Adaptive TV program recommender
First Claim
Patent Images
1. A data processing device comprising:
- at least one input for receiving data includingviewer profile data; and
data regarding a television program;
a medium readable by the data processing device coupled to the input, said medium storing said viewer profile data; and
a processor, the processor being adapted to perform the following;
calculating a probability that the television program is a desired one; and
supplying a recommendation regarding the television program based on the probability,wherein the processor maintains the viewer profile in accordance with a data structure comprising;
a list of feature values; and
for each element of the list, a respective number of times programs having that feature value were watched, and a respective number of times programs having that feature value were not watched,and wherein the processor is further arranged to perform the following, each time a user watches a new program,first adding, to the list, feature values or counts of such feature values, associated with that new program;
selecting at least one companion program to the new program, the companion program being selected at random from a program schedule, which companion program has not been watched; and
second adding, to the list, feature values of the companion program, or counts of such feature values.
4 Assignments
0 Petitions
Accused Products
Abstract
A system for recommending television programs makes use of probabilistic calculations and a viewer profile to create a recommendation. The probabilistic calculations preferably are in the form of Bayesian classifier theory. Modifications to classical Bayesian classifier theory are proposed.
368 Citations
36 Claims
-
1. A data processing device comprising:
-
at least one input for receiving data including viewer profile data; and data regarding a television program; a medium readable by the data processing device coupled to the input, said medium storing said viewer profile data; and a processor, the processor being adapted to perform the following; calculating a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the processor maintains the viewer profile in accordance with a data structure comprising; a list of feature values; and for each element of the list, a respective number of times programs having that feature value were watched, and a respective number of times programs having that feature value were not watched, and wherein the processor is further arranged to perform the following, each time a user watches a new program, first adding, to the list, feature values or counts of such feature values, associated with that new program; selecting at least one companion program to the new program, the companion program being selected at random from a program schedule, which companion program has not been watched; and second adding, to the list, feature values of the companion program, or counts of such feature values. - View Dependent Claims (2, 3, 4, 5)
-
-
6. A data processing device comprising:
-
at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following; calculating, using a Bayesian classifier, a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the processor is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein the viewer profile data comprises a list of feature values; a respective negative count for each element of the list, the negative count indicating a number of times programs having that feature value have not been watched; a respective positive count for each element of the list, the positive count indicating a number of times programs having that feature value have been watched; the noise threshold calculation comprises selecting a sub-list comprising at least feature values having at least one specific type of feature; choosing the highest negative count in the sub-list as the noise threshold; the recommendation comprises a program selected from a group having at least one feature value having a positive or negative count in the viewer profile, which count exceeds the noise threshold. - View Dependent Claims (7, 8, 9, 10, 11)
-
-
12. A data processing device comprising:
-
at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following; calculating, using a Bayesian classifier, a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the processor is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein subjecting the viewer profile to the noise threshold further comprises using observations gathered by a known random process to estimate a reasonable noise threshold.
-
-
13. A data processing device comprising:
-
at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following; calculating a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein calculating the probability comprises; computing a prior possibility, of whether a program is desired or not; computing a conditional probability of whether a feature fi will be present if a show is desired or not; and computing a posterior probability of whether program is desired or not, based on the conditional probability and the prior probability. - View Dependent Claims (14, 15)
-
-
16. A data processing device comprising:
-
at least one input for receiving data including viewer profile data; and data regarding a television program; and a processor, the processor being adapted to perform the following; calculating a probability that the television program is a desired one; and supplying a recommendation regarding the television program based on the probability, wherein the viewer profile comprises a list of features types and values for such feature types; the feature types are selected from at least two sets, including a first set of feature types whose values are deemed non-independent; and a second set of feature types whose values are deemed independent; and calculating a probability comprises applying a Bayesian classifier calculation corresponding to feature types from the second set; and applying a modified Bayesian classifier calculation corresponding to feature types from the first set. - View Dependent Claims (17)
-
-
18. A computer readable medium having computer-executable instructions stored thereon for performing the method comprising:
-
calculating a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the computer readable medium further embodies the viewer profile, the viewer profile being embodied as a data structure comprising; a list of feature values; and for each element of the list, a respective number of times programs having that feature value were watched, and wherein the software is further arranged to perform the following, each time a user watches a new program, first adding, to the list, feature values or counts of such feature values, associated with that new program; selecting at least one companion program to the new program, the companion program being selected at random from a program schedule, which companion program has not been watched; and second adding, to the list, feature values of the companion program, or counts of such feature values. - View Dependent Claims (19, 20, 21, 22)
-
-
23. A computer readable medium having computer-executable instructions stored thereon for performing the method comprising:
-
calculating, using a Bayesian classifier, a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the computer-executable instructions is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein the viewer profile data comprises a list of feature values; a respective negative count for each element of the list, the negative count indicating a number of times programs having that feature value have not been watched; a respective positive count for each element of the list, the positive count indicating a number of times programs having that feature value have been watched; the noise threshold calculation comprises selecting a sub-list comprising at least feature values having at least one specific type of feature; choosing the highest negative count in the sub-list as the noise threshold; the recommendation comprises a program selected from a group having at least one feature value having a positive or negative count in the viewer profile exceeding the noise threshold. - View Dependent Claims (24, 25, 26, 27, 28)
-
-
29. A computer readable medium having computer-executable instructions stored thereon for performing the method comprising:
-
calculating, using a Bayesian classifier, a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the computer-executable instructions is further adapted to subject the viewer profile to a noise threshold calculation prior to using the Bayesian classifier, and wherein subjecting the viewer profile to the noise threshold further comprises using observations gathered by a known random process to estimate a reasonable noise threshold.
-
-
30. A computer readable medium having computer-executable instructions stored thereon for performing the method comprising:
-
calculating a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein calculating the probability comprises; computing a prior possibility, of whether a program is desired or not; computing a conditional probability of whether a feature fi will be present if a show is desired; and computing a posterior probability of whether program is desired or not, based on the conditional probability and the prior probability. - View Dependent Claims (31, 32)
-
-
33. A computer readable medium having computer-executable instructions stored thereon for performing the method comprising:
-
calculating a probability that a television program is a desired one, based on a viewer profile and data regarding the television program; and supplying a recommendation regarding the television program based on the probability, wherein the viewer profile comprises a list of features types and values for such feature types; the feature types are selected from at least two sets, including a first set of feature types whose values are deemed non-independent; and a second set of feature types whose values are deemed independent; and calculating a probability comprises applying a Bayesian classifier calculation corresponding to feature types from the second set; and applying a modified Bayesian classifier calculation corresponding to feature types from the first set. - View Dependent Claims (34)
-
-
35. A data processing method comprising performing the following operations in a data processing device:
-
first receiving data reflecting physical observations, which data includes a list of feature values and observations about feature values, some of which feature values are independent and some of which are not; second receiving data about an item to be classified, the data about the item to be classified including feature values; maintaining a division of the data reflecting physical observations into at least two sets, including a first set including those feature values which are deemed not independent; and a second set including those feature values which are deemed independent; performing a probabilistic calculation on the data reflecting physical observations and the data regarding an item to be classified including; applying a Bayesian classifier calculation with respect to feature values relating to the second set; and applying a modified Bayesian classifier calculation with respect to feature values relating to the first set presenting a conclusion regarding the item to be classified to a user based on the probabilistic calculation. - View Dependent Claims (36)
-
Specification