Generate blog context ranking using track-back weight, context weight and, cumulative comment weight

• US 7,421,429 B2
• Filed: 08/04/2005
• Issued: 09/02/2008
• Est. Priority Date: 08/04/2005
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Thota, Chandrasekhar
• Primary Examiner(s): CHANNAVAJJALA, SRIRAMA T
• Application Number:
  US11/197,067
• Publication Number:
  US 20070038646A1
• Time in Patent Office: 1,125 Days
• Field of Search: 707/1, 707/3, 707 6- 7, 707/10, 707100-102, 707/104.1, 707/200, 707/204, 706 45- 50, 725/112, 709/203, 709219-220
• 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 computer readable storage medium comprising 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;

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, the track-back weight is calculated using

$w_{\mathrm{trackback}}=\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br><br><br>\frac{w_{\mathrm{trackback}}<br><br>i}{n},<br><br>\mathrm{where}<br><br><br><br>w_{\mathrm{trackback}}<br><br>i=c_{\mathrm{trackback}}\times <br><br>w_{\mathrm{site}}<br><br>i$

and n represents a number of links to the blog item, C_track-back is a constant for the track-back, the analyzer component further identifies at least one comment associated to the blog item, the engine factors the at least one comment into the comment weight, the comment weight is calculated using an

$w_{\mathrm{comment}}=\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>\frac{w_{\mathrm{comment}}<br><br>i}{n},\mathrm{where}<br><br><br><br>w_{\mathrm{comment}}<br><br>i=c_{\mathrm{comment}}<br><br>{\mathrm{xw}}_{\mathrm{time}}<br><br>i$

and n represents a number of comments on the blog item, C_comment is a constant for the comment; and

a ranking component that ranks the blog item based at least in part upon the comment weight and track-back weight.

2. The system of claim 1, further comprising an analyzer component that identifies a location that relates to the blog item, the engine factors the location into the context weight.

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

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

5. 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.

6. The system of claim 5, wherein the filtering component excludes the subset by utilizing an implicitly trained classifier that infers a type of blog item desired by a user.

7. The system of claim 6, further comprising a rendering component that outputs the subset to a target user.

8. The system of claim 1, wherein the engine is a search engine.

9. 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;

excluding a subset of the blog items based at least in part upon the weight so as to facilitate elimination of content noise;

calculating a track-back weight using

$w_{\mathrm{trackback}}=\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br><br><br>\frac{w_{\mathrm{trackback}}<br><br>i}{n},<br><br>\mathrm{where}<br><br><br><br>w_{\mathrm{trackback}}<br><br>i=c_{\mathrm{trackback}}\times <br><br>w_{\mathrm{site}}<br><br>i$

and n represents a number of links to a particular blog item, C_track-back is a constant for the track-back, the track-back weight is factored into the weight;

calculating a comment weight using

$w_{\mathrm{comment}}=\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>\frac{w_{\mathrm{comment}}<br><br>i}{n},\mathrm{where}<br><br><br><br>w_{\mathrm{comment}}<br><br>i=c_{\mathrm{comment}}<br><br>{\mathrm{xw}}_{\mathrm{time}}<br><br>i$

and n represents a number of comments on the blog item, C_comment is a constant for the comment, the comment weight is factored into the weight; and

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

10. The method of claim 9, 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.

11. A computer readable storage medium comprising 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 calculating a trackback weight using is

$w_{\mathrm{trackback}}=\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>\frac{w_{\mathrm{trackback}}<br><br>i}{n},\mathrm{where}<br><br><br><br>w_{\mathrm{trackback}}<br><br>i=c_{\mathrm{trackback}}<br><br>{\mathrm{xw}}_{\mathrm{time}}<br><br>i.$

and n represents a number of links to the blog item, C_track-back is a constant for the track-back;

means for generating a comment weight that corresponds to the blog item, calculating the comment weight using

$w_{\mathrm{comment}}=\underset{i-1}{\overset{n}{\sum <br><br>}}<br><br><br><br>\frac{w_{\mathrm{comment}}<br><br>i}{n},<br><br>\mathrm{where}<br><br><br><br>w_{\mathrm{comment}}<br><br>i=c_{\mathrm{comment}}\times <br><br>w_{\mathrm{time}}<br><br>i,$

n represents a number of comments on the blog item, C_comment is a constant for the comment;

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.