Client-side recommendations on one-way broadcast networks

• US 9,749,691 B2
• Filed: 04/12/2012
• Issued: 08/29/2017
• Est. Priority Date: 04/12/2012
• Status: Active Grant

• Current Assignee: Contentwise S.R.L. (Moviri S.p.A.)
• Original Assignee: Contentwise S.R.L. (Moviri S.p.A.), Politecnico di Milano
• Inventors: Cremonesi, Paolo, Turrin, Roberto
• Primary Examiner(s): Tran, Hai V
• Application Number:
  US14/391,836
• Publication Number:
  US 20150089524A1
• Time in Patent Office: 1,965 Days
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04N 21/23614 Multiplexing of additional data and video streams arrangements for simultaneous broadcast of plural pieces of information H04H20/28
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/2665 Gathering content from different sources, e.g. Internet and satellite
• CPC Class Codes: H04N 21/4348 Demultiplexing of additional data and video streams
• CPC Class Codes: H04N 21/442 Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk arrangements for monitoring broadcast services or broadcast-related services H04H60/29; arrangements for identifying or recognising characteristics with a direct linkage to broadcast information H04H60/35; monitoring of user activities for profile generation for accessing a video database G06F16/739; monitoring in wireless networks H04W24/00
• CPC Class Codes: H04N 21/44226 on social networks
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/4622 Retrieving content or additional data from different sources, e.g. from a broadcast channel and the Internet web site content organization and management for information retrieval from the Internet G06F16/958; transmission by internet of broadcast information H04H60/82; stock exchange data over packet-switching network H04L12/1804; push services including data channel over packet-switching network H04L12/1859
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/4668 for recommending content, e.g. movies

Abstract

A method and a client-side Recommender system on One-Way Broadcast Network includes at least a one-way channel connecting a broadcasting station to a set-top-box device of a first set of users, the set-top-box including a memory, a computation and driving unit for tracking the user activity, storing a user profile, generating and displaying a ranking of recommended media items, the ranking being determined by the computation unit through a recommender model stored on the memory. The system further includes a collaborative dual module, a first back-end module running on a auxiliary server connected to a two-way network and belonging to an auxiliary domain, this first back-end module leveraging external data sources to collect community based information on at least part of the media items, while the recommender model stored on the memory being a second front-end module, wherein the broadcasting station and the set-top-box device belong to a target domain.

Claims

1. A recommender system on a one-way broadcast network, comprising:

a set-top-box (STB) device of a first set of users of the one-way broadcast network, the set-top-box (STB) device of the first set of users operatively connected to a television (TV) set of the first set of users, the set-top-box (STB) device of the first set of users comprising i) a memory that stores a user profile, and ii) a computation device that tracks the first set of users'"'"' activity, generates, and displays a ranking of recommended media items on the TV set, said ranking being determined by said computation device through a recommender model stored on said memory;

a broadcasting station connected to the set-top-box (STB) device of the first set of users via a one-way channel that transmits data to the set-top-box (STB) device of the first set of users;

an auxiliary server connected to a bidirectional network, the auxiliary server and the bidirectional network both belonging to an auxiliary domain;

a first back-end module running on the auxiliary server, the first back-end module belonging to the auxiliary domain and leveraging external data sources to collect community based information;

a collaborative dual module comprising first and second modules,

said first module being said first back-end module, leveraging external data sources to collect community based information belonging to the auxiliary domain on at least part of said media items, and said recommender model stored on said memory of the set-top-box (STB) device of the first set of users being said second module comprised of a second front-end module, wherein,

the broadcasting station and said set-top-box device of the first set of users belong to a target domain,

said auxiliary server is part of said bidirectional network, comprising at least two channels to gather feedback data from a second set of users belonging to the auxiliary domain on at least part of said media items, said first back-end module is processed by said auxiliary server through collaborative techniques including said feedback data collected from said second set of users,

said first back-end module is transformed in a compact representation by said auxiliary server and one-way broadcast to said set-top-box (STB) device of the first set of users,

wherein users'"'"' preferences of the auxiliary domain are compactly stored in a n×

m user rating matrix R (URM) that is a collection of all ratings implicitly or explicitly expressed by “

n”

users of the second set of users registered with an external provider about the “

m”

items available in a catalog of the auxiliary domain,

wherein said user rating matrix R (URM) is factorized by means of a singular value decomposition (SVD) model to decompose the user rating matrix R (URM) into a product of lower rank matrices to be approximated in a f-dimensional feature space by the product;

U·

S·

Q*

where “

U”

is a n×

f orthonormal matrix, Q* is the transpose of “

Q”

which is a m×

f orthonormal matrix, “

S”

is a f x f diagonal matrix containing the first “

f”

singular values, in order of decreasing magnitude, where “

n”

, “

m”

, and “

f”

are greater than zero, and

wherein Q is representing the recommender model to be transferred from said auxiliary domain to said target domain.

2. The recommender system as in claim 1, wherein said second set of users is different from said first set of users.

3. The recommender system as in claim 2, wherein said computation capacity unit, through said second front-end module, tracks locally behavior of the user of said first set of users and compares the behavior of the user of said first set of users with the behavior of the other users of said second set of users, in order to provide said ranking of recommended media items.

4. The recommender system as in claim 3, wherein said first back-end module is processed through hybrid recommender algorithms by combining both collaborative and content-based techniques including additional items different from said media items.

5. The recommender system as in claim 2, wherein said first back-end module is processed through hybrid recommender algorithms by combining both collaborative and content-based techniques including additional items different from said media items.

6. The recommender system as in claim 1, wherein said computation capacity unit, through said second front-end module, tracks locally behavior of the user of said first set of users and compares the behavior of the user of said first set of users with the behavior of the other users of said second set of users, in order to provide said ranking of recommended media items.

7. The recommender system as in claim 6, wherein said first back-end module is processed through hybrid recommender algorithms by combining both collaborative and content-based techniques including additional items different from said media items.

8. The recommender system as in claim 1, wherein said first back-end module is processed through hybrid recommender algorithms by combining both collaborative and content-based techniques including additional items different from said media items.

9. A one-way broadcast program recommendation method comprising:

providing a one-way channel connecting a broadcasting station to a set-top-box (STB) device of a first set of users of the one-way broadcast network, wherein said set-top-box (STB) device of the first set of users is arranged for tracking the user activity, storing a user profile, generating and displaying on a television (TV) set a ranking of recommended media items, said ranking being determined by a computation unit of said set-top-box (STB) device of the first set of users through a recommender model stored in a memory thereof, including operating a collaborative dual module comprising a first module and a second module,

connecting an auxiliary server to a bidirectional network, the auxiliary server and the bidirectional network both belonging to an auxiliary domain,

running the first module on the auxiliary server as a first back-end module, the first back-end module leveraging external data sources to collect community based information belonging to the bidirectional network both on at least part of said media items, with the second module being a second front-end module representing said recommender model stored on said memory of the set-top-box (STB) device of the first set of users belonging to a target domain, and

using at least two channels of said auxiliary server to gather feedback data from a second set of users belonging to the bidirectional network both on at least part of said media items,

using said auxiliary server to process said first back-end module through collaborative techniques including said feedback data collected from said second set of users, and

using said auxiliary server to transform said first back-end module into a compact representation and one-way broadcast to said set-top-box (STB) device of the first set of users where the compact representation is the recommender model implemented as said second front-end module,

wherein users'"'"' preferences of the auxiliary domain are compactly stored in a n×

m user rating matrix R (URM) that is a collection of all ratings implicitly or explicitly expressed by “

n”

users of the second set of users registered with an external provider about the “

m”

items available in a catalog of the auxiliary domain,

wherein said user rating matrix R (URM) is factorized by means of a singular value decomposition (SVD) model to decompose the user rating matrix R (URM) into a product of lower rank matrices to be approximated in a f-dimensional feature space by the product;

U·

S·

Q*

where “

U”

is a n x f orthonormal matrix, Q* is the transpose of “

Q”

which is a m x f orthonormal matrix, “

S”

is a f x f diagonal matrix containing the first “

f”

singular values, in order of decreasing magnitude, where “

n”

, “

m”

, and “

f”

are greater than zero, and

wherein Q is representing the recommender model transferred from said auxiliary domain to said target domain.

10. A one-way broadcast program recommendation method, comprising:

connecting a set-top-box (STB) device of first set of users to a television (TV) set of a first user of the first set of users, the set-top-box (STB) device of the first set of users comprising i) a memory that stores a first user profile, and ii) a computation device that, through a front-end module, tracks the first user'"'"'s activity, generates, and displays a ranking of recommended media items on the TV set, said ranking being determined by said computation device through the front-end module, the front-end module comprised of a recommender model stored on said memory, the set-top-box (STB) device of the first set of users belonging to a target domain;

operating a broadcasting station with a one-way channel that transmits data to the set-top-box (STB) device of the first set of users, wherein no user activity data is received by the broadcasting station from the set-top-box (STB) device of the first set of users, the broadcasting station belonging to the target domain;

operating an auxiliary server connected to a bidirectional network and to the broadcasting station, the auxiliary server comprising a back-end module that collects community-based information belonging to an auxiliary domain from external data sources, the auxiliary server and the bidirectional network both belonging to the auxiliary domain separate from the target domain;

using the auxiliary server to gather feedback data from behavior of a second set of users belonging to the auxiliary domain on at least part of said media items, to transform the back-end module into a compact representation, and to provide the compact representation to the set-top-box in a one-way communication via the one-way channel, the compact representation provided to the set-top-box being an updated recommender model; and

using the computation device and the first user profile in the set-top-box (STB) device of the first set of users to compare the first user'"'"'s tracked activity with the behavior of the second set of users to generate and display the ranking of recommended media items on the TV set,

wherein preferences of the auxiliary domain users are compactly stored in a n×

m user rating matrix R (URM) that is a collection of all ratings implicitly or explicitly expressed by “

n”

users of the second set of users registered with an external provider about the “

m”

items available in a catalog of the auxiliary domain,

wherein said user rating matrix R (URM) is factorized by means of a singular value decomposition (SVD) model to decompose the user rating matrix R (URM) into a product of lower rank matrices to be approximated in a f-dimensional feature space by the product;

U·

S·

Q*

where “

U”

is a n x f orthonormal matrix, Q* is the transpose of “

Q”

which is a m x f orthonormal matrix, “

S”

is a f×

f diagonal matrix containing the first “

f”

singular values, in order of decreasing magnitude, where “

n”

, “

m”

, and “

f”

are greater than zero, and

wherein Q is representing the updated recommender model being transferred from said auxiliary domain to said target domain.