OPTIMIZATION FILTERS FOR USER GENERATED CONTENT SEARCHES

• US 20100082627A1
• Filed: 09/24/2008
• Published: 04/01/2010
• Est. Priority Date: 09/24/2008
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Low, Biam Chee, Lai, Tzu-Chien (Reggie)
• Granted Patent: US 8,793,249 B2
• Time in Patent Office: Days
• US Class Current: 707/738
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

Embodiments are directed towards filtering from a user generated content (UGC) search result those documents determined to have insufficient subject matter specificity as defined by a training of a classification filter. The training comprises selecting a set of UGC that is definable as having sufficient subject matter specificity (a good set), and another set of UGC that is definable as having insufficient subject matter specificity (a bad set). The trained UGC classifier may examine search documents, and based on the documents having values above a defined threshold categorize the document as having sufficient subject matter specificity (or not). Those documents having insufficient subject matter specificity based on their determined thresholds may be filtered out of the submitted UGC search results. The documents remaining within the UGC search results may then be provided to a searcher for display at a client device.

Claims

1. A network device, comprising:

a transceiver to send and receive data over a network; and

a processor that is operative to perform actions, including;

determining a plurality of document features for each document in a plurality of documents, wherein the plurality of documents include at least one document defined as having sufficient subject matter specificity, and at least one other document having insufficient subject matter specificity;

training a classifier based at least on the plurality of document features;

providing to the trained classifier, at least one other document, wherein the trained classifier determines a quality value for the at least one other document; and

if the determined quality value of the at least one other document is above a quality threshold value, identifying the at least one other document to have sufficient subject matter specificity, and providing the at least one other document to a client device for display.

2. The network device of claim 1, wherein the plurality of document features comprises at least one of a word count, a number of phonetics, a frequency of special characters, a number of special characters, a frequency of visual line breaks, a frequency of images, or a frequency of function words, wherein the frequencies are determined based in part on the word count.

3. The network device of claim 1, wherein the classifier comprises a decision tree that is trained based on the plurality of document features for each document in the plurality of documents.

4. The network device of claim 1, wherein identifying the at least one other document to have sufficient subject matter specificity further comprises traversing a decision tree, and combining feature scores as the decision tree is traversed.

5. The network device of claim 1, wherein the at least one other document comprises user generated content documents obtained during a search query.

6. The network device of claim 1, wherein the processor that is operative to perform actions, including:

re-training the classifier based at least in part on user feedback.

7. A system that is operative to manage a search query, comprising:

a search tool configured to receive a search query request and to provide a plurality of documents in response; and

a document classifier that is configured to detect a document'"'"'s subject matter specificity based on a plurality of document features about the document, wherein the document classifier performs actions, including;

receiving a plurality of documents from the search tool; and

for each document in the plurality of documents;

extracting from each respective document, a plurality of document features;

employing a machine learning algorithm to generate a document quality feature score for the respective document based on the extracted document features; and

if the document quality feature score for the respective document is below a threshold value, removing the respective document from the received plurality of documents as indicating that the document has insufficient subject matter specificity; and

providing a resulting set of documents to a search requester based on each document quality feature score, such that the provided documents are determined to have sufficient subject matter specificity.

8. The system of claim 7, wherein the decision tree is generated based on a training process that comprises:

selecting a first set of documents that are identified as having sufficient subject matter specificity based at least on one feature characteristic of each document;

selecting a second set of documents that are identified as having insufficient subject matter specificity based at least on one feature characteristic of each document;

employing the first and second sets of documents to generate the machine learning algorithm.

9. The system of claim 7, wherein receiving a plurality of documents from the search tool further comprises receiving a plurality of documents associated with user generated content, wherein at least one document is associated with a blog site.

10. The system of claim 7, wherein the machine learning algorithm comprises at least one of a decision tree, naï

ve Bayes algorithm, Support-Vector machine, logistic regression algorithm, a perceptron, a Markovian discrimination based algorithm, or a neural network.

11. The system of claim 7, wherein the document features comprises at least one of a word count, a number of phonetics within a document, a frequency of special characters, a number of non-word symbols, a frequency of visual line breaks, a frequency of images, or a frequency of function words.

12. A computer-readable storage medium having computer-executable instructions, the computer-executable instructions when installed onto a computing device enable the computing device to perform actions, comprising:

receiving a plurality of documents in response to a search query; and

for each document in the plurality of documents;

extracting from each respective document, a plurality of document features;

employing a document classifier to generate a document quality feature score for the respective document based on the extracted document features;

if the document quality feature score for the respective document is below a threshold value, removing the respective document from the received plurality of documents as indicating that the document has insufficient subject matter specificity; and

providing a resulting set of documents to a search requester based on each document quality feature score, such that the provided documents are determined to have sufficient subject matter specificity.

13. The computer-readable storage medium of claim 12, wherein employing the document classifier further comprises:

traversing a decision tree that is generated based on a training process using a plurality of training documents, wherein each of training documents are labeled as to a respective quality for subject matter specificity; and

generating the overall document quality feature score based combining individual document feature scores determined as the decision tree is traversed.

14. The computer-readable storage medium of claim 13, wherein the training process further comprises:

extracting from each of the plurality of training documents at least one document feature;

labeling each training document based on a quality for subject matter specificity; and

generating the decision tree, including a decision flow, and feature scores using the labeled training documents and extracted document features.

15. The computer-readable storage medium of claim 12, wherein the document features comprise at least one of a word count, a number of phonetics within a document, a frequency of special characters, a number of non-word symbols, a frequency of visual line breaks, a frequency of images, or a frequency of function words.

16. The computer-readable storage medium of claim 12, wherein the search query is performed exclusively over user generated content sites.

17. A method of performing a search query, comprising:

receiving a plurality of documents in response to the search query over user generated content (UGC); and

for-each document in the plurality of documents;

extracting document features from each respective document useable to identify subject matter specificity of a UGC document;

employing a document classifier to generate a document quality feature score for the respective document based on the extracted document features; and

if the document quality feature score for the respective document is below a threshold value, removing the respective document from the received plurality of documents as indicating that the document has insufficient subject matter specificity; and

providing a resulting set of documents to a search requester based on each document quality feature score, such that the provided documents are determined to have sufficient subject matter specificity.

18. The method of claim 17, wherein the UGC further comprises content from at least one blog site.

19. The method of claim 17, wherein the document classifier comprises a machine learning algorithm that is trained based on another set of UGC documents, wherein each of UGC document in the other set are labeled with a subject matter specificity quality value.

20. The method of claim 17, further comprising:

receiving user feedback regarding a subject matter specificity of the result set of documents; and

employing the user feedback to modify the document classifier.