Apparatus and method for performing profile based collaborative filtering
Methods and apparatus are disclosed to various embodiments for recommending items ( 150 ) to an advisee such as television program recommendations, based on other user's viewing preferences ( 140 ) or profiles ( 160 ). In contrast to the state of the art, the other user's have at least one demographic in common with the advise, such as age, income or gender for example or combinations thereof. According to one aspect of the invention, recommendations may be generated before a viewing or purchase history of the advisee is available. According to one embodiment, a method for recommending items includes the acts of receiving a recommendation request from an advisee for a recommendation of said items; filtering a general population of users to identify a sub-population of users who share at least one demographic in common with said advisee; computing a degree of closeness measure between preference data associated with each user in the sub-population and one of preference ( 140 ) and profile ( 160 ) data associated with said advisee; selecting the preference data associating with N users from said sub-population having the lowest computed degree of closeness measure with the advisee, where N is a positive integer value, equal to or greater than 1; using the selected preference data to recommend said items to the advisee.
1 . A method for recommending items ( 150 ), comprising the acts of:
(a) receiving a recommendation request from an advisee;
(b) filtering a general population of users ( 300 ) to identify a sub-population of users who share at least one demographic in common with said advisee;
(c) computing a degree of closeness measure ( 600 ) between preference data ( 130 ) associated with each user in the sub-population and one of preference ( 140 ) and profile data ( 160 ) associated with said advisee;
(d) selecting the preference data associated with N users from said sub-population having the lowest computed degree of closeness measure ( 600 ) with the advisee, where N is a positive integer value, equal to or greater than 1; and
(e) using the preference data selected at said step (d) to recommend said items ( 150 ) to the advisee.
2 . The method of claim 1 , wherein N is provided as one of a system default value and as a user provided input value.
3 . The method of claim 1 , further comprising the act of accumulating preference data, prior to said act (c), wherein said preference data is accumulated from one or more viewer surveys that provide a rating of item features.
4 . The method of claim 1 , wherein said items are television programs obtained from an electronic program guide.
5 . The method of claim 1 , wherein said preference data is provided by one of an implicit and explicit program recommender.
6 . The method of claim 1 , wherein said preference data is provided by a collaborative program recommender.
7 . The method of claim 1 , wherein the at least one demographic is provided as one of an input parameter and a system default value.
8 . A method for recommending items ( 150 ), comprising the acts of:
(a) receiving a recommendation request from an advisee;
(b) filtering a general population of users ( 300 ) to identify a sub-population of users who share at least one demographic in common with said advisee;
(c) computing a degree of closeness measure ( 600 ) between profile data associated with each user in said sub-population and one of preference ( 140 ) and profile data ( 160 ) associated with said advisee;
(d) selecting the profile data associated with N users from said sub-population whose computed degree of closeness measure is lowest, where N is a positive integer value, equal to or greater than 1; and
(e) using the profile data selected at said step (d) to recommend said items ( 150 ) to the advisee.
9 . The method of claim 8 , prior to said act (c), further comprising the acts of:
generating preference data ( 130 ) for said sub-population of users from one or more viewer surveys that provide a rating for program features; and
generating profile data ( 160 ) from said generated preference data ( 130 ).
10 . The method of claim 8 , wherein said list of one or more items are programs obtained from an electronic program guide.
11 . The method of claim 8 , wherein said preference data is provided by one of an implicit and explicit program recommender.
12 . The method of claim 8 , wherein said preference data is provided by a collaborative program recommender.
13 . The method of claim 8 , wherein the at least one demographic is provided as one of and input parameter and a system default value.
14 . A method for recommending items ( 150 ), comprising the acts of:
(a) receiving a recommendation request from an advisee;
(b) filtering a general population of users ( 300 ) to identify a sub-population of users who share at least one demographic in common with said advisee;
(c) generating mean profiles ( 700 ) from preference data associated with said sub-population of users;
(d) computing a distance measure ( 800 ) between the N mean profiles and profile data associated with said advisee;
(e) selecting N mean profiles whose computed distance measure ( 800 ) is determined to be lowest, where N is a positive integer value, equal to or greater than 1; and
(f) using the N selected mean profiles to recommend said items ( 150 ) to the advisee.
15 . The method of claim 14 , wherein the act of generating mean profiles further comprises the acts of:
accumulating preference data ( 130 ) for said sub-population of users from one or more viewer surveys that provide a rating for program features; and
generating profile data ( 500 ) from said accumulated preference data;
generating at least one cluster ( 650 ) from said preference data; and
generating a mean profile corresponding to said at said at least one cluster.
16 . The method of claim 14 , wherein said of one or more items are television programs obtained from an electronic program guide.
17 . The method of claim 14 , wherein said preference data is provided by one of an implicit and explicit program recommender.
18 . The method of claim 14 , wherein the at least one demographic is provided as one of an input parameter and a default value.
19 . A computer implemented apparatus for recommending items ( 150 ), the apparatus comprising:
a processor ( 115 );
a memory ( 120 ) connected to the processor ( 1 15 ) and storing computer executable instructions therein;
wherein the processor ( 115 ), in response to execution of the instructions:
receives a recommendation request from an advisee;
filters a general population of users ( 300 ) to identify a sub-population of users who share at least one demographic in common with said advisee;
computes a degree of closeness measure ( 600 ) between preference data ( 130 ) associated with each user in the sub-population and one of preference ( 140 ) and profile data ( 160 ) associated with said advisee;
selects the preference data associated with N users from said sub-population having the lowest computed degree of closeness measure with the advisee, where N is a positive integer value, equal to or greater than 1; and
uses the selected preference data to recommend said items ( 150 ) to the advisee.
20 . A computer implemented apparatus for recommending items ( 150 ), the apparatus comprising:
a processor ( 115 );
a memory ( 120 ) connected to the processor ( 115 ) and storing computer executable instructions therein;
wherein the processor ( 115 ), in response to execution of the instructions:
receives a recommendation request from an advisee;
filters a general population of users ( 300 ) to identify a sub-population of users who share at least one demographic in common with said advisee;
computes a degree of closeness measure ( 600 ) between profile data associated with each user in said sub-population and one of preference ( 140 ) and profile data ( 160 ) associated with said advisee;
selects the profile data associated with N users whose computed degree of closeness measure is lowest, where N is a positive integer value, equal to or greater than 1; and
uses the selected profile data to recommend said items ( 150 ) to the advisee.
21 . A computer implemented apparatus for recommending items ( 150 ), the apparatus comprising:
a processor ( 115 );
a memory ( 120 ) connected to the processor ( 115 ) and storing computer executable instructions therein;
wherein the processor ( 115 ), in response to execution of the instructions:
receives a recommendation request from an advisee;
filters a general population of users ( 300 ) to identify a sub-population of users who share at least one demographic in common with said advisee;
generates mean profiles from preference data associated with said sub-population of users;
computes a distance measure between the N mean profiles and profile data associated with said advisee;
selects N mean profiles whose computed distance measure is determined to be lowest; and
uses the N selected mean profiles to recommend said items ( 150 ) to the advisee.
22 . An article of manufacture for recommending items ( 150 ), comprising: a computer readable medium having computer readable code means embodied thereon, said computer readable program code means comprising:
an act of receiving a recommendation request from an advisee;
an act of filtering a general population of users ( 300 ) to identify a sub-population of users who share at least one demographic in common with said advisee;
an act of computing a degree of closeness measure ( 600 ) between preference data ( 130 ) associated with each user in the sub-population and one of preference ( 140 ) and profile data ( 160 ) associated with said advisee;
an act of selecting the preference data associated with N users from said sub-population having the lowest computed degree of closeness measure with the advisee, where N is a positive integer value, equal to or greater than 1; and
an act of using the selected preference data) to recommend said items to the advisee.
23 . An article of manufacture for recommending items ( 150 ), comprising: a computer readable medium having computer readable code means embodied thereon, said computer readable program code means comprising:
an act of receiving a recommendation request from an advisee for a recommendation of said items;
an act of filtering a general population of users ( 300 ) to identify a sub-population of users who share at least one demographic in common with said advisee;
an act of computing a degree of closeness measure between profile data associated with each user in said sub-population and one of preference ( 140 ) and profile data ( 160 ) associated with said advisee;
an act of selecting the profile data associated with N users whose computed degree of closeness measure is lowest, where N is a positive integer value, equal to or greater than 1; and
an act of using the selected profile data to recommend said items to the advisee.
24 . An article of manufacture for recommending items ( 150 ), comprising: a computer readable medium having computer readable code means embodied thereon, said computer readable program code means comprising:
an act of receiving a recommendation request from an advisee;
an act of filtering a general population of users ( 300 ) to identify a sub-population of users who share at least one demographic in common with said advisee;
an act of generating mean profiles ( 700 ) from preference data associated with said sub-population of users;
an act of computing a distance measure ( 800 ) between the mean profiles and profile data associated with said advisee;
an act of selecting N mean profiles whose computed distance measure is determined to be lowest, where N is a positive integer value, equal to or greater than 1; and
an act of using the N selected mean profiles to recommend said items ( 150 ) to the advisee.