SYSTEMS, METHODS, AND MEDIA FOR RATING WEBSITES FOR SAFE ADVERTISING
Systems, methods, and media for rating websites for safe advertising are provided. In accordance with some embodiments of the disclosed subject matter, the method comprises: receiving a uniform resource locator corresponding to a webpage; selecting a plurality of evidentiary sources for obtaining evidence relating to the uniform resource locator, wherein each piece of evidence corresponds to one of the plurality of evidentiary sources; converting each piece of evidence obtained from the plurality of evidentiary sources into a plurality of instances that describe the webpage; applying the plurality of instances to a plurality of rating models, wherein each of the plurality of rating models generates an ordinomial and wherein the ordinomial encodes a probability of membership in one or more severity classes of a category; combining the ordinomial from each of the plurality of rating models into a combined ordinomial probability estimate; and generating a rating for the webpage based at least in part on the combined ordinomial probability estimate, wherein the rating identifies whether the webpage is likely to contain objectionable content of the category.
1 . A method for rating webpages for safe advertising, the method comprising:
receiving a uniform resource locator corresponding to a webpage;
selecting a plurality of evidentiary sources for obtaining evidence relating to the uniform resource locator, wherein each piece of evidence corresponds to one of the plurality of evidentiary sources;
converting each piece of evidence obtained from the plurality of evidentiary sources into a plurality of instances that describe the webpage;
applying the plurality of instances to a plurality of rating models, wherein each of the plurality of rating models generates an ordinomial and wherein the ordinomial encodes a probability of membership in one or more severity classes of a category;
combining the ordinomial from each of the plurality of rating models into a combined ordinomial probability estimate; and
generating a rating for the webpage based at least in part on the combined ordinomial probability estimate, wherein the rating identifies whether the webpage is likely to contain objectionable content of the category.
2 . The method of claim 1 , wherein the plurality of evidentiary sources are selected based at least in part on a budget parameter.
3 . The method of claim 2 , further comprising determining an optimized subset of evidentiary sources based at least in part on the plurality of evidentiary sources, the uniform resource locator, and the budget parameter.
4 . The method of claim 1 , further comprising merging each piece of evidence obtained from the plurality of evidentiary sources into a page object associated with the uniform resource locator.
5 . The method of claim 4 , further comprising receiving feedback relating to the evidence obtained from the plurality of evidentiary sources, wherein additional evidence is collected in response to receiving the feedback and wherein a revised page objected is created.
6 . The method of claim 1 , wherein each instance maps facets from the obtained evidence with a particular feature.
7 . The method of claim 1 , wherein the plurality of rating models are modular such that a rating model can be inserted and removed from the plurality of rating models applied to the plurality of instances.
8 . The method of claim 1 , wherein the category includes at least one of: adult content, guns, bombs, ammunition, alcohol, drugs, tobacco, offensive language, hate speech, obscenities, gaming, gambling, entertainment, spyware, malicious code, and illegal content.
9 . The method of claim 1 , further comprising:
generating an ordinomial distribution that includes each ordinomial for the one or more severity classes;
receiving a confidence parameter; and
removing at least one of the one or more severity classes based at least in part on the confidence parameter.
10 . The method of claim 1 , further comprising applying weights to each piece of evidence obtained from the plurality of evidentiary sources.
11 . The method of claim 1 , further comprising applying weights to each of the plurality of rating models.
12 . The method of claim 1 , further comprising training at least one of the plurality of rating models with labeling instances.
13 . The method of claim 1 , further comprising:
using the plurality of rating models to assign a utility to unlabeled instances; and
transmitting unlabeled instances having an assigned utility that is greater than a predetermined value to an oracle for labeling.
14 . The method of claim 1 , further comprising:
receiving a plurality of uniform resource locators associated with a plurality of webpages; and
generating a priority list of the plurality of uniform resource locators, wherein the priority list is generated based on one of: frequency of each uniform resource locator in an advertisement stream, frequency of changes on the webpage associated with each uniform resource locator, page popularity of each uniform resource locator, and a utility estimate of each uniform resource locator.
15 . A system for rating webpages for safe advertising, the system comprising:
a processor that:
receives a uniform resource locator corresponding to a webpage;
selects a plurality of evidentiary sources for obtaining evidence relating to the uniform resource locator, wherein each piece of evidence corresponds to one of the plurality of evidentiary sources;
converts each piece of evidence obtained from the plurality of evidentiary sources into a plurality of instances that describe the webpage;
applies the plurality of instances to a plurality of rating models, wherein each of the plurality of rating models generates an ordinomial and wherein the ordinomial encodes a probability of membership in one or more severity classes of a category;
combines the ordinomial from each of the plurality of rating models into a combined ordinomial probability estimate; and
generates a rating for the webpage based at least in part on the combined ordinomial probability estimate, wherein the rating identifies whether the webpage is likely to contain objectionable content of the category.
16 . The system of claim 15 , wherein the plurality of evidentiary sources are selected based at least in part on a budget parameter.
17 . The system of claim 16 , wherein the processor is further configured to determine an optimized subset of evidentiary sources based at least in part on the plurality of evidentiary sources, the uniform resource locator, and the budget parameter.
18 . The system of claim 15 , the processor is further configured to merge each piece of evidence obtained from the plurality of evidentiary sources into a page object associated with the uniform resource locator.
19 . The system of claim 18 , the processor is further configured to receive feedback relating to the evidence obtained from the plurality of evidentiary sources, wherein additional evidence is collected in response to receiving the feedback and wherein a revised page objected is created.
20 . The system of claim 15 , wherein each instance maps facets from the obtained evidence with a particular feature.
21 . The system of claim 15 , wherein the plurality of rating models are modular such that a rating model can be inserted and removed from the plurality of rating models applied to the plurality of instances.
22 . The system of claim 15 , wherein the category includes at least one of: adult content, guns, bombs, ammunition, alcohol, drugs, tobacco, offensive language, hate speech, obscenities, gaming, gambling, entertainment, spyware, malicious code, and illegal content.
23 . The system of claim 15 , the processor is further configured to:
generate an ordinomial distribution that includes each ordinomial for the one or more severity classes;
receive a confidence parameter; and
remove at least one of the one or more severity classes based at least in part on the confidence parameter.
24 . The system of claim 15 , the processor is further configured to apply weights to each piece of evidence obtained from the plurality of evidentiary sources.
25 . The system of claim 15 , the processor is further configured to apply weights to each of the plurality of rating models.
26 . The system of claim 15 , the processor is further configured to train at least one of the plurality of rating models with labeling instances.
27 . The system of claim 15 , the processor is further configured to:
use the plurality of rating models to assign a utility to unlabeled instances; and
transmit unlabeled instances having an assigned utility that is greater than a predetermined value to an oracle for labeling.
28 . The system of claim 15 , the processor is further configured to:
receive a plurality of uniform resource locators associated with a plurality of webpages; and
generate a priority list of the plurality of uniform resource locators, wherein the priority list is generated based on one of: frequency of each uniform resource locator in an advertisement stream, frequency of changes on the webpage associated with each uniform resource locator, page popularity of each uniform resource locator, and a utility estimate of each uniform resource locator.
29 . A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for rating webpages for safe advertising, the method comprising:
receiving a uniform resource locator corresponding to a webpage;
selecting a plurality of evidentiary sources for obtaining evidence relating to the uniform resource locator, wherein each piece of evidence corresponds to one of the plurality of evidentiary sources;
converting each piece of evidence obtained from the plurality of evidentiary sources into a plurality of instances that describe the webpage;
applying the plurality of instances to a plurality of rating models, wherein each of the plurality of rating models generates an ordinomial and wherein the ordinomial encodes a probability of membership in one or more severity classes of a category;
combining the ordinomial from each of the plurality of rating models into a combined ordinomial probability estimate; and
generating a rating for the webpage based at least in part on the combined ordinomial probability estimate, wherein the rating identifies whether the webpage is likely to contain objectionable content of the category.