Method and apparatus for creating user-generated document feedback to improve search relevancy

1 . A method for improving relevancy of online search results, comprising:

collecting highlighted phrases from users who review one or more documents at one or more websites;

aggregating the highlighted phrases about the one or more documents in a distributed hash table;

ranking relevancy of the highlighted phrases according to frequency of occurrences of similar phrases;

generating search relevancy data to be used by a search relevancy algorithm of a search engine; and

generating search results in response to a search query using the search relevancy data.

2 . The method of claim 1 , wherein collecting highlighted phrases comprises:

installing a client application at a plurality of user devices;

monitoring users' activities while viewing the one or more documents at the one or more websites;

retrieving highlighted phrases and their corresponding metadata;

sending the highlighted phrases and their corresponding metadata to a set of servers for processing and storage.

3 . The method of claim 2 further comprising:

sending client identifiers and universal resources indicators of the documents to the set of servers for processing and storage.

4 . The method of claim 1 , wherein an entry to the distributed hash table comprises:

a universal resource indicator;

one or more highlighted phrases collected from the plurality of users; and

a rank of relevancy for each of the highlighted phrases according to a count of number of times the phrase being highlighted.

5 . The method of claim 1 , wherein aggregating the highlighted phrases comprises:

determining whether a similar highlighted phrase already exists in the distributed hash table; and

incrementing a count of number of times the highlighted phrase in response to the highlighted phrase already exists in the distributed hash table.

6 . The method of claim 5 , wherein aggregating the highlighted phrases further comprises:

pruning phrases having low frequency count from the distributed hash table according to a predetermined threshold of frequency counts during a predetermined period of time.

7 . The method of claim 1 , wherein aggregating the highlighted phrases comprises:

determining whether a similar highlighted phrase already exists in the distributed hash table; and

adding the highlighted phrase to the distributed hash table in response to the highlighted phrase not being found in the distributed hash table.

8 . The method of claim 1 , wherein ranking relevancy of the highlighted phrases comprises:

promoting relevancy of a phrase in accordance with its corresponding frequency of occurrence in the distributed hash table.

9 . A computer program product for improving relevancy of online search results, comprising a medium storing computer programs for execution by one or more computer systems, the computer program product comprising:

code for collecting highlighted phrases from users who review one or more documents at one or more websites;

code for aggregating the highlighted phrases about the one or more documents in a distributed hash table;

code for ranking relevancy of the highlighted phrases according to frequency of occurrences of similar phrases;

code for generating search relevancy data to be used by a search relevancy algorithm of a search engine; and

code for generating search results in response to a search query using the search relevancy data.

10 . The computer program product of claim 9 , wherein the code for collecting highlighted phrases comprises:

code for installing a client application at a plurality of user devices;

code for monitoring users' activities while viewing the one or more documents at the one or more websites;

code for retrieving highlighted phrases and their corresponding metadata;

code for sending the highlighted phrases and their corresponding metadata to a set of servers for processing and storage.

11 . The computer program product of claim 10 further comprising:

code for sending client identifiers and universal resources indicators of the documents to the set of servers for processing and storage.

12 . The computer program product of claim 9 , wherein an entry to the distributed hash table comprises:

a universal resource indicator;

one or more highlighted phrases collected from the plurality of users; and

a rank of relevancy for each of the highlighted phrases according to a count of number of times the phrase being highlighted.

13 . The computer program product of claim 9 , wherein the code for aggregating the highlighted phrases comprises:

code for determining whether a similar highlighted phrase already exists in the distributed hash table; and

code for incrementing a count of number of times the highlighted phrase in response to the highlighted phrase already exists in the distributed hash table.

14 . The computer program product of claim 13 , wherein the code for aggregating the highlighted phrases further comprises:

code for pruning phrases having low frequency count from the distributed hash table according to a predetermined threshold of frequency counts during a predetermined period of time.

15 . The computer program product of claim 9 , wherein the code for aggregating the highlighted phrases comprises:

code for determining whether a similar highlighted phrase already exists in the distributed hash table; and

code for adding the highlighted phrase to the distributed hash table in response to the highlighted phrase not being found in the distributed hash table.

16 . The computer program product of claim 9 , wherein the code for ranking relevancy of the highlighted phrases comprises:

code for promoting relevancy of a phrase in accordance with its corresponding frequency of occurrence in the distributed hash table.