Visualization of reputation ratings

Patent metadata

Field	Value
Publication number	US-8977631-B2
Application number	US-83481707-A
Country	US
Kind code	B2
Filing date	Aug 7, 2007
Priority date	Apr 16, 2007
Publication date	Mar 10, 2015
Grant date	Mar 10, 2015

Abstract

In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

First claim

What is claimed is:

1. A method comprising:
receiving a request to retrieve a plurality of feedback entries about a particular user, the plurality of feedback entries submitted by a plurality of other users, the request including an identifier of the particular user;
retrieving the plurality of feedback entries in response to the request, a portion of the plurality of the feedback entries containing a first term, each retrieved feedback entry being about the particular user identified by the identifier in the request;
building a scoring model implemented by one or more processors, the scoring model based, in part, upon a term frequency count denoting a frequency with which the first term is associated with the particular user in the plurality of the feedback entries in a searchable data structure;
mapping, using the one or more processors, the first term to a graphical illustration, the mapping based upon a second term associated with the graphical illustration; and
generating a feedback page containing the first term and the graphical illustration.

2. The method of claim 1 , further comprising:
assigning a value to the first term so as to identify the first term;
assigning the first term to the searchable data structure; and
extracting the first term from the searchable data structure based, in part, upon an extraction rule.

3. The method of claim 1 , wherein the plurality of feedback entries includes at least one of positive, neutral, or negative feedback.

4. The method of claim 1 , further comprising highlighting the first term based upon a score related to the scoring model, the score being a product of the term frequency count and an inverse document frequency value.

5. The method of claim 4 , wherein the first term is part of at least one of a category level context, a global level context, a static domain level context, or a dynamic domain context.

6. The method of claim 4 , further comprising highlighting using a highlight including at least one of a font size highlight, a color highlight, a underline highlights, or a bold highlight.

7. The method of claim 1 , wherein the searchable data structure is at least one of a trie, hash table, red-black tree, or Binary Search Tree (BST).

8. The method of claim 1 , wherein the graphical illustration are emoticons.

9. The method of claim 1 , further comprising filtering a noise word from a feedback entry of the plurality of feedback entries.

10. The method of claim 1 , further comprising:
receiving an asynchronous feedback request generated, in part, through selecting a phrase appearing on the feedback page;
retrieving feedback information from a pool of feedback relating to the particular user; and
transmitting the feedback information to be displayed on the feedback page.

11. A computer system comprising:
a memory to store a plurality of feedback entries; and
a processor in electronic communication with the memory to implement:
a first receiver to receive a request to retrieve the plurality of feedback entries about a particular user, the plurality of feedback entries submitted by a plurality of other users, the request including an identifier of the particular user;
a first retriever to retrieve the plurality of feedback entries in response to the request, a portion of the plurality of feedback entries containing a first term, each retrieved feedback entry being about the particular user identified by the identifier in the request;
a score builder to build a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term is associated with the particular user in the plurality of the feedback entries in a searchable data structure;
a mapping engine to map the first term to a graphical illustration, the mapping based upon a second term associated with the graphical illustration; and
a page generator to generate a feedback page containing the first term and the graphical illustration.

12. The computer system of claim 11 , further comprising:
a first assigner to assign a value to the first term so as to identify the first term;
a second assigner to assign the first term to the searchable data structure; and
a term extractor to extract the first term from the searchable data structure based, in part, upon an extraction rule.

13. The computer system of claim 11 , wherein the plurality of the feedback entries includes at least one of positive, neutral, or negative feedback.

14. The computer system of claim 11 , further comprising a highlighter engine to highlight the first term based upon a score related to the scoring model, the score being a product of the term frequency count and an inverse document frequency value.

15. The computer system of claim 14 , wherein the first term is part of at least one of a group consisting of category level context, a global level context, a static domain level context, and a dynamic domain context.

16. The computer system of claim 14 , further comprising the highlighter engine using a highlight including at least one of a group of highlights consisting of font size highlight, a color highlight, a underline highlights, and a bold highlight.

17. The computer system of claim 11 , wherein the searchable data structure is at least one of a group consisting of trie, hash table, red-black tree, and Binary Search Tree (BST).

18. The computer system of claim 11 , wherein the graphical illustration is an emoticon.

19. The computer system of claim 11 , further comprising a filter to filter a noise word from one feedback entry of the plurality of the feedback entries.

20. The computer system of claim 11 , further comprising:
a second receiver to receive an asynchronous feedback request generated, in part, through selecting a phrase appearing on the feedback page;
a second retriever to retrieve feedback information from a pool of feedback relating to the particular user; and
a transmitter to transmit the feedback information to be displayed on the feedback page.

21. An apparatus comprising:
means for receiving a request to retrieve a plurality of feedback entries about a particular user, the feedback entries submitted by other users, the request including an identifier of the particular user;
means for retrieving the plurality of feedback entries in response to the request, a portion of the plurality of the feedback entries each containing a first term, each retrieved feedback entry being about the particular user identified by the identifier in the request;
means for building a scoring model implemented by one or more processors, the scoring model based, in part, upon a term frequency count denoting a frequency with which the first term is associated with the particular user in the plurality of the feedback entries in a searchable data structure;
means for mapping, using the one or more processors, the first term to a graphical illustration, the mapping based upon a second term associated with the graphical illustration; and
means for generating a feedback page containing the first term and the graphical illustration.

22. A non-transitory machine-readable storage medium comprising instructions, which when implemented by one or more machines, cause the one or more machines to perform operations comprising:
receiving a request to retrieve a plurality of feedback entries about a particular user, the plurality of feedback entries submitted by a plurality of other users, the request including an identifier of the particular user;
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Assignees

• Sundaresan Neelakantan
• Ganesan Kavita
• Deo Harshal Ulhas
• Ebay Inc

Inventors

• Sundaresan Neelakantan
• Ganesan Kavita
• Deo Harshal Ulhas

Classifications

• G06Q30/0282 Primary

  Rating or review of business operators or products · CPC title

• G06F16/248

  Presentation of query results · CPC title

• G06F40/30

  Semantic analysis · CPC title

• G06F16/287

  Visualization; Browsing · CPC title

• G06F16/3326

  using relevance feedback from the user, e.g. relevance feedback on documents, documents sets, document terms or passages · CPC title