US2013275235A1PendingUtilityA1
Using linear and log-linear model combinations for estimating probabilities of events
Est. expiryJul 21, 2030(~4 yrs left)· nominal 20-yr term from priority
G06Q 30/0241G06Q 10/04G06Q 30/0277
61
PatentIndex Score
0
Cited by
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References
0
Claims
Abstract
A system for determining predictive models associated with online advertising can include a communications interface, a processor, and a display. The communications interface can be configured to receive a partial dataset. The partial dataset may include user information. The processor can be communicatively coupled to the communications interface and configured to identify the partial dataset. The processor can also be configured to determine a first predictive model corresponding to at least part of the partial dataset and a second predictive model by combining a probability distribution with the first predictive model. The display can be communicatively coupled to the processor and configured to display the second predictive model.
Claims
exact text as granted — not AI-modifiedWe claim:
1 . A system for determining predictive models associated with online advertising, comprising:
a communications interface configured to receive a partial dataset, the partial dataset including user information; a processor, communicatively coupled to the communications interface, configured to: identify the partial dataset; determine a first predictive model corresponding to at least part of the partial dataset; and determine a second predictive model by combining a probability distribution with the first predictive model; and a display, communicatively coupled to the processor, configured to display the second predictive model.
2 . The system of claim 1 , wherein the probability distribution is a weighted distribution model.
3 . The system of claim 2 , wherein the weighted distribution model is a log-linear combination using maximum-entropy weighting.
4 . The system of claim 2 , wherein the weighted distribution model is a linear combination using uniform average weighting.
5 . The system of claim 1 , wherein the user information includes user interests.
6 . The system of claim 1 , where the user information includes user demographics.
7 . The system of claim 1 , where the user information includes user web browsing behaviors.
8 . The system of claim 7 , where the web browsing behaviors include historical data regarding one or more of user queries, click-throughs, or other measurable web browsing events.
9 . The system of claim 1 , wherein the second predictive model (p(c|x)) uses the formula:
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10 . The system of claim 1 , wherein the second predictive model (p(c|x)) uses the formula:
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11 . The system of claim 1 , wherein the determination of the first predictive model includes training a predictive model using flat weighting.
12 . The system of claim 1 , wherein the processor is further configured to partition the user information into categories.
13 . The system of claim 12 , wherein the categories include one or more of user interests, user demographics, and user web browsing behaviors.
14 . A method for determining predictive models associated with online advertising, comprising:
receiving, at a communications interface, a partial dataset including user information; identifying the partial dataset by a processor communicatively coupled to the communications interface; determining, by the processor, a first predictive model corresponding to at least part of the partial dataset; and determining, by the processor, a second predictive model by combining a probability distribution with the first predictive model.
15 . The method of claim 14 , further comprising displaying the second predictive model at a display communicatively coupled to the processor.
16 . The method of claim 14 , wherein the probability distribution is a weighted distribution model or a log-linear combination using maximum-entropy weighting, and wherein the weighted distribution model is a linear combination using uniform average weighting.
17 . The method of claim 14 , wherein the user information includes one or more of user interests, user demographics, and user web browsing behaviors, and wherein the web browsing behaviors include historical data regarding one or more of user queries, click-throughs, or other measurable web browsing events.
18 . The method of claim 14 , wherein the second predictive model (p(c|x)) uses the formula:
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19 . The method of claim 14 , wherein the processor is further configured to partition the user information into categories, and wherein the categories include one or more of user interests, user demographics, and user web browsing behaviors.
20 . A non-transitory computer readable medium for determining predictive models associated with online advertising, comprising:
instructions executable by a processor to receive a partial dataset, the partial dataset including user information; instructions executable by a processor to identify the partial dataset; instructions executable by a processor to determine a first predictive model corresponding to at least part of the partial dataset; and instructions executable by a processor to determine a second predictive model by combining a probability distribution with the first predictive model.Join the waitlist — get patent alerts
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