System and method for customizing search results from user's perspective

• US 9,886,514 B2
• Filed: 02/05/2016
• Issued: 02/06/2018
• Est. Priority Date: 12/01/2009
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Ghosh, Rishab Aiyer, Cui, Lun Ted
• Primary Examiner(s): ALAM, SHAHID AL
• Application Number:
  US15/017,517
• Publication Number:
  US 20160232248A1
• Time in Patent Office: 732 Days
• Field of Search: 707706
• CPC Class Codes: G06F 16/10 File systems; File servers
• CPC Class Codes: G06F 16/2455 Query execution
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/382 using citations hypermedia G06F16/94
• CPC Class Codes: G06F 16/9038 Presentation of query results
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results

Abstract

A new approach is proposed that contemplates systems and methods to generate customized subjective search results from the perspective of a user who conducts the search or any other subject entity of chosen by the user. A scored subject list is created from the user'"'"'s network of sources/subjects/contacts, where each element on the list is a subject/source and the score reflects the subject'"'"'s potential influence or closeness of its connection/relation with the user. Once created, the subject list is then used as a bias filter on the list of citations from search results. With such influence-weighted citation scores, objects and/or subjects from citations of subjects that have big influence on or enjoy high respect from the user will be ranked prominently in the search result presented to the user, thus biasing the search results from the user'"'"'s perspective.

Claims

1. A non-transitory machine-readable medium which when executed by a data processing device to perform a method, the method comprising:

receiving a search query from a user;

retrieving a plurality of citations, wherein content from each of the plurality of citations matches the search query and the plurality of citations cites a plurality of objects;

generating a subject list that includes a plurality of subjects, wherein each of the plurality of subjects are connected to the user either directly or indirectly in an influence network of a user;

calculating an influence score for each of the plurality of subjects based at least on a distance on a path from the user to that subject in the influence network and each subject is a representation of a different user in the influence network;

ranking the plurality of objects using a bias filter that includes a ranking function based on at least the influence scores of the subjects of each matching citation, wherein the influence scores are obtained from the user'"'"'s subject list for those citing subjects that are on the list; and

selecting a subset of objects from the ranked plurality of objects as the search results for presentation to the user.

2. The machine-readable medium of claim 1, wherein the method further comprises:

enabling a citation centric search process that focuses on influence of the plurality subjects that cite the plurality of objects.

3. The machine-readable medium of claim 1, wherein the method further comprises:

accepting and enforcing the searching query on citation searching, retrieving and ranking, each of which is either be explicitly described by a user or best guessed by a system based on internal statistical data.

4. The machine-readable medium of claim 1, wherein the method further comprises:

creating the subject list by flattening the user'"'"'s influence network of subjects up to a given number of degrees, levels, or depth.

5. The machine-readable medium of claim 4, wherein the method further comprises:

calculating the influence score of a subject based on one or more of;

influence of the subject on and/or respect from the user and number and length of paths from the subject to the user in the influence network.

6. The machine-readable medium of claim 1, wherein the method further comprises:

assigning a low score to the citation during ranking if a subject of a matching citation does not appear on the user'"'"'s subject list.

7. The machine-readable medium of claim 1, wherein the method further comprises:

each of the plurality of citations describes an opinion by a subject on an object matching the searching query in the search result.

8. The machine-readable medium of claim 1, wherein the method further comprises:

each of the plurality of citations includes one or more of;

expression of opinions on the objects, expressions of authors in the form of Tweets, blog posts, reviews of objects on Internet web sites Wikipedia entries, postings to social media, postings to websites, postings in the form of reviews, recommendations, or any other form of citation made to mailing lists, newsgroups, discussion forums, comments to websites or any other form of Internet publication.

9. A method comprising:

receiving a search query from a user;

retrieving a plurality of citations, wherein content from each of the plurality of citations matches the search query and the plurality of citations cites a plurality of objects;

generating a subject list that includes a plurality of subjects, wherein each of the plurality of subjects are connected to the user either directly or indirectly in an influence network of a user and each subject is a representation of a different user in the influence network;

calculating an influence score for each of the plurality of subjects based at least on a distance on a path from the user to that subject in the influence network and each subject is a representation of a different user in the influence network;

ranking the plurality of objects using a bias filter that includes ranking function based on at least the influence scores of the subjects of each matching citation, wherein the influence scores are obtained from the user'"'"'s subject list for those citing subjects that are on the list; and

selecting a subset of objects from the ranked plurality of objects as the search results for presentation to the user.

10. The method of claim 9, further comprising:

enabling a citation centric search process that focuses on influence of the plurality subjects that cite the plurality of objects.

11. The method of claim 9, further comprising:

accepting and enforcing the searching query on citation searching, retrieving and ranking, each of which is either be explicitly described by a user or best guessed by the system based on internal statistical data.

12. The method of claim 9, further comprising:

creating the subject list by flattening the user'"'"'s influence network of subjects up to a given number of degrees, levels, or depth.

13. The method of claim 9, further comprising:

calculating the influence score of a subject based on the one or more of;

influence of the subject on and/or respect from the user and number and length of paths from the subject to the user in a user'"'"'s influence network.

14. The method of claim 9, further comprising:

assigning a low score to the citation during ranking if a subject of a matching citation does not appear on the user'"'"'s subject list.

15. A device comprising:

a processor;

a memory coupled to the processor though a bus; and

a process executed from the memory by the processor that causes the processor to receive a search query from a user,

retrieve a plurality of citations, wherein content from each of the plurality of citations matches the search query and the plurality of citations cites a plurality of objects,

generate a subject list that includes a plurality of subjects, wherein each of the plurality of subjects are connected to the user either directly or indirectly in an influence network of a user,

calculate an influence score for each of the plurality of subjects based at least on a distance on a path from the user to that subject in the influence network and each subject is a representation of a different user in the influence network,

rank the plurality of objects using a bias filter that includes ranking function based on at least the influence scores of the subjects of each matching citation, wherein the influence scores are obtained from the user'"'"'s subject list for those citing subjects that are on the list, and

select a subset of objects from the ranked plurality of objects as the search results for presentation to the user.

16. The device of claim 15, wherein the process further causes the processor to enable a citation centric search process that focuses on influence of the plurality subjects that cite the plurality of objects.

17. The device of claim 15, wherein the process further causes the processor to accept and enforcing the searching query on citation searching, retrieving and ranking, each of which is either be explicitly described by a user or best guessed by the system based on internal statistical data.

18. The device of claim 15, wherein the process further causes the processor to create the subject list by flattening the user'"'"'s influence network of subjects up to a given number of degrees, levels, or depth, wherein the user'"'"'s influence network includes subjects that are connected to the user either directly or indirectly through other sources.