Communications channels in media systems

1. A method comprising:

receiving, from a first user device associated with a first user, first user indicia of interest for a media content item;

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

determining a recommendation score of the media content item corresponding to the monitored conversation based on (a) preferences of the first user and (b) facial expressions of the second user during the monitored conversation correlating to the media content item;

generating, based on the recommendation score, output to the first user device 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.

2. The method of claim 1 , further comprising:

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

3. The method of claim 1 , 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.

4. The method of claim 1 , 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.

5. The method of claim 1 , further comprising:

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

generating the 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.

6. The method of claim 1 , wherein the first user indicia of interest 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.

7. The method of claim 1 , wherein the first user indicia of interest and a 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 first user and the second user, (b) one or more keywords indicating characteristics or features of media content items of interest to the first user and the second user, and (c) numerical ratings of one or more media content items for the first user and the second user.

8. A non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to perform operations comprising:

receiving, from a first user device associated with a first user, first user indicia of interest for a media content item;

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

determining a recommendation score of the media content item corresponding to the monitored conversation based on (a) preferences of the first user and (b) facial expressions of the second user during the monitored conversation correlating to the media content item;

generating, based on the recommendation score, output to the first user device 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.

9. The non-transitory computer-readable medium of claim 8 , wherein the operations further comprise:

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

10. The non-transitory computer-readable medium of claim 8 , wherein the operations further comprise:

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.

11. The non-transitory computer-readable medium of claim 8 , wherein the operations further comprise:

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.

12. The non-transitory computer-readable medium of claim 8 , wherein the operations further comprise:

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

generating the 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.

13. The non-transitory computer-readable medium of claim 8 , wherein the first user indicia of interest 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 non-transitory computer-readable medium of claim 8 , wherein the first user indicia of interest and a 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 first user and the second user, (b) one or more keywords indicating characteristics or features of media content items of interest to the first user and the second user, and (c) numerical ratings of one or more media content items for the first user and the second user.

15. A system comprising:

one or more processors; and

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

receiving, from a first user device associated with a first user, first user indicia of interest for a media content item;

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

determining a recommendation score of the media content item corresponding to the monitored conversation based on (a) preferences of the first user and (b) facial expressions of the second user during the monitored conversation correlating to the media content item;

generating, based on the recommendation score, output to the first user device 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.

16. The system according to claim 15 , wherein the process further comprises:

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

17. The system according to claim 15 , wherein the process further comprises:

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.

18. The system according to claim 15 , wherein the process further comprises:

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.

19. The system according to claim 15 , wherein the process further comprises:

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

generating the 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. The system according to claim 15 ,

wherein the first user indicia of interest 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, and

wherein the first user indicia of interest and a 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 first user and the second user, (b) one or more keywords indicating characteristics or features of media content items of interest to the first user and the second user, and (c) numerical ratings of one or more media content items for the first user and the second user.