Systems and methods for identifying electronic content using video graphs
Systems and methods are provided for identifying and recommending electronic content to consumers. In accordance with an implementation, one or more elements of electronic content are identified based on video graph data. In an exemplary method, information associated with a first element of video content is received, and corresponding video graph data is obtained. One or more second elements of video content that are similar to the first element of video content are identified based on the obtained video graph data. A subset the first and second elements of video content is subsequently identified for delivery to the user.
1. An apparatus, comprising:
a storage device that stores a set of instructions; and
at least one processor coupled to the storage device and operative with the instructions to:
obtain video graph data for first video content accessed by a first user and second video content accessed by a second user;
generate updated video graph data indicative of a link between the first user and the second user based on an association between video consumption of the first user and video consumption of the second user;
compute one or more measures of similarity between the first video content and the second video content;
determine one or more weight factors for the one or more measures of similarity;
calculate a value indicative of the association between the video consumption of the first user and the second user based on the one or more weighted measures of similarity;
determine whether the calculated value exceeds a threshold value; and
generate the updated video graph data when the calculated value is determined to exceed the threshold value.
2. The apparatus of claim 1 , wherein the at least one processor is further operative with the instructions to:
identify additional video content based on the updated video graph data; and
transmit at least a portion of the additional video content to at least one of a first device of the first user and a second device of the second user.
3. The apparatus of claim 1 , wherein the video graph data comprises links between a first video clip of the first video content and at least a second video clip.
4. The apparatus of claim 1 , wherein the association between the video consumption of the first user and the video consumption of the second user is based on the obtained video graph data and video consumption information of the first user and the second user.
5. The apparatus of claim 4 , wherein the weight factors comprise at least one of a frequency of access for the video content, a time of day of access for the video content, and a date of access for the video content.
6. The apparatus of claim 4 , wherein the video consumption information for the first user and the second user comprises at least one of a list of accessed video content, a list of consumed video content, a frequency of accessed video content, and a frequency of consumed video content.
7. The apparatus of claim 1 , wherein the first user and the second user are associated with a social network.
8. The apparatus of claim 1 , wherein each of the first video content and the second video content comprises video clips.
9. A computer-implemented method comprising the following operations performed by at least one processor:
obtaining video graph data for first video content accessed by a first user and second video content accessed by a second user;
generating updated video graph data indicative of a link between the first user and the second user based on an association between video consumption of the first user and video consumption of the second user,
computing one or more measures of similarity between the first video content and the second video content;
determining one or more weight factors for the one or more measures of similarity;
calculating a value indicative of the association between the video consumption of the first user and the second user based on the one or more weighted measures of similarity;
determining whether the calculated value exceeds a threshold value; and
generating the updated video graph data when the calculated value is determined to exceed the threshold value.
10. The method of claim 9 , further comprising:
identifying additional video content based on the updated video graph data; and
transmitting at least a portion of the additional video content to at least one of a first device of the first user and a second device of the second user.
11. The method of claim 9 , wherein the video graph data comprises links between a first video clip of the first video content and at least a second video clip.
12. The method of claim 9 , wherein the association between the video consumption of the first user and the video consumption of the second user is based on the obtained video graph data and video consumption information of the first user and the second user.
13. The method of claim 9 , wherein the weight factors comprise at least one of a frequency of access for the video content, a time of day of access for the video content, and a date of access for the video content.
14. The method of claim 12 , wherein the video consumption information for the first user and the second user comprises at least one of a list of accessed video content and a frequency of accessed video content.
15. The method of claim 9 , wherein the first user and the second user are associated with a social network.
16. A tangible, non-transitory computer-readable medium that stores a set of instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
obtaining video graph data for first video content accessed by a first user and second video content accessed by a second user;
generating updated video graph data indicative of a link between the first user and the second user based on an association between video consumption of the first user and video consumption of the second user,
computing one or more measures of similarity between the first video content and the second video content;
determining one or more weight factors for the one or more measures of similarity;
calculating a value indicative of the association between the video consumption of the first user and the second user based on the one or more weighted measures of similarity;
determining whether the calculated value exceeds a threshold value; and
generating the updated video graph data when the calculated value is determined to exceed the threshold value.