TRUST PROPAGATION THROUGH BOTH EXPLICIT AND IMPLICIT SOCIAL NETWORKS

1 . A method implemented on a machine having at least one processor, storage, and a communication platform capable of connecting to a network for generating an implicit relationship, comprising:

obtaining a content item;

receiving a first user profile including information about one or more activities of a first user with respect to the content item;

receiving a second user profile including information about one or more activities of a second user with respect to the content item;

determining a similarity with respect to the content item between the first user and the second user based on the first and the second user profiles; and

generating an implicit relationship between the first user and the second user based on the similarity and a threshold, wherein a ranking of a second content item determined based on the second user profile and an evaluation of the second user from other users implicitly related to the second user is to be used, based on the implicit relationship, to rank the second content item in a search result provided to the first user.

2 . The method of claim 1 , further comprising:

calculating a first feature vector for the first user based on the first user profile; and

calculating a second feature vector for the second user based on the second user profile.

3 . The method of claim 2 , wherein

calculating the first feature vector comprises calculating on the basis of a tag by the first user for the content item, and

calculating the first feature vector comprises calculating on the basis of a save by the first user of the content item.

4 . The method of claim 1 , wherein determining comprises determining if the first user and the second user are related in accordance with an implicit social network.

5 . The method of claim 2 , wherein the first feature vector indicates that the first user saved the content item and the second feature vector indicates that the second user saved the content item.

6 . The method of claim 2 , wherein the first feature vector indicates that the first user tagged the content item and the second feature vector indicates that the second user tagged the content item.

7 . The method of claim 2 , further comprising:

calculating a third feature vector for a third user;

comparing the first feature vector with the third feature vector to calculate a similarity value;

recording a relationship between the first user and the third user in the first user profile and a third user profile based upon the similarity value, wherein the similarity value exceeds the threshold;

retrieving the third user profile based on the recorded relationship between the first user and the third user, wherein both the second user profile and the third user profile can be used to rank search results provided to the first user.

8 . The method of claim 7 , wherein determining comprises determining if the first user and the third user are related in accordance with an implicit social network.

9 . A method implemented on a machine having at least one processor, storage, and a communication platform capable of connecting to a network for providing a ranked search result set, comprising:

obtaining a content item;

receiving a first user profile including information about a first set of activities of a first user with respect to the content item, wherein the first set of activities includes an annotation from the first user with respect to the content item;

receiving a second user profile including information about a second set of activities of a second user with respect to the content item, wherein the second set of activities includes an annotation from the second user with respect to the content item;

determining a similarity between the first user and the second user based on the first and the second sets of activities;

generating an implicit relationship between the first user and the second user based on the similarity and a threshold;

in response to a search request by the first user, determining a search result set that includes the content item;

ranking the search result set based on the second user profile when the similarity exceeds the threshold, wherein the ranking comprises:

determining a trust value for the second user based on the second user profile, wherein the trust value indicates a degree of trust with respect to the second user by one or more users each of which has an implicit relationship with the second user,

obtaining a rating of the content item by the second user, and

determining a rank of the content item in the search result set based on a product of the trust value and the rating; and

providing the ranked search result set to the first user.

10 . A machine-readable, non-transitory and tangible medium having data recorded thereon for generating an implicit relationship, the medium, when read by the machine, causes the machine to perform the following:

obtaining a content item;

receiving a first user profile including information about one or more activities of a first user with respect to the content item;

receiving a second user profile including information about one or more activities of a second user with respect to the content item;

determining a similarity with respect to the content item between the first user and the second user based on the first and the second user profiles; and

generating an implicit relationship between the first user and the second user based on the similarity and a threshold, wherein a ranking of a second content item determined based on the second user profile and an evaluation of the second user from other users implicitly related to the second user is to be used, based on the implicit relationship, to rank the second content item in a search result provided to the first user.

11 . The medium of claim 10 , wherein the medium, when read by the machine, further causes the machine to perform the following:

calculating a first feature vector for the first user based on the first user profile; and

calculating a second feature vector for the second user based on the second user profile.

12 . The medium of claim 11 , wherein

calculating the first feature vector comprises calculating on the basis of a tag by the first user for the content item, and

calculating the first feature vector comprises calculating on the basis of a save by the first user of the content item.

13 . The medium of claim 10 , wherein determining comprises determining if the first user and the second user are related in accordance with an implicit social network.

14 . The medium of claim 11 , wherein the first feature vector indicates that the first user saved the content item and the second feature vector indicates that the second user saved the content item.

15 . The medium of claim 11 , wherein the first feature vector indicates that the first user tagged the content item and the second feature vector indicates that the second user tagged the content item.

16 . The medium of claim 11 , wherein the medium, when read by the machine, further causes the machine to perform the following:

calculating a third feature vector for a third user;

comparing the first feature vector with the third feature vector to calculate a similarity value;

recording a relationship between the first user and the third user in the first user profile and a third user profile based upon the similarity value, wherein the similarity value exceeds the threshold;

retrieving the third user profile based on the recorded relationship between the first user and the third user, wherein both the second user profile and the third user profile can be used to rank search results provided to the first user.

17 . The medium of claim 16 , wherein determining comprises determining if the first user and the third user are related in accordance with an implicit social network.