Recommending media programs based on media program popularity
A computer-implemented method includes receiving information expressing a user's interest in one or more media programs, obtaining information indicative of popularity for a plurality of media programs responsive to the received information by individuals other than the user, and transmitting one or more recommendations of media programs for display to the user, from the plurality of media programs that relate to the received information.
1. A system comprising:
one or more computer processors; and
one or more non-transitory computer readable devices that include instructions that, when executed by the one or more computer processors, causes the processors to perform operations, the operations comprising:
receiving information expressing a user's interest in one or more media programs;
training a recommendation engine by using behavior information from a plurality of users;
deriving, based on user profile information, a similarity between the user and one or more of the plurality of users;
identifying, using the recommendation engine, one or more other media programs that other users who share the similarity with the user have frequently presented in search submissions; and
generating and providing, for presentation to the user, a recommendation of the identified one or more other media programs.
2. A computer-implemented method, comprising:
receiving, at a computer system, information expressing a user's interest in one or more media programs;
training a recommendation engine by using behavior information from a plurality of users;
deriving, based on user profile information, a similarity between the user and one or more of the plurality of users;
identifying, using the recommendation engine, one or more other media programs that other users who share the similarity with the user have frequently presented in search submissions; and
generating and providing, for presentation to the user, a recommendation of the identified one or more other media programs.
3. The method of claim 2 , wherein the behavior information comprises user profiles.
4. The method of claim 3 , wherein the user profiles comprise demographic information, wherein the determined similarity relates to the demographic information, and wherein identifying the one or more other media programs comprises identifying media programs that share particular characteristics relating to the demographic information.
5. The method of claim 2 , wherein identifying the one or more other media programs is further based on ratings information for the one or more other media programs.
6. The method of claim 2 , wherein the similarity between the user and the one or more of the plurality users is determined using information from at least one of search, maps, and shopping services.
7. The method of claim 2 , wherein determining a similarity between the user and one or more of the plurality of users comprises using a Bayesian Network or Bayesian clustering.
8. The method of claim 2 , wherein the behavior information comprises audience measurement data.
9. The method of claim 2 , wherein the behavior information comprises web activity data.
10. The method of claim 2 , wherein identifying the one or more other media programs is further based on a combination of genre data and ratings-based popularity data.
11. The method of claim 10 , wherein identifying the one or more other media programs further comprises ranking sets of similar programs by a popularity of stations or a popularity of programs.
12. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:
receiving information expressing a user's interest in one or more media programs;
training a recommendation engine by using behavior information from a plurality of users;
deriving, based on user profile information, a similarity between the user and one or more of the plurality of users;
identifying, using the recommendation engine, one or more other media programs that other users who share the similarity with the user have frequently presented in search submissions; and
generating and providing, for presentation to the user, a recommendation of the identified one or more other media programs.
13. The non-transitory computer-readable medium of claim 12 , wherein the behavior information comprises user profiles.
14. The non-transitory computer-readable medium of claim 13 , wherein the user profiles comprise demographic information, wherein the determined similarity relates to the demographic information, and wherein identifying the one or more other media programs comprises identifying media programs that share particular characteristics relating to the demographic information.
15. The non-transitory computer-readable medium of claim 12 , wherein the similarity between the user and the one or more of the plurality users is determined using information from at least one of search, maps, and shopping services.
16. The non-transitory computer-readable medium of claim 12 , wherein determining a similarity between the user and one or more of the plurality of users comprises using a Bayesian Network or Bayesian clustering.
17. The non-transitory computer-readable medium of claim 12 , wherein the behavior information comprises audience measurement data.
18. The non-transitory computer-readable medium of claim 12 , wherein the behavior information comprises web activity data.
19. The non-transitory computer-readable medium of claim 12 , wherein identifying the one or more other media programs is further based on a combination of genre data and ratings-based popularity data.
20. The non-transitory computer-readable medium of claim 19 , wherein identifying the one or more other media programs further comprises ranking sets of similar programs by a popularity of stations or a popularity of programs.