Ranking blog content

• US 20070038646A1
• Filed: 08/04/2005
• Published: 02/15/2007
• Est. Priority Date: 08/04/2005
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Thota, Chandrasekhar
• Granted Patent: US 7,421,429 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: Y10S 707/99931 Database or file accessing
• CPC Class Codes: Y10S 707/99937 Sorting
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

A mechanism of ranking weblog or “blog” items is provided. More particularly, the subject ranking mechanisms can facilitate ranking blog feeds and blog items contained therein thus focusing and intelligently delivering content (e.g., blog items) to users. The subject innovation facilitates ranking the blog feeds and blog items by creating a context rank around each blog feed. The context rank represents a sum of a context weight, a track-back weight and a comment weight. Accordingly, this context rank can allow readers to sort blog items in the order of popularity or importance thus effectively reducing content noise.

Claims

1. A system that facilitates ranking weblog (blog) item content, comprising:

a data reception component that receives data relating to a blog item;

an engine that constructs a weight relating to the data; and

a ranking component that ranks the blog item based at least in part upon the weight.

2. The system of claim 1, the weight includes a sum of a context weight, a track-back weight and a comment weight.

3. The system of claim 2, further comprising an analyzer component that identifies a location that relates to the blog item, the engine factors the location into the context weight.

4. The system of claim 3, the analyzer component identifies a language that relates to the blog item, the engine factors the language into the context weight.

5. The system of claim 4, the analyzer component identifies a temporal factor that relates to the blog item, the engine factors the temporal factor into the context weight.

6. The system of claim 1, further comprising an analyzer component that identifies at least one link to the blog item from a disparate source, the engine factors the at least one link into the track-back weight.

7. The system of claim 6, the track-back weight is calculated using an algorithm, the algorithm is

8. The system of claim 1, further comprising an analyzer component that identifies at least one comment associated to the blog item, the engine factors the at least one comment into the comment weight.

9. The system of claim 8, the comment weight is calculated using an algorithm, the algorithm is

10. The system of claim 1, further comprising a filtering component that facilitates excluding a subset of the data based at least in part upon the weight so as to facilitate elimination of content noise.

11. The system of claim 10, wherein the filtering component excludes the subset by utilizing an implicitly trained classifier that infers a type of blog item desired by a user.

12. The system of claim 11, further comprising a rendering component that outputs the subset to a target user.

13. A search engine comprising the system of claim 1.

14. A computer-readable medium having stored thereon computer-executable instructions for carrying out the system of claim 1.

15. A method for ranking blog content comprising:

receiving and recognizing a blog feed;

parsing the blog feed to identify a plurality of blog items;

obtaining a context criteria that corresponds to each of the blog items;

generating a weight that corresponds to each of the plurality of blog items, the weight is based at least in part upon the context criteria; and

ranking the plurality of blog items based at least in part upon the weight.

16. The method of claim 15, further comprising identifying at least one of a location, a language and temporal value that relates to each of the blog items and factoring the identified location, language and temporal value into a context weight, the context weight is factored into the weight.

17. The method of claim 16, further comprising calculating a track-back weight using an algorithm, the algorithm is

18. The method of claim 17, further comprising calculating a comment weight using an algorithm, the algorithm is

19. The method of claim 18, further comprising excluding a subset of the data based at least in part upon the weight so as to facilitate elimination of content noise.

20. A system that facilitates ranking blog item content, comprising;

means for receiving a blog item;

means for analyzing the blog item;

means for generating a context weight that corresponds to the blog item;

means for generating a track-back weight that corresponds to the blog item;

means for generating a comment weight that corresponds to the blog item;

means for generating a sum of the context weight, the track-back weight and the comment weight; and

means for ranking the sum against at least one disparate sum that corresponds to a disparate blog item.