Recommendation network

• US 20100306194A1
• Filed: 06/07/2010
• Published: 12/02/2010
• Est. Priority Date: 08/19/2005
• Status: Active Grant

• Current Assignee: SitNet LLC
• Original Assignee: Evree, LLC
• Inventors: Evans, Matthew R.
• Granted Patent: US 8,090,673 B2
• Time in Patent Office: Days
• US Class Current: 707/733
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

A recommendation network is described. In some embodiments, the recommendation network includes recommenders that explicitly or implicitly recommend, rate or refer items and recommendation receivers that receive the recommendations. In some embodiments, the recommenders can be recommendation receivers, and vice versa. In some embodiments, recommendation receivers assign rust ratings to recommenders. The recommendation receiver can assign separate trust ratings to individual topics for which the recommendation receiver trusts the recommender. The separate trust ratings represent the recommendation receiver'"'"'s amount of trust in the recommender to makes valuable recommendations for the specific topic. The recommendation network can use the separate trust ratings, along with ratings provided by the recommender, to rank recommendations per the separate topics. The recommendation receiver can assign the recommender to different bundles, topics, channels, etc. to which other recommendation receivers can subscribe.

Claims

1. A system, comprising:

a server configured to

assign a trust rating, in response to input from a user source, to a recommender source specifically for a user-defined topic, wherein the trust rating represents a degree of trust that the user source has in the recommender source to provide content recommendations of value specifically for the user-defined topic, wherein the user-defined topic is also defined by the input from the user source,

determine a recommendation rating for a content recommendation provided by the recommender source, wherein the content recommendation is for content specified with the user-defined topic, and wherein the recommendation rating represents a degree of preference that the recommender source has for the content, and

calculate a score for the content recommendation by computing the recommendation rating with the trust rating; and

a client device configured to

receive the input from the user source,

provide the input to the server,

receive the score from the server, and

present the score on a display associated with the client device.

2. The system of claim 1, wherein the client device is further configured to

present the score for the content recommendation as an entry on a ranked list of a website.

3. The system of claim 1, wherein the server is further configured to

generate, in response to additional input provided by a user source, a recommendation container configured to receive the content recommendation from the recommender source, wherein the recommender source is within a social network of the user source, and wherein the recommendation container is user definable by the user source,

assign the user-defined topic to the recommendation container, wherein the user-defined topic is defined by the user source, and

associate, via the additional input by the user source, the recommender source with the recommendation container.

4. The system of claim 3, wherein the server is further configured to

receive an additional trust rating assigned specifically to the recommendation container, wherein the additional trust rating indicates an additional degree of trust that the user source has in the recommendation container, as an additional recommendation source, to provide recommendations of value on the user-defined topic, and

calculate the score for the content recommendation by computing the recommendation rating with the trust rating and with the additional trust rating.

5. The system of claim 3, where the server is further configured to

provide an instance of the recommendation container to an additional user source,

channel the content recommendation into the instance of the recommendation container,

determine an additional user-defined topic indicated by the additional user source,

assign the additional user-defined topic to the instance of the recommendation container,

receive an additional trust rating from the additional user source, wherein the additional trust rating indicates an additional degree of trust that the additional user source has in the recommendation container, as an additional recommendation source, to provide recommendations of value on the additional user-defined topic, and

calculate an additional score for the content recommendation channeled into the recommendation container by computing the recommendation rating with the trust rating and with the additional trust rating.

6. The system of claim 1, where the server is further configured to

receive a recommendation temporality rating, wherein the recommendation temporality rating indicates a user-defined, timeliness of relevance for the content recommendation, wherein the timeliness of the relevance is indicative of a perceived degree, by the user source, that the content is relevant over time,

assign the recommendation temporality rating to content recommendation, and

organize the content recommendation into a ranked list using the score and the recommendation temporality rating.

7. The system of claim 1, wherein the recommendation source represents a subset of social-network user entities linked to a website account for the user source.

8. A computer-implemented method, wherein one or more processors for a computer associated with a computerized social network perform operations comprising:

assigning a trust rating, in response to input from a user source, to a recommender source specifically for a user-defined topic, wherein the trust rating represents a degree of trust that the user source has in the recommender source to provide content recommendations of value specifically for the user-defined topic, wherein the user-defined topic is also defined by the input from the user source;

determining a recommendation rating for a content recommendation provided by the recommender source, wherein the content recommendation is for content specified with the user-defined topic, and wherein the recommendation rating represents a degree of preference that the recommender source has for the content; and

calculating a score for the content recommendation by computing the recommendation rating with the trust rating; and

ranking the content recommendation in a ranked list based on the score.

9. The computer-implemented method of claim 8 further comprising:

presenting the score for the content recommendation as an entry on a ranked list of a website.

10. The computer-implemented method of claim 8, further comprising:

generating, in response to additional input provided by a user source, a recommendation container configured to receive the content recommendation from the recommender source, wherein the recommender source is within a social network of the user source, and wherein the recommendation container is user definable by the user source;

assigning the user-defined topic to the recommendation container, wherein the user-defined topic is defined by the user source; and

associating, via the additional input by the user source, the recommender source with the recommendation container.

11. The computer-implemented method of claim 10, further comprising:

receiving an additional trust rating assigned specifically to the recommendation container, wherein the additional trust rating indicates an additional degree of trust that the user source has in the recommendation container, as an additional recommendation source, to provide recommendations of value on the user-defined topic; and

calculating the score for the content recommendation by computing the recommendation rating with the trust rating and with the additional trust rating.

12. The computer-implemented method of claim 10, further comprising:

providing an instance of the recommendation container to an additional user source;

channeling the content recommendation into the instance of the recommendation container;

determining an additional user-defined topic indicated by the additional user source;

assigning the additional user-defined topic to the instance of the recommendation container;

receiving an additional trust rating from the additional user source, wherein the additional trust rating indicates an additional degree of trust that the additional user source has in the recommendation container, as an additional recommendation source, to provide recommendations of value on the additional user-defined topic; and

calculating an additional score for the content recommendation channeled into the recommendation container by computing the recommendation rating with the trust rating and with the additional trust rating.

13. The computer-implemented method of claim 8, further comprising:

receiving a recommendation temporality rating, wherein the recommendation temporality rating indicates a user-defined, timeliness of relevance for the content recommendation, wherein the timeliness of the relevance is indicative of a perceived degree, by the user source, that the content is relevant over time;

assigning the recommendation temporality rating to content recommendation; and

organizing the content recommendation into a ranked list using the score and the recommendation temporality rating.

14. The computer-implemented method of claim 8 further comprising:

sorting the content recommendation based on the user-defined topic.

15. One or more computer readable storage media having instructions stored thereon, which when executed by a set of one or more processors causes the set of one or more processors to perform operations comprising:

assigning a trust rating, in response to input from a user source, to a recommender source specifically for a user-defined topic, wherein the trust rating represents a degree of trust that the user source has in the recommender source to provide content recommendations of value specifically for the user-defined topic, wherein the user-defined topic is also defined by the input from the user source;

determining a recommendation rating for a content recommendation provided by the recommender source, wherein the content recommendation is for content specified with the user-defined topic, and wherein the recommendation rating represents a degree of preference that the recommender source has for the content; and

calculating a score for the content recommendation by computing the recommendation rating with the trust rating;

receiving a search query by an additional user source for one or more key terms from the user-defined topic; and

ranking the content recommendation in a ranked list, based on the score, in response to the search query.

16. The one or more computer readable storage media of claim 15, the operations further comprising:

determining a topic description assigned by the recommender source to the content recommendation;

determining that the topic description matches the user-defined topic; and

ranking the content recommendation in a ranked list in part in response to determining the that the topic description matches the user-defined topic.

17. The one or more computer readable storage media of claim 15, the operations further comprising:

generating, in response to additional input provided by a user source, a recommendation container configured to receive the content recommendation from the recommender source, wherein the recommender source is within a social network of the user source, and wherein the recommendation container is user definable by the user source;

assigning the user-defined topic to the recommendation container, wherein the user-defined topic is defined by the user source; and

associating, via the additional input by the user source, the recommender source with the recommendation container.

18. The one or more computer readable storage media of claim 17, the operations further comprising:

receiving an additional trust rating assigned specifically to the recommendation container, wherein the additional trust rating indicates an additional degree of trust that the user source has in the recommendation container, as an additional recommendation source, to provide recommendations of value on the user-defined topic; and

calculating the score for the content recommendation by computing the recommendation rating with the trust rating and with the additional trust rating.

19. The one or more computer readable storage media of claim 17, the operations further comprising:

providing an instance of the recommendation container to an additional user source;

channeling the content recommendation into the instance of the recommendation container;

determining an additional user-defined topic indicated by the additional user source;

assigning the additional user-defined topic to the instance of the recommendation container;

receiving an additional trust rating from the additional user source, wherein the additional trust rating indicates an additional degree of trust that the additional user source has in the recommendation container, as an additional recommendation source, to provide recommendations of value on the additional user-defined topic; and

calculating an additional score for the content recommendation channeled into the recommendation container by computing the recommendation rating with the trust rating and with the additional trust rating.

20. The one or more computer readable storage media of claim 15, the operations further comprising:

filtering the ranked list based on the user-defined topic.