Systems and methods for filtering techniques using metadata and usage data analysis

1. A method for providing a recommendation based on a model representing similarity between a plurality of media assets, the method comprising:

receiving, using control circuitry, a first vector of values associated with a first media asset and a second vector of values associated with a second media asset;

determining, using the control circuitry, whether a user has viewed both the first media asset and the second media asset;

in response to determining that the user has viewed both the first media asset and the second media asset:

determining, using the control circuitry, a modeled similarity value representing modeled similarity between the first media asset and the second media asset, wherein the modeled similarity value is determined based on the first vector of values and the second vector of values;

retrieving, using the control circuitry, usage data for the first and second media assets, the usage data comprising at least one of: a rating from the user, an amount of time viewed by the user, a number of episodes watched by the user, and a number of related social media interactions by the user;

calculating, using the control circuitry, an observed similarity value representing observed similarity between the first media asset and the second media asset, wherein the observed similarity is based on the retrieved usage data for the first and second media assets;

determining, using the control circuitry, a modeling error value that minimizes an error metric computed based on a comparison of the modeled similarity value and the observed similarity value;

retrieving, using the control circuitry, a threshold error value associated with the model;

determining, using the control circuitry, whether the modeling error value is below the threshold error value;

in response to determining that the modeling error value is not below the threshold error value, updating, using the control circuitry, the first vector of values associated with the first media asset and the second vector of values associated with the second media asset based on the modeling error value; and

providing a media asset recommendation based on at least one of the first vector of values and the second vector of values.

2. The method of claim 1 , wherein the first vector of values associated with the first media asset includes one or more metadata-based values related to metadata for the first media asset and one or more free floating values unrelated to metadata for the first media asset.

3. The method of claim 2 , wherein updating the first vector of values associated with the first media asset includes updating at least one of the one or more free floating values and the one or more metadata-based values.

4. The method of claim 1 , wherein determining the modeling error value includes determining the modeling error value based on a confidence term, wherein a higher confidence term indicates a higher trust in the usage data.

5. The method of claim 1 , further comprising:

retrieving metadata for the first and second media assets, wherein the metadata for the first media asset includes at least one of genre, category, content source, title, series identifier, characteristic, actor, director, cast information, crew, plot, location, description, descriptor, keyword, artist, mood, tone, lyrics, comments, rating, length or duration, transmission time, availability time, and sponsor, and wherein the observed similarity is based on the retrieved metadata for the first and second media assets.

6. The method of claim 1 , wherein determining the modeled similarity value comprises:

determining, using the control circuitry, a distance between the first vector of values and the second vector of values based on a dot product between the first vector of values and the second vector of values; and

determining, using the control circuitry, the modeled similarity value based on the determined distance.

7. The method of claim 6 , wherein updating the first vector of values and second vector of values based on the modeling error value comprises:

adjusting, using the control circuitry, the values stored in the first vector and the second vector such that the distance between the first vector and the second vector is reduced.

8. The method of claim 1 , wherein the observed similarity is determined using Pearson correlation coefficient between the first media asset and the second media asset.

9. The method of claim 1 , further comprising:

in response to determining that no user has viewed both the first media asset and the second media asset, storing, using the control circuitry, a zero value for the modeling error value.

10. A system for providing a recommendation based on a model representing similarity between a plurality of media assets, the system comprising:

control circuitry configured to:

receive a first vector of values associated with a first media asset and a second vector of values associated with a second media asset;

determine whether a user has viewed both the first media asset and the second media asset;

in response to determining that the user has viewed both the first media asset and the second media asset:

determine a modeled similarity value representing modeled similarity between the first media asset and the second media asset, wherein the modeled similarity value is determined based on the first vector of values and the second vector of values;

retrieve usage data for the first and second media assets, the usage data comprising at least one of: a rating from the user, an amount of time viewed by the user, a number of episodes watched by the user, and a number of related social media interactions by the user;

calculate an observed similarity value representing observed similarity between the first media asset and the second media asset, wherein the observed similarity is based on the retrieved usage data for the first and second media assets;

determine a modeling error value that minimizes an error metric computed based on a comparison of the modeled similarity value and the observed similarity value;

retrieve a threshold error value associated with the model;

determine whether the modeling error value is below the threshold error value;

in response to determining that the modeling error value is not below the threshold error value, update the first vector of values associated with the first media asset and the second vector of values associated with the second media asset based on the modeling error value; and

provide a media asset recommendation based on at least one of the first vector of values and the second vector of values.

11. The system of claim 10 , wherein the first vector of values associated with the first media asset includes one or more metadata-based values related to metadata for the first media asset and one or more free floating values unrelated to metadata for the first media asset.

12. The system of claim 11 , wherein control circuitry configured to update the first vector of values associated with the first media asset includes control circuitry configured to update at least one of the one or more free floating values and the one or more metadata-based values.

13. The system of claim 10 , wherein control circuitry configured to determine the modeling error value includes control circuitry configured to determine the modeling error value based on a confidence term, wherein a higher confidence term indicates a higher trust in the usage data.

14. The system of claim 10 , wherein the control circuitry is configured to:

retrieving metadata for the first and second media assets, wherein the metadata for the first media asset includes at least one of genre, category, content source, title, series identifier, characteristic, actor, director, cast information, crew, plot, location, description, descriptor, keyword, artist, mood, tone, lyrics, comments, rating, length or duration, transmission time, availability time, and sponsor, and wherein the observed similarity is based on the retrieved metadata for the first and second media assets.

15. The system of claim 10 , wherein control circuitry configured to determine the modeled similarity value comprises control circuitry configured to:

determine a distance between the first vector of values and the second vector of values based on a dot product between the first vector of values and the second vector of values; and

determine the modeled similarity value based on the determined distance.

16. The system of claim 15 , wherein control circuitry configured to update the first vector of values and second vector of values based on the modeling error value comprises control circuitry configured to:

adjust the values stored in the first vector and the second vector such that the distance between the first vector and the second vector is reduced.

17. The system of claim 10 , wherein the observed similarity is determined using Pearson correlation coefficient between the first media asset and the second media asset.

18. The system of claim 10 , further comprising control circuitry configured to:

in response to determining that no user has viewed both the first media asset and the second media asset, store a zero value for the modeling error value.