user data received from a first user device and second user indicia of interest for media content determined at least in part from second user data received from a second user device;

further receive, from the first user device, first user data related to a media content item;

monitor a conversation between the first user and the second user;

determine a recommendation score of the media content item corresponding to the monitored conversation by evaluating recommendation data based upon data indicating (a) preferences of the first user, (b) facial expressions of the second user during the monitored conversation correlating to the media content item, and (c) feedback from users with an affinity to the first user;

identify, based on the recommendation score, at least one indicia of recommendation of the media content item for the second user;

update, based on the at least one indicia of recommendation, the first user indicia of interest and the second user indicia of interest;

generate output to the first user device, based at least in part on the updated first user indicia of interest, the updated second user indicia of interest, and the first user data related to the media content items, that the first user recommend the media content item to the second user; and

determine a feedback score of the second user for the media content item based on monitoring facial expressions of the second user when viewing the media content item.

2. The computing device of claim 1 , wherein the first and second user indicia of interest include at least one of; (a) one or more numerical values indicating types of media content associated with each of the respective first and second users, (b) one or more keywords indicating characteristics or features of media content items of interest to the respective first and second users and (c) numerical ratings of one or more media content items for the respective first and second users.

3. The computing device of claim 1 , wherein the first user data related to the media content item is one of; (a) a numerical rating of the media content item, (b) a video recording including comments and (c) communications from the first user related to the media content item.

4. The computing device of claim 1 , wherein the processor is further programmed to:

determine a numerical value indicating a degree of relationship between the first and second users is greater than a predetermined threshold; and

generate the output to the first user to recommend the media content item to the second user based at least in part on the determination that the numerical value indicating the degree of relationship between the first and second users is greater than the predetermined threshold.

5. The computing device of claim 1 , wherein the processor is further programmed to:

calculate, prior to generating the output that the first user recommend the media content item to the second user, a numerical value indicating a degree of association between keywords of the first user and keywords of the second user; and

generate the output to the first user to recommend the media content item to the second user based at least in part on the numerical value indicating the degree of association between the keywords of the first user and the keywords of the second user being greater than a predetermined threshold.

6. The computing device of claim 1 , wherein the processor is further programmed to:

calculate, prior to generating the output that the first user recommend the media content item to the second user, a numerical value indicating a degree of association between keywords of the first user and keywords of the second user; and

generate the output to the first user to recommend the media content item to the second user based at least in part on the numerical value indicating the degree of association between the keywords of the first user and the keywords of the second user being less than a predetermined threshold.

7. The computing device of claim 1 , wherein the processor is further programmed to:

determine, prior to generating the output that the first user recommend the media content item to the second user, that the period of time is greater than a predetermined period of time; and

generate the output to the first user to recommend the media content item to the second user based at least in part on determining that the period of time is greater than the predetermined period of time.

8. The computing device of claim 1 , wherein the processor is further programmed to:

establish a communications link between a first user device associated with the first user and a second user device associated with the second user.

9. The computing device of claim 1 , wherein the processor is further programmed to:

identify, based on the first user feedback, keywords associated with the media content item; and

determine, prior to generating output to the first user to recommend the media content item to the second user, that an association of the keywords associated with the media content item by the first user with second user keywords is greater than a predetermined threshold.

10. The computing device of claim 1 , wherein the processor is further programmed to:

receive, subsequent to a conversation during which the first user recommends the media content item to the second user, data indicating that the second user viewed the media content item, and

update second user indicia of interest based on the data indicating that the second user viewed the media content item.

11. A method comprising:

receiving, by a computing device, first user indicia of interest for media content determined at least in part from first user data received from a first user device and second user indicia of interest for media content determined at least in part from second data received from a second user device;

further receiving, from the first user device, first user data related to a media content item;

monitoring a conversation between the first user and the second user;

determining a recommendation score of the media content item corresponding to the monitored conversation by evaluating recommendation data based upon data indicating (a) preferences of the first user, (b) facial expressions of the second user during the monitored conversation correlating to the media content item, and (c) feedback from users with an affinity to the first user;

identifying, based on the recommendation score, at least one indicia of recommendation of the media content item of the second user;

updating, based on the at least one indicia of recommendation, the first user indicia of interest and the second user indicia of interest; and

generating output to the first user device, based at least in part on the updated first user indicia of interest, the updated second user indicia of interest, and the first user data related to the media content item, that the first user recommend the media content item to the second user; and

determining a feedback score of the second user for the media content item based on monitoring facial expressions of the second user when viewing the media content item.

12. The method of claim 11 , wherein the first and second user indicia of interest include at least one of; (a) one or more numerical values indicating types of media content associated with each of the respective first and second users, (b) one or more keywords indicating characteristics or features of media content items of interest to the respective first and second users and (c) numerical ratings of one or more media content items for the respective first and second users.

13. The method of claim 11 , wherein the first user data related to the media content item is one of; (a) a numerical rating of the media content item, (b) a video recording including comments and (c) communications from the first user related to the media content item.

14. The method of claim 11 , further comprising:

determining a numerical value indicating a degree of relationship between the first and second users is greater than a predetermined threshold; and

generating the output to the first user to recommend the media content item to the second user based at least in part on the determination that the numerical value indicating the degree of relationship between the first and second users is greater than the predetermined threshold.

15. The method of claim 11 , further comprising:

calculating, prior to generating the output that the first user recommend the media content item to the second user, a numerical value indicating a degree of association between keywords of the first user and keywords of the second user; and

generating the output to the first user to recommend the media content item to the second user based at least in part on the numerical value indicating the degree of association between the keywords of the first user and the keywords of the second user being greater than a predetermined threshold.

16. The method of claim 11 , further comprising:

calculating, prior to generating the output that the first user recommend the media content item to the second user, a numerical value indicating a degree of association between keywords of the first user and keywords of the second user; and

generating the output to the first user to recommend the media content item to the second user based at least in part on the numerical value indicating the degree of association between the keywords of the first user and the keywords of the second user being less than a predetermined threshold.

17. The method of claim 11 , further comprising:

determining, prior to generating the output that the first user recommend the media content item to the second user, that the period of time is greater than a predetermined period of time; and

generating the output to the first user to recommend the media content item to the second user based at least in part on determining that the period of time is greater than the predetermined period of time.

18. The method of claim 11 , further comprising:

establishing a communications link between a first user device associated with the first user and a second user device associated with the second user.

19. The computing device of claim 1 , wherein the processor is further programmed to:

determine a period of time since the first user and the second user have communicated; and

generate output to the first user device, based at least in part on the period of time since the first user and the second user have communicated.

20. A computing system comprising:

one or more processors; and

one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to perform a process comprising:

receiving computing device, first user indicia of interest for media content determined at least in part from first user data received from a first user device and second user indicia of interest for media content determined at least in part from second data received from a second user device;

further receiving, from the first user device, first user data related to a media content item;

monitoring a conversation between the first user and the second user;

determining a recommendation score of the media content item corresponding to the monitored conversation by evaluating recommendation data based upon data indicating (a) preferences of the first user, (b) facial expressions of the second user during the monitored conversation correlating to the media content item, and (c) feedback from users with an affinity to the first user;

identifying, based on the recommendation score, at least one indicia of recommendation of the media content item of the second user;

updating, based on the at least one indicia of recommendation, the first user indicia of interest and the second user indicia of interest;

generating output to the first user device, based at least in part on the updated first user indicia of interest, the updated second user indicia of interest, and the first user data related to the media content item, that the first user recommend the media content item to the second user; and

determining a feedback score of the second user for the media content item based on monitoring facial expressions of the second user when viewing the media content item.