Abstract:
A method and system of recommending content and targeting advertisements for one or more users is provided. The system includes an aggregator that is connected to the one or more users and collects rich user data therefrom. The method includes collecting rich user data from one or more users; building one or more user profiles corresponding to the one or more users; storing the one or more user profiles in a memory database; requesting one or more content profiles from one or more providers; receiving the one or more content profiles; determining whether one of the user profiles is a target user profile for one of the content profiles based on the rich user data associated with the target user profile; and delivering content programs associated with the content profiles to the target user.

Description:
BACKGROUND 
       [0001]    Targeted advertising and content recommendation are typically based around the collection, sharing and mining of information with respect to a potential viewer base. For example, if a person or group of people has a particular interest, then an advertiser who learns of this interest may select an appropriate advertisement to be viewed by such a person or group that would garner the most views and have the greatest likelihood of converting a viewer into a customer. In order to learn of such interests, targeted advertising and recommendation systems often request feedback from a person or group of people to learn what their interests are. Alternatively, targeted advertising and recommendation systems often monitor the activity of a viewer of a particular medium, such as television, and use associated contextual information (e.g. temporal information) to judge whether a viewer is likely or unlikely to be interested in a particular advertisement. 
         [0002]    Unfortunately, privacy becomes an issue for users of such personalization systems. When a user learns that a particular system shares the information located in his/her user profile with a content provider or an advertiser, the user often feels that his or her privacy has been violated and is more reluctant to share any additional information about their interests. Such reluctance can stymie the effectiveness of a content recommendation and targeted advertising system, making their services appear less valuable to advertisers. 
       SUMMARY 
       [0003]    In view of the foregoing, a privacy-preserving content recommendation and targeted advertising method and system are disclosed. The system includes an aggregator that is connected to one or more users and collects rich user data therefrom. The method involves collecting rich user data from one or more users, the rich user data including content viewing habits of the one or more users; building one or more user profiles corresponding to the one or more users; storing the one or more user profiles in a memory database; requesting one or more content profiles from one or more providers; receiving the one or more content profiles; determining whether one of the one or more user profiles is a target user profile for one of the one or more content profiles based on the rich user data associated with the target user profile; and delivering one or more content programs associated with the one of the one or more content profiles to the user associated with the target user profile. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0004]    For a more complete understanding of the present invention, reference is made to the following detailed description of an embodiment considered in conjunction with the accompanying drawings, in which: 
           [0005]      FIG. 1  is a diagram of content recommendation and targeted advertising system in accordance with an embodiment of the invention; 
           [0006]      FIG. 2  is a flow chart illustrating a method of recommending content and targeting advertisements in accordance with an embodiment of the invention; and 
           [0007]      FIG. 3  is a flow chart illustrating a method of recommending content and targeting advertisements in accordance with an embodiment of the invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0008]    The techniques generally relate to a content recommendation and targeted advertising system that utilizes rich user data in making decisions on what content to recommend and what advertisements to deliver to a particular user. The rich user data is collected at an aggregator, which creates user profiles for multiple users using such rich user data. The user profiles are then used to recommend content and target advertisements based on whether a particular content and/or advertisement profile can be a suitable match to a particular user based on the rich user data associated with that user. During this process, the privacy of the user profiles is kept by not sharing them with content providers and/or advertisers, and/or by sharing them with content providers/advertisers under a privacy-preserving protocol. 
         [0009]    Turning to  FIG. 1 , a diagram of a content recommendation and targeted advertising system  10  in accordance with an embodiment is displayed. The system comprises an aggregator  12  that is connected to users  14 ,  16 ,  18  through their media consumption devices (e.g., set top box  20 ) via a gateway  22 . The aggregator  12  gathers user data  24  from the users  14 ,  16 ,  18  and creates a user profile  26  from the user data  24  for each user  14 ,  16 ,  18 . The aggregator  12  also makes connections to content providers (e.g., content provider  28 ) and advertisers (e.g., advertiser  30 ) to obtain content profiles  32  having content recommendations  34  and advertisement profiles  36  having targeted advertisements  38  based on the information contained in a user&#39;s user profile  26 . The aggregator  12  can then send these content recommendations  34  and advertisements  38  to a user  14 ,  16 ,  18 . The information from the user profile  26  is kept from content provider  28  and/or advertiser  30  to maintain an associated user&#39;s privacy while allowing the aggregator  12  to choose the appropriate content recommendation  34  and/or advertisement  38  to be delivered to the user. 
         [0010]      FIG. 2  shows a flow chart illustrating a method  100  of recommending content and targeting advertisements according to an embodiment. In one embodiment, the method  100  is performed by the aggregator  12  shown in  FIG. 1 . In other embodiments, the method  100  can be performed by other parts of the system  10 , like, for example, the set top box  20  and/or the gateway  22 . The aggregator  12  begins by collecting rich user data from the users  14 ,  16 ,  18  (step  102 ). Such rich user data can include a user&#39;s television viewing habits  104 , such as what programs are watched and information about those programs, explicit user feedback about those programs (e.g. a “5-star” rating system), digital video recording operations performed during particular programs (e.g., play, pause, rewind and fast forward, jump back, scenes watched or skipped, actual time elapsed between start and end of program), advertisements skipped or viewed, and channel tuning and the like. Such rich user data can also come from a user&#39;s internet activity  106 , which can be drawn from a user&#39;s gateway  22  and the like. The aggregator  12  can also draw social data  108  from a user&#39;s social network, which can include the user&#39;s own activity as well as that of the user&#39;s friends and connections, as well as behavioral data  110 , such as laughing, screaming, and eye contact with the television, from cameras, microphones, and other sensing devices associated with the system. 
         [0011]    After gathering such rich user data, the aggregator  12  builds a user profile (step  112 ) that serves as a description of the user&#39;s interests. This user profile can be updated regularly as the aggregator  12  obtains more rich user data during the course of its operation. The user profile can also be used with other user profiles to train content recommendation algorithms. Once they are built, the aggregator  12  stores the user profiles in a local memory (step  114 ). In one embodiment, once user profiles are stored locally, the aggregator  12  requests content profiles  32  and/or ad profiles  36  from the content provider  28  and/or advertiser  30 , respectively (step  116 ). Upon receiving the content and/or ad profiles  32 ,  36 , the aggregator  12  then matches them to the user profiles it deems most suitable for the content recommendations  34  and/or advertisements  38  contained in the content and ad profiles  32 ,  36  (step  118 ). The aggregator  12  then delivers the content recommendations  34  and/or targeted advertisements  38  to the users associated with the matched user profiles. 
         [0012]      FIG. 3  shows a flow chart illustrating a method  101  of recommending content and targeting advertisements according to an embodiment. The embodiment shown in  FIG. 3  is similar to the embodiment shown in  FIG. 2  except where explicitly shown and discussed below. It should be noted that the steps identified in the method  101  can be performed as substitutes for certain steps identified in  FIG. 2 . Alternatively, the steps identified in the method  101  can be performed in addition to the steps identified in  FIG. 2 . 
         [0013]    After the aggregator  12  has stored the user profiles in a local memory, the aggregator  12  identifies which user profiles are associated with a set of users watching a particular program (step  115 ). The aggregator then identifies the advertising spaces available during the particular program (step  117 ). Such advertising spaces can include commercial breaks, embedded video space present during the program, and in-program objects that can selectively display advertisements, such as a billboard at a live sporting event or a blank label of an object during a scripted show. The aggregator  12  then auctions the advertisement spaces associated with the particular program to advertisers based on the information contained in the user profiles (step  119 ). These advertisement auctions can auction the advertising space based on where and when they appear during a program. For example, certain advertisement spaces that appear earlier in the program can be auctioned at a higher price than advertisement spaces that appear later in the program. Alternatively, the advertisement auctions can be based on the location of the advertisement spaces in the watched program. When an auction is won, the aggregator receives a content and/or advertisement profile from the content provider and/or advertiser having the winning bid (step  121 ). The content profile is then delivered to the set of users watching the particular program ( 123 ). 
         [0014]    In the embodiments discussed above, the user profiles are not accessible by content providers  28  and advertisers  30 . This is done to allow the aggregator  12  to pick and choose what information it deems suitable for sharing with the content provider  28  and/or advertiser  30  for the purposes of selecting content recommendations or advertisements while maintaining the individual privacy of each user. In other embodiments, the user profiles are exchanged with the content provider  28  and/or advertiser  30  under a privacy-preserving protocol. In such a protocol, the user profile is transformed into a sanitized version of the user profile, which can then be shared with the content provider. The content provider then matches the sanitized user profile to a content profile and returns the content profile to the aggregator. 
         [0015]    The various embodiments disclosed herein can be implemented as hardware, firmware, software, or any combination thereof. Moreover, the software is preferably implemented as an application program tangibly embodied on a program storage unit or computer readable medium. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. Preferably, the machine is implemented on a computer platform having hardware such as one or more central processing units (“CPUs”), a memory, and input/output interfaces. The computer platform may also include an operating system and microinstruction code. The various processes and functions described herein may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU, whether or not such computer or processor is explicitly shown. In addition, various other peripheral units may be connected to the computer platform such as an additional data storage unit and a printing unit. 
         [0016]    All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. 
         [0017]    It will be understood that the embodiments described herein are merely exemplary and that a person skilled in the art may make many variations and modifications without departing from the spirit and scope of the invention. All such variations and modifications are intended to be included within the scope of the invention as defined in the appended claims.