Patent Publication Number: US-8984082-B2

Title: Personalization based upon social value in online media

Description:
RELATED APPLICATIONS 
     This utility patent application is a Continuation claiming the benefit under 35 U.S.C. §120 of allowed U.S. patent application Ser. No. 13/967/976 filed on Aug. 15, 2013, entitled “Personalization Based Upon Social Value in Online Media,” which is a non-provisional patent application claiming the benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application Ser. No. 61/694,404 filed on Aug. 29, 2012, entitled “Personalization Based Upon Social Value in Online Media,” all of which are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     The present invention relates generally to content management, and more particularly, to providing content to a user based on a social value recommendation score of the content for the user. 
     BACKGROUND 
     Today, online media is a world where curated content is king. Brands typically employ talented editors that become world-class experts in their fields. Their editorial voice and coverage directs the attention of millions of readers. In this world, a selection of the cover story may determine the topic of interest for millions of readers. However, this approach may lack personalization for individual readers. For example, news about a particular celebrity may be provided to a reader as a mainstream interest, even though that reader may not want to read articles about that celebrity. Sometimes, these uninterested readers quickly abandon the media property because they do not quickly see an article that is interesting to them. As a result, the loyalty of the uninterested reader to the media property and/or publisher may be diminished, leading the uninterested reader to a different media property in the future. 
     At the same time, not only do readers like to read articles about topics that they are interested in, but they may also read articles about other topics that their friends and/or social network may be interested in. If a reader reads an article about a topic that may be of interest to the reader&#39;s social network, then the reader may be able to talk to his/her friends about that article. As a result, the reader may appear to his/her social network as smarter, more socially valuable person. It is with respect to these considerations and others that the present invention has been made. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Non-limiting and non-exhaustive embodiments of the present invention are described with reference to the following drawings. In the drawings, like reference numerals refer to like parts throughout the various figures unless otherwise specified. 
       For a better understanding of the present invention, reference will be made to the following Detailed Description, which is to be read in association with the accompanying drawings, wherein: 
         FIG. 1  is a system diagram of an environment in which embodiments of the invention may be implemented; 
         FIG. 2  shows an embodiment of a client device that may be included in a system such as that shown in  FIG. 1 ; 
         FIG. 3  shows an embodiment of a network device that may be included in a system such as that shown in  FIG. 1 ; 
         FIG. 4  illustrates a logical flow diagram generally showing one embodiment of an overview process for providing content to a user based on a recommendation score of the content for the user; 
         FIG. 5  illustrates a logical flow diagram generally showing one embodiment of a process for determining a social value of content to a user; 
         FIG. 6  illustrates a logical flow diagram generally showing one embodiment of a process for determining content to provide to a user; and 
         FIG. 7  illustrates a use case embodiment of a social network of a user that may be utilized to determine a recommendation score for a user. 
     
    
    
     DETAILED DESCRIPTION 
     Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The phrase “in one embodiment” as used herein does not necessarily refer to the same embodiment, though it may. Furthermore, the phrase “in another embodiment” as used herein does not necessarily refer to a different embodiment, although it may. Thus, as described below, various embodiments of the invention may be readily combined, without departing from the scope or spirit of the invention. 
     In addition, as used herein, the term “or” is an inclusive “or” operator, and is equivalent to the term “and/or,” unless the context clearly dictates otherwise. The term “based on” is not exclusive and allows for being based on additional factors not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a,” “an,” and “the” include plural references. The meaning of “in” includes “in” and “on.” 
     As used herein, the term “content” refers to digital data that may be communicated over a network to be remotely displayed by a computing device. Non-exhaustive examples of content include but are not limited to articles, blogs, movies, videos, music, sounds, pictures, illustrations, graphics, images, text, or the like. 
     As used herein, the phrase “intrinsic value” may refer to a predictive value representative of a personal interest to a user for a given piece of content. 
     As used herein, the phrase, “social value” may refer to a predictive value representative of an interest to a user&#39;s social network for a given piece of content. 
     As used herein, the term “social network” refers to a concept of an individual&#39;s personal network of friends, family, colleagues, coworkers, and the subsequent connections within those networks. Social networks can be utilized to find more relevant connections for a variety of activities, including, but not limited to dating, job networking, service referrals, content sharing, like-minded individuals, activity partners, or the like. 
     An online social network typically comprises a person&#39;s set of direct and/or indirect personal relationships, including real and virtual privileges and permissions that users may associate with these people. Direct personal relationships usually include relationships with people the user can communicate or has communicated with directly, including family members, friends, colleagues, coworkers, and other people with which the person has had some form of direct contact, such as contact in person, by telephone, by email, by instant message, by letter, or the like. These direct personal relationships are sometimes referred to as first-degree friends and/or first-degree relationships. First-degree friends can have varying degrees of closeness, trust, and other characteristics. 
     Indirect personal relationships typically include relationships through first-degree friends to people with whom a person has not had some form of direct or limited direct contact, such as in being cc&#39;d on an e-mail message, or the like. For example, a friend of a friend represents an indirect personal relationship. A more extended, indirect relationship might be a friend of a friend of a friend. These indirect relationships are sometimes characterized by a degree of separation between the people. For instance, a friend of a friend can be characterized as two degrees of separation or a second-degree friend and/or a second-degree relationship. Similarly, a friend of a friend of a friend can be characterized as three degrees of separation or a third-degree friend. And so on. 
     The following briefly describes embodiments of the invention in order to provide a basic understanding of some aspects of the invention. This brief description is not intended as an extensive overview. It is not intended to identify key or critical elements, or to delineate or otherwise narrow the scope. Its purpose is merely to present some concepts in a simplified form as a prelude to the more detailed description that is presented later. 
     Briefly stated, various embodiments are directed to personalizing content to be provided to a user. In at least one embodiment, a recommendation score may be determined for a piece of content for a user. The recommendation score may be based on a combination of an intrinsic value and/or a social value of the content to the user. The intrinsic value may be the interest, enjoyment, and/or value that the user may receive from the content. The social value may be an interest to a user&#39;s social network for a given piece of content. 
     In some embodiments, the social value may be calculated based on a combination of an individual social value for each of the user&#39;s first degree friends. The individual social value of a friend may be determined based on the combination of a social weight, an interest probability, and a recommendation score for the friend. Accordingly, the recommendation score may be a recursive function based on the user&#39;s social network. 
     Online personalization of content for a user may provide the user with the tools to be a valuable, appreciated member of the user&#39;s social network. By employing embodiments, as described herein, content may be determined and provided to a user, which may help the user gain the attention of their social circles. Instead of trying to capture and direct the user&#39;s attention (e.g., by providing a user a publisher selected headline article), the publisher becomes a back-stage prompter, helping provide the user with content that may make them the center of attention among their social networks. Each time a publisher provides content to a user that can be used to make that user more valuable within his/her social network, that user&#39;s loyally to the publisher may increase. 
     Illustrative Operating Environment 
       FIG. 1  shows components of one embodiment of an environment in which embodiments of the invention may be practiced. Not all of the components may be required to practice the invention, and variations in the arrangement and type of the components may be made without departing from the spirit or scope of the invention. As shown, system  100  of  FIG. 1  includes local area networks (LANs)/wide area networks (WANs)—(network)  110 , wireless network  108 , client devices  102 - 105 , Score Generator Server Device (SGSD)  112 , Content Selection Server Device (CSSD)  114 , and Logging Agent Server Device (LASD)  116 . 
     At least one embodiment of client devices  102 - 105  is described in more detail below in conjunction with  FIG. 2 . In one embodiment, at least some of client devices  102 - 105  may operate over a wired and/or wireless network, such as networks  110  and/or  108 . Generally, client devices  102 - 105  may include virtually any computing device capable of communicating over a network to send and receive information, perform various online activities, offline actions, or the like. In one embodiment, one or more of client devices  102 - 105  may be configured to operate within a business or other entity to perform a variety of services for the business or other entity. For example, client devices  102 - 105  may be configured to operate as a web server, an accounting server, a production server, an inventory server, or the like. However, client devices  102 - 105  are not constrained to these services and may also be employed, for example, as an end-user computing node, in other embodiments. It should be recognized that more or less client devices may be included within a system such as described herein, and embodiments are therefore not constrained by the number or type of client devices employed. 
     Devices that may operate as client device  102  may include devices that typically connect using a wired or wireless communications medium such as personal computers, multiprocessor systems, microprocessor-based or programmable electronic devices, network PCs, or the like. In some embodiments, client devices  102 - 105  may include virtually any portable personal computing device capable of connecting to another computing device and receiving information such as, laptop computer  103 , smart mobile telephone  104 , and tablet computers  105 , and the like. However, portable computing devices are not so limited and may also include other portable devices such as cellular telephones, display pagers, radio frequency (RF) devices, infrared (IR) devices, Personal Digital Assistants (PDAs), handheld computers, wearable computers, integrated devices combining one or more of the preceding devices, and the like. As such, client devices  102 - 105  typically range widely in terms of capabilities and features. Moreover, client devices  102 - 105  may access various computing applications, including a browser, or other web-based application. 
     A web-enabled client device may include a browser application that is configured to receive and to send web pages, web-based messages, and the like. The browser application may be configured to receive and display graphics, text, multimedia, and the like, employing virtually any web-based language, including a wireless application protocol messages (WAP), and the like. In one embodiment, the browser application is enabled to employ Handheld Device Markup Language (HDML), Wireless Markup Language (WML), WMLScript, JavaScript, Standard Generalized Markup Language (SGML), HyperText Markup Language (HTML), eXtensible Markup Language (XML), and the like, to display and send a message. In one embodiment, a user of the client device may employ the browser application to perform various activities over a network (online). However, another application may also be used to perform various online activities. 
     Client devices  102 - 105  also may include at least one other client application that is configured to receive and/or send content between another computing device. The client application may include a capability to send and/or receive content, or the like. The client application may further provide information that identifies itself, including a type, capability, name, and the like. In one embodiment, client devices  102 - 105  may uniquely identify themselves through any of a variety of mechanisms, including an Internet Protocol (IP) address, a phone number, Mobile Identification Number (MIN), an electronic serial number (ESN), or other device identifier. Such information may be provided in a network packet, or the like, sent between other client devices, SGSD  112 , CSSD  114 , LASD  116 , or other computing devices. 
     Client devices  102 - 105  may further be configured to include a client application that enables an end-user to log into an end-user account that may be managed by another computing device, such as SGSD  112 , CSSD  114 , LASD  116 , or the like. Such end-user account, in one non-limiting example, may be configured to enable the end-user to manage one or more online activities, including in one non-limiting example, search activities, social networking activities, browse various websites, communicate with other users, or the like. However, participation in such online activities may also be performed without logging into the end-user account. 
     Wireless network  108  is configured to couple client devices  103 - 105  and its components with network  110 . Wireless network  108  may include any of a variety of wireless sub-networks that may further overlay stand-alone ad-hoc networks, and the like, to provide an infrastructure-oriented connection for client devices  103 - 105 . Such sub-networks may include mesh networks, Wireless LAN (WLAN) networks, cellular networks, and the like. In one embodiment, the system may include more than one wireless network. 
     Wireless network  108  may further include an autonomous system of terminals, gateways, routers, and the like connected by wireless radio links, and the like. These connectors may be configured to move freely and randomly and organize themselves arbitrarily, such that the topology of wireless network  108  may change rapidly. 
     Wireless network  108  may further employ a plurality of access technologies including 2nd (2G), 3rd (3G), 4th (4G) 5th (5G) generation radio access for cellular systems, WLAN, Wireless Router (WR) mesh, and the like. Access technologies such as 2G, 3G, 4G, 5G, and future access networks may enable wide area coverage for mobile devices, such as client devices  103 - 105  with various degrees of mobility. In one non-limiting example, wireless network  108  may enable a radio connection through a radio network access such as Global System for Mobil communication (GSM), General Packet Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), code division multiple access (CDMA), time division multiple access (TDMA), Wideband Code Division Multiple Access (WCDMA), High Speed Downlink Packet Access (HSDPA), Long Term Evolution (LTE), and the like. In essence, wireless network  108  may include virtually any wireless communication mechanism by which information may travel between client devices  103 - 105  and another computing device, network, and the like. 
     Network  110  is configured to couple network devices with other computing devices, including, SGSD  112 , CSSD  114 , LASD  116 , client device  102 , and client devices  103 - 105  through wireless network  108 . Network  110  is enabled to employ any form of computer readable media for communicating information from one electronic device to another. Also, network  110  can include the Internet in addition to local area networks (LANs), wide area networks (WANs), direct connections, such as through a universal serial bus (USB) port, other forms of computer-readable media, or any combination thereof. On an interconnected set of LANs, including those based on differing architectures and protocols, a router acts as a link between LANs, enabling messages to be sent from one to another. In addition, communication links within LANs typically include twisted wire pair or coaxial cable, while communication links between networks may utilize analog telephone lines, full or fractional dedicated digital lines including T1, T2, T3, and T4, and/or other carrier mechanisms including, for example, E-carriers, Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines (DSLs), wireless links including satellite links, or other communications links known to those skilled in the art. Moreover, communication links may further employ any of a variety of digital signaling technologies, including without limit, for example, DS-0, DS-1, DS-2, DS-3, DS-4, OC-3, OC-12, OC-48, or the like. Furthermore, remote computers and other related electronic devices could be remotely connected to either LANs or WANs via a modem and temporary telephone link. In one embodiment, network  110  may be configured to transport information of an Internet Protocol (IP). In essence, network  110  includes any communication method by which information may travel between computing devices. 
     Additionally, communication media typically embodies computer readable instructions, data structures, program modules, or other transport mechanism and includes any information delivery media. By way of example, communication media includes wired media such as twisted pair, coaxial cable, fiber optics, wave guides, and other wired media and wireless media such as acoustic RF, infrared, and other wireless media. 
     One embodiment of SGSD  112  is described in more detail below in conjunction with  FIG. 3 . Briefly, however SGSD  112  includes virtually any network device capable of determining a recommendation score of content for one or more users. In at least one embodiment, SGSD  112  may calculate the recommendation score based on a determined intrinsic value and social value of content to the user. 
     In some embodiments, SGSD  112  may receive possible content from CSSD  114 . In other embodiments, SGSD  112  may provide CSSD  114  the determined recommendation scores. In at least one embodiment, SGSD  112  may communicate with LASD  116  to obtain historical data associated with a user, which may be utilized to determine the recommendation score of content for the user. 
     Devices that may be arranged to operate as SGSD  112  include various network devices, including, but not limited to personal computers, desktop computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, server devices, network appliances, and the like. 
     Although  FIG. 1  illustrates SGSD  112  as a single computing device, the invention is not so limited. For example, one or more functions of the SGSD  112  may be distributed across one or more distinct network devices. Moreover, SGSD  112  is not limited to a particular configuration. Thus, in one embodiment, SGSD  112  may contain a plurality of network devices to generate recommendation scores for content. In another embodiment, SGSD  112  may contain a plurality of network devices that operate using a master/slave approach, where one of the plurality of network devices of SGSD  112  operates to manage and/or otherwise coordinate operations of the other network devices. In other embodiments, the SGSD  112  may operate as a plurality of network devices within a cluster architecture, a peer-to-peer architecture, and/or even within a cloud architecture. Thus, the invention is not to be construed as being limited to a single environment, and other configurations, and architectures are also envisaged. In at least one embodiment, each of a plurality of SGSD  112  may be geographically distributed. In another embodiment, SGSD  112  may include a different computing device for monitoring tags in each of a plurality of different tag states. 
     One embodiment of CSSD  114  is described in more detail below in conjunction with  FIG. 3 . Briefly, however, CSSD  114  includes virtually any network device capable of selecting content to provide to a user based on recommendation scores of the content for the user. In at least one embodiment, CSSD  114  may provide SGSD  112  with possible content to provide to a particular user of client devices  102 - 105 . In some embodiments, CSSD  114  may receive recommendation scores from SGSD  112  based on the particular user and the possible content. CSSD  114  may then employ the recommendation scores to determine which content to provide to the user. In at least one embodiment, CSSD  114  may rank the recommendation scores for a plurality of content to determine an order for providing content to a user. CSSD  114  may, in other embodiments, also provide other content and/or low ranked content to the user to be used to for later recommendation score generation. 
     Devices that may be arranged to operate as CSSD  114  include various network devices, including, but not limited to personal computers, desktop computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, server devices, network appliances, and the like. 
     Although  FIG. 1  illustrates CSSD  114  as a single computing device, the invention is not so limited. For example, one or more functions of the CSSD  114  may be distributed across one or more distinct network devices. Moreover, CSSD  114  is not limited to a particular configuration. Thus, in one embodiment, CSSD  114  may contain a plurality of network devices to select content to provide to a user based on determined recommendation scores of the content. In another embodiment, CSSD  114  may contain a plurality of network devices that operate using a master/slave approach, where one of the plurality of network devices of CSSD  114  operates to manage and/or otherwise coordinate operations of the other network devices. In other embodiments, the CSSD  114  may operate as a plurality of network devices within a cluster architecture, a peer-to-peer architecture, and/or even within a cloud architecture. Thus, the invention is not to be construed as being limited to a single environment, and other configurations, and architectures are also envisaged. In at least one embodiment, each of a plurality of CSSD  114  may be geographically distributed. In another embodiment, CSSD  114  may include a different computing device for monitoring tags in each of a plurality of different tag states. 
     One embodiment of LASD  116  is described in more detail below in conjunction with  FIG. 3 . Briefly, however, LASD  116  includes virtually any network device capable of logging and/or recording data associated with users of client devices  102 - 105 . In some embodiments, the logged data may include information about whether the user accessed a particular piece of content, how long the user accessed the content, did the user share the content with another user, or the like. 
     In at least one embodiment, LASD  116  may collect and store data for each of a plurality of individual users. In some embodiments, LASD  116  may identify and/or distinguish users based on a unique identifier of the user, such as if the user is a subscriber to a website, a cookie previously provided to the client device of the user, a device identifier, or the like. In at least one embodiment, LASD  116  may be enabled to utilize third party subscriptions to track individual users. In some embodiments, LASD  116  may provide the logged data to SGSD  112 . In some embodiments, LASD  116  and CSSD  114  may be associated with same and/or different publishers. In at least one embodiment, LASD  116  and/or CSSD  114  may be referred to and/or associated with third party publishers. 
     Devices that may be arranged to operate as LASD  116  include various network devices, including, but not limited to personal computers, desktop computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, server devices, network appliances, and the like. In at least one embodiment, LASD  116  may be associated with a publisher. In some embodiments, LASD  116  may include a plurality of different devices, where each device may be associated with a different publisher. 
     Although  FIG. 1  illustrates LASD  116  as a single computing device, the invention is not so limited. For example, one or more functions of the LASD  116  may be distributed across one or move distinct network devices. Moreover, LASD  116  is not limited to a particular configuration. In another embodiment, LASD  116  may contain a plurality of network devices that operate using a master/slave approach, where one of the plurality of network devices of LASD  116  operates to manage and/or otherwise coordinate operations of the other network devices. In other embodiments, the LASD  116  may operate as a plurality of network devices within a cluster architecture, a peer-to-peer architecture, and/or even within a cloud architecture. Thus, the invention is not to be construed as being limited to a single environment, and other configurations, and architectures are also envisaged. In at least one embodiment, each of a plurality of LASD  116  may be geographically distributed. In another embodiment, LASD  116  may include a different computing device for monitoring tags in each of a plurality of different tag states. 
     Illustrative Client Device 
       FIG. 2  shows one embodiment of client device  200  that may be included in a system implementing embodiments of the invention. Client device  200  may include many more or less components than those shown in  FIG. 2 . However, the components shown are sufficient to disclose an illustrative embodiment for practicing the present invention. Client device  200  may represent, for example, one embodiment of at least one of client devices  102 - 105  of  FIG. 1 . 
     As shown in the figure, client device  200  includes a processor  202  in communication with a mass memory  226  via a bus  234 . In some embodiments, processor  202  may include one or more central processing units (CPU). Client device  200  also includes a power supply  228 , one or more network interfaces  236 , an audio interface  238 , a display  240 , a keypad  242 , an illuminator  244 , a video interface  246 , an input/output interface  248 , a haptic interface  250 , and a global positioning system (GPS) receiver  232 . 
     Power supply  228  provides power to client device  200 . A rechargeable or non-rechargeable battery may be used to provide power. The power may also be provided by an external power source, such as an alternating current (AC) adapter or a powered docking cradle that supplements and/or recharges a battery. 
     Client device  200  may optionally communicate with a base station (not shown), or directly with another computing device. Network interface  236  includes circuitry for coupling client device  200  to one or more networks, and is constructed for use with one or more communication protocols and technologies including, but not limited to, GSM, CDMA, TDMA, GPRS, EDGE, WCDMA, HSDPA, LTE, user datagram protocol (UDP), transmission control protocol/Internet protocol (TCP/IP), short message service (SMS), WAP, ultra wide band (UWB), IEE 802.16 Worldwide Interoperability for Microwave Access (WiMax), session initialed protocol/real-time transport protocol (SIP/RTP), or any of a variety of other wireless communication protocols. Network interface  236  is sometimes known as a transceiver, transceiving device, or network interface card (NIC). 
     Audio interface  238  is arranged to produce and receive audio signals such as the sound of a human voice. For example, audio interface  238  may be coupled to a speaker and microphone (not shown) to enable telecommunication with others and/or generate an audio acknowledgement for some action. 
     Display  240  may be a liquid crystal display (LCD), gas plasma, light emitting diode (LED), organic LED, or any other type of display used with a computing device. Display  240  may also include a touch sensitive screen arranged to receive input from an object such as a stylus or a digit from a human hand. 
     Keypad  242  may comprise any input device arranged to receive input from a user. For example, keypad  242  may include a push button numeric dial, or a keyboard. Keypad  242  may also include command buttons that are associated with selecting and sending images 
     Illuminator  244  may provide a status indication and/or provide light. Illuminator  244  may remain active for specific periods of time or in response to events. For example, when illuminator  244  is active, it may backlight the buttons on keypad  242  and stay on while the client device is powered. Also, illuminator  244  may backlight these buttons in various patterns when particular actions are performed, such as dialing another client device. Illuminator  244  may also cause light sources positioned within a transparent or translucent case of the client device to illuminate in response to actions. 
     Video interface  246  is arranged to capture video images, such as a still photo, a video segment, an infrared video, or the like. For example, video interface  246  may be coupled to a digital video camera, a web-camera, or the like. Video interface  246  may comprise a lens, an image sensor, and other electronics. Image sensors may include a complementary metal-oxide-semiconductor (CMOS) integrated circuit, charge-coupled device (CCD), or any other integrated circuit for sensing light. 
     Client device  200  also comprises input-output interface  248  for communicating with external devices, such as a headset, or other input or output devices not shown in  FIG. 2 . Input/output interface  248  can utilize one or more communication technologies, such as USB, infrared, Bluetooth™, or the like. 
     Haptic interface  250  is arranged to provide tactile feedback to a user of the client device. For example, the haptic interface  250  may be employed to vibrate client device  200  in a particular way when another user of a computing device is calling. In some embodiments, haptic interface  250  may be optional. 
     Client device  200  may also include GPS transceiver  232  to determine the physical coordinates of client device  200  on the surface of the Earth. GPS transceiver  232 , in some embodiments, may be optional. GPS transceiver  232  typically outputs a location as latitude and longitude values. However, GPS transceiver  232  can also employ other geo-positioning mechanisms, including, but not limited to, triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference (E-OTD), Cell Identifier (CI), Service Area Identifier (SAI), Enhanced Timing Advance (ETA), Base Station Subsystem (BSS), or the like, to further determine the physical location of client device  200  on the surface of the Earth. It is understood that under different conditions, GPS transceiver  232  can determine a physical location within millimeters for client device  200 ; and in other cases, the determined physical location may be less precise, such as within a meter or significantly greater distances. In one embodiment, however, mobile device  200  may through other components, provide other information that may be employed to determine a physical location of the device, including for example, a Media Access Control (MAC) address, IP address, or the like. 
     Mass memory  226  includes a Random Access Memory (RAM)  204 , a Read-only Memory (ROM)  222 , and other storage means. Mass memory  220  illustrates an example of computer readable storage media (devices) for storage of information such as computer readable instructions, data structures, program modules or other data. Mass memory  220  stores a basic input/output system (BIOS)  224  for controlling low-level operation of client device  200 . The mass memory also stores an operating system  206  for controlling the operation of client device  200 . It will be appreciated that this component may include a general-purpose operating system such as a version of UNIX, or LINUX™, or a specialized client communication operating system such as Microsoft Corporation&#39;s Windows Mobile™, Apple Corporation&#39;s iOS™, Google Corporation&#39;s Android™ or the Symbian® operating system. The operating system may include, or interface with a Java virtual machine module that enables control of hardware components and/or operating system operations via Java application programs. 
     Mass memory  226  further includes one or more data storage  208 , which can be utilized by client device  200  to store, among other things, applications  214  and/or other data. For example, data storage  208  may also be employed to store information that describes various capabilities of client device  200 . The information may then be provided to another device based on any of a variety of events, including being sent as part of a header during a communication, sent upon request, or the like. Data storage  208  may also be employed to store social networking information including address books, buddy lists, aliases, user profile information, or the like. Further, data storage  208  may also store message, we page content, or any of a variety of user generated content. At least a portion of the information may also be stored on another component of network device  200 , including, but not limited to processor readable storage media  230 , a disk drive or other computer readable storage devices (not shown) within client device  200 . 
     Processor readable storage media  230  may include volatile, nonvolatile, removable, and non-removable media implemented in any method or technology for storage of information, such as computer- or processor-readable instructions, data structures, program modules, or other data. Examples of computer readable storage media include RAM, ROM, Electrically Erasable Programmable Read-only Memory (EEPROM), flash memory or other memory technology, Compact Disc Read-only Memory (CD-ROM), digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other physical medium which can be used to store the desired information and which can be accessed by a computing device. Processor readable storage media  230  may also be referred to herein as computer readable storage media and/or computer readable storage device. 
     Applications  214  may include computer executable instructions which, when executed by client device  200 , transmit, receive, and/or otherwise process network data. Network data may include, but is not limited to, messages (e.g. SMS, Multimedia Message Service (MMS), instant message (IM), email, and/or other messages), audio, video, and enable telecommunication with another user of another client device. Applications  214  may include, for example, browser  218 , and other applications  220 . Other applications  220  may include, but are not limited to, calendars, search programs, email clients, IM applications, SMS applications, voice over Internet Protocol (VOIP) applications, contact managers, task managers, transcoders, database programs, word processing programs, security applications, spreadsheet programs, games, search programs, and so forth. 
     Browser  218  may include virtually any application configured to receive and display graphics, text, multimedia, messages, and the like, employing virtually any web based language. In one embodiment, the browser application is enabled to employ HDML, WML, WMLScript, JavaScript, SGML, HTML, XML, and the like, to display and send a message. However, any of a variety of other web-based programming languages may be employed. In one embodiment, browser  218  may enable a user of client device  200  to communicate with another network device, such as SGSD  112 , CSSD  114 , LASD  116  of  FIG. 1 . 
     Illustrative Network Device 
       FIG. 3  shows one embodiment of a network device  300 , according to one embodiment of the invention. Network device  300  may include many more or less components than those shown. The components shown, however, are sufficient to disclose an illustrative embodiment for practicing the invention. Network device  300  may be configured to operate as a server, client, peer, a host, or any other device. Network device  300  may represent, for example SGSD  112 , CSSD  114 , LASD  116  of  FIG. 1 , and/or other network devices. 
     Network device  300  includes processor  302 , processor readable storage media  328 , network interface unit  330 , an input/output interface  332 , hard disk drive  334 , video display adapter  336 , and memory  326 , all in communication with each other via bus  338 . In some embodiments, processor  302  may include one or more central processing units. 
     As illustrated in  FIG. 3 , network device  300  also can communicate with the Internet, or some other communications network, via network interface unit  330 , which is constructed for use with various communication protocols including the TCP/IP protocol. Network interface unit  330  is sometimes known as a transceiver, transceiving device, or network interface card (NIC). 
     Network device  300  also comprises input-output interface  332  for communicating with external devices, such as a keyboard, or other input or output devices not shown in  FIG. 3 . Input /output interface  332  can utilize one or more communication technologies, such as USB, infrared, Bluetooth™, or the like. 
     Memory  326  generally includes RAM  304 , ROM  322  and one or more permanent mass storage devices, such as hard disk drive  334 , tape drive, optical drive, and or floppy disk drive. Memory  326  stores operating system  306  for controlling the operation of network device  300 . Any general-purpose operating system may be employed. Basic input/output system (BIOS)  324  is also provided for controlling the low-level operation of network device  300 . 
     Although illustrated separately, memory  326  may include processor readable storage media  328 . Processor readable storage media  328  may be referred to and/or include computer readable media, computer readable storage media, and/or processor readable storage device. Processor readable storage media  328  may include volatile, nonvolatile, removable, and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of processor readable storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other media winch can be used to store the desired information and which can be accessed by a computing device. 
     Memory  326  further includes one or more data storage  308 , which can be utilized by network device  300  to store, among other things, applications  314  and/or other data. For example, data storage  308  may also be employed to store information that describes various capabilities of network device  300 . The information may then be provided to another device based on any of a variety of events, including being sent as part of a header during a communication, sent upon request, or the like. Data storage  308  may also be employed to store messages, web page content, or the like. At least a portion of the information may also be stored on another component of network device  300 , including, but not limited to processor readable storage media  328 , hard disk drive  334 , or other computer readable storage medias (not shown) within client device  300 . 
     Data storage  308  may include a database, text, spreadsheet, folder, file, or the like, that may be configured to maintain and store user account identifiers, user profiles, email addresses, IM addresses, and/or other network addresses; or the like. Data storage  308  may further include program code, data, algorithms, and the like, for use by a processor, such as processor  302  to execute and perform actions. In at least one embodiment data storage  308  may include at least one state table to indicate which tags are in a particular state. In one embodiment, at least some of data store  308  might also be stored on another component of network device  300 , including, but not limited to processor-readable storage media  328 , hard disk drive  334 , or the like. 
     Applications  314  may include computer executable instructions, which may be loaded into mass memory and run on operating system  306 . Examples of application programs may include transcoders, schedulers, calendars, database programs, word processing programs, Hypertext Transfer Protocol (HTTP) programs, customizable user interface programs, IPSec applications, encryption programs, security programs, SMS message servers, IM message servers, email servers, account managers, and so forth. Applications  314  may also include website server  318 , Logging Agent Application (LAA)  319 , Score Generator Application (SGA)  320 , and Content Selection Application (CSA)  321 . 
     Website server  318  may represents any of a variety of information and services that are configured to provide content, including messages, over a network to another computing device. Thus, website server  318  can include, for example, a web server, a File Transfer Protocol (FTP) server, a database server, a content server, or the like. Website server  318  may provide the content including messages over the network using any of a variety of formats including, but not limned to WAP, HDML, WML, SGML, HTML, XML, Compact HTML (eHTML), Extensible HTML (xHTML), or the like. 
     LAA  319  may be configured to collect and/or store data associated with a plurality of users, as described above in conjunction with LASD  116  of  FIG. 1 . In some embodiments, LAA  319  may be employed by LASD  116  of  FIG. 1 . In any event, LAA  319  may employ processes, or parts of processes, similar to those described in conjunction with  FIGS. 4-5 , to perform at least some of its actions. 
     SGA  320  may be configured to determine a recommendation score of content for a user, as described above in conjunction with SGSD  112  of  FIG. 1 . In some embodiments, SGA  320  may be employed by SGSD  112  of  FIG. 1 . In any event, SGA  320  may employ processes, or parts of processes, similar to those described in conjunction with  FIGS. 4-5 , to perform at least some of its actions. 
     CSA  321  may be configured to select content to provide to a user based on a recommendation score of the content, as described above in conjunction with CSSD  114  of  FIG. 1 . In some embodiments, CSA  321  may be employed by CSSD  114  of  FIG. 1 . In any event, CSA  321  may employ processes, or parts of processes, similar to those described in conjunction with  FIGS. 4-5 , to perform at least some of its actions. 
     General Operation 
     The operation of certain aspects of the invention will now be described with respect to  FIGS. 4-6 .  FIG. 4  illustrates a logical flow diagram generally showing one embodiment of an overview process for providing content to a user based on a recommendation score of the content for the user. In some embodiments, process  400  of  FIG. 4  may be implemented by and/or executed on a single network device, such as network device  300  of  FIG. 3 . In other embodiments, process  400  or portions of process  400  of  FIG. 4  may be implemented by and/or executed on a plurality of network devices, such as network device  300  of  FIG. 3 . 
     Process  400  begins, after a start block, at block  402 , where a piece of content may be selected. In at least one embodiment, the content may be selected by a publisher, editor, or the like. In some embodiments, the content may be selected from a plurality of different possible content that may be provided to a user (also referred to herein as a reader). For example, a plurality of possible content may include, but is not limited to, “today&#39;s top stories,” content regarding breaking news, most popular content among readers, editorially-curated content of special importance, randomly selected content, or the like. In some embodiments, process  400  may be employed for each of the plurality of possible content that may be provided to a user. Accordingly, a subset of the plurality of possible content may be provided to the user based on a calculated recommendation score for each piece of content (as described in more detail below). 
     Process  400  continues at block  404 , where an intrinsic value of the content may be determined. In at least one embodiment, the intrinsic value may be determined by employing one or more statistical analysis tools on historical data associated with the user. In at least one embodiment, the historical data may include previous content provided to the user and actions taken by the user on the previous content. Such actions may include, but are not limited to, clicking on the previous content, sharing the previous content, “liking” the previous content, how long the user accessed the previous content, or the like. 
     In some embodiments, the intrinsic value of the selected content may be calculated based on a type of action performed by the user on previous content that is similar to the selected content. In other embodiments, the intrinsic value of the selected content may be calculated based on number of times the user accessed previous content similar to the selected content. In yet other embodiments, the intrinsic value of the selected content may be calculated based on a time spent by the user accessing previous content similar to the selected content. However, embodiments are not so limited and other methods and/or algorithms may be employed to determine the intrinsic value of the content to the user. Content similar to selected content may be based on keywords within the content, a category of the content (e.g., sports, politics, or the like), type of content (e.g., news article, video, blog post, or the like), an author and/or publisher of the content, or the like, or any combination thereof. 
     In some other embodiments, the intrinsic value may be selected by the user, such that the intrinsic value of the selected content may be based on a category of the content and the user selected intrinsic value for that category. In at least one such embodiment, the user may be enabled to determine and/or select intrinsic values for different categories of content. For example, the user may select an intrinsic value of ‘1’ for content categorized as political (i.e., the user is not interested in political related content) and a ‘9’ for content categorized as sports (i.e., the user is very interested in sports related content). In other embodiments, a user may be enabled to select intrinsic values for subcategories. For example, the category of sports may include subcategories of baseball and football. In this example, the user may select an intrinsic value of ‘2’ for baseball (i.e., the user is not interested in baseball related content) and ‘9.3’ for football (i.e., the user is very interested in football related content). However, embodiments are not so limited and other algorithms and/or metrics may be employed to determine an intrinsic value of the selected content to the user. 
     Process  400  proceeds next to block  406 , which is described in more detail below in conjunction with  FIG. 5 . Briefly, however, at block  406 , a social value of the content may be determined. In at least one embodiment, the social value may be determined based on a combination of an individual social value of the selected content for each of a plurality of friends of the user. 
     Process  400  continues next at block  408 , where a recommendation score of the content for the user may be calculated. In at least one embodiment, the recommendation score may be calculated based on a combination of the intrinsic value and the social value. In some embodiments, the intrinsic value may be added to the social value. However, embodiments are not so limited and other methods and/or algorithms may be employed for combining the intrinsic value and the social value. A use case illustration of an equation that may be employed to calculate the recommendation score of the selected content for the user is described in more detail below. 
     In at least one of various embodiments, an intrinsic weight may be applied to the intrinsic value. In at least one embodiment, the intrinsic weight may be multiplied by the intrinsic value. In some embodiments, employing an intrinsic weight may indicate how much the user may value the personal relevance of the selected content (i.e., the intrinsic value) compared to the social relevance of the selected content (i.e., the social value). For example, in some embodiments, the intrinsic weight may be increased if it is more important to the user to be provided articles related to the user&#39;s personal interests rather than articles related to the interests of the user&#39;s social network. However, in other embodiments, the intrinsic weight may be decreased if it is more important to the user to be provided articles related to the interest of the user&#39;s social network rather than articles related to the user&#39;s personal interests. 
     The intrinsic weight may be static or dynamic. In at least one embodiment, an initial intrinsic weight may be determined, such as, for example, by an operator, publisher, or the like. In one non-limiting, non-exhaustive example, the initial intrinsic weight may be determined such that the intrinsic value and the social value are equally important to the user. In some embodiments, the intrinsic weight may change over time based on how the user interacts with content. For example, if the user continuously clicks on content that has a higher social value than intrinsic value, then the social value may be more important to the user, which may result in a decrease in the intrinsic weight. However, if the user continuously clicks on content that has a higher intrinsic value than social value, then the intrinsic value may be more important to the user, which may result in an increase in the intrinsic weight. 
     In some other embodiments, a social weight may be applied to the social value. In at least one such embodiment, the social weight may be employed in a manner similar to the intrinsic weight. For example, the social weight may be multiplied by the social value. 
     In any event, process  400  continues at block  410 , where the content may be provided to the user. In some embodiments, providing content to the user may include displaying the content to the user in a personalized content stream on a webpage visited by the user, the user&#39;s home page, or the like. In at least one embodiment, the content may be provided based on the calculated recommendation score. For example, the calculated recommendation score may be employed to determine whether or not to provide the selected content to the user, an order of providing the selected content along with other content to the user, or the like. 
     As described above, process  400  may be employed for a plurality of possible content to be provided to the user. In some embodiments, a recommendation score (as calculated at block  408 ) of each piece of possible content may be utilized to determine which content to provide to the user. In other embodiments, the calculated recommendation score of each piece of content may be ranked for the user. The content may be ordered and provided to the user based on the ranking of recommendation scores. For example, content with the top x number of recommendation scores (e.g., the top five recommendation scores) may be ordered and provided to the user. 
     In another embodiment, content with a recommendation score above a threshold value may be provided to the user. The threshold value may be determined by a user and/or an operator. In some embodiments, the threshold value may change over time based on actions by the user on content provided to the user. For example, if the user typically accesses content with a recommendation score above a particular value, then this value may be determined to be the threshold value. 
     In some other embodiments, content with a recommendation score that does not meet the requirements for being provided to the user (e.g., not at the top of the ranking and/or not above the recommendation score threshold value) may still be provided to the user. For example, 10-30% of the remaining content that did not make the cut may be shown to the user (e.g., in the personalized content stream). Various heuristics may be employed to determine which of the remaining content to provide to the user, including, but not limited to, content representing breaking news, editorially-curated content of special importance, most popular content, randomly selected content, or the like, or any combination thereof. 
     In at least one embodiment, by providing this additional content to the user, additional data may be collected and/or analyzed to dynamically modify how the recommendation score of content is calculated (e.g., modifying the intrinsic weight applied to the intrinsic value, modifying an interest probability (P(F i ,X,U) as described below), modifying a weight of a friend (a i  as described below), or the like. 
     After block  410 , process  400  may return to a calling process to perform other actions. 
     Ideas described herein are applicable not only to online publishing; but virtually any realm where social feedback loops are involved—and are measurable—may benefit from employing embodiments of process  400 , as described above. Some non-limiting, non-exhaustive examples include, but are not limited to online gaming, e-commerce, advertisement targeting, or the like. 
     In some embodiments, in multi-player online role playing games, a user might be suggested a particular path of character upgrades because his guild members appreciate this kind of support from their friends (where recommendations scores of character upgrades may be calculated similar to calculating recommendations scores of content as described above). For example, the intrinsic value may be a gamer&#39;s personal interest in playing a particular character, using particular skills or attributes, using particular weapons, or the like. The social value may be the interest that the gamer&#39;s friends (e.g., his guild members) have for—or the interest the gamer&#39;s friends have in the gamer using—the particular character, skills, upgrades, attributes, or the like. A recommendation store may then be determined (by employing embodiments described herein) for each of a plurality of different characters, skills, upgrades, or the like, to suggest and/or influence the gamer&#39;s character selections. These suggestions may enable gamers to create and play characters that may be most beneficial to their friends/guild as a whole. 
     In other embodiments, in an online shopping site that maintains a user identity, a user might be suggested a set of purchases (i.e., content) that their friends are most likely to value, based upon their prior purchases. For example, if a user&#39;s friend bought a last years&#39; model of a product, it is likely going to be fruitful to suggest this years&#39; model of this product to the user. In at least one example, the intrinsic value may be a purchaser&#39;s interest in a particular product, line of products, supplier, brand, or the like. The social value may be the interest that the purchaser&#39;s friends have in the particular product, line of products, supplier, brand, or the like. A recommendation score may then be determined (by employing embodiments described herein) for each of a plurality of different products, to suggest products to purchase (or provide advertisements for) such that the purchaser looks good to her friends. 
     By employing embodiments described above, in some other embodiments, advertisements (i.e., content) may be determined to be provided to a user. In some embodiments, those advertisements (or other content) may be provided and/or sold to one or more publishers. In at least one embodiment, a publisher may be enabled to opt out from having data collected about its users and used to generate recommendation scores of content for another publisher to provide to the other publisher&#39;s users. 
       FIG. 5  illustrates a logical flow diagram generally showing one embodiment of a process for determining a social value of content to a user. In some embodiments, process  500  of  FIG. 5  may be implemented by and/or executed on a single network device, such as network device  300  of  FIG. 3 . In other embodiments, process  500  or portions of process  500  of  FIG. 5  may be implemented by and/or executed on a plurality of network devices, such as network device  300  of  FIG. 3 . 
     Process  500  begins, after a start block, at block  502 , where a friend of the user may be selected. In at least one of the various embodiments, the friend may be a first-degree friend of the user&#39;s social network. In some embodiments, the friend may be selected based on a contact list of the user, other users that the user has been in contact with (e.g., send/receive emails, text messages, or other person-to-person online communications, sharing content, or the like), or the like. 
     Process  500  proceeds to block  504 , where a recommendation score of content (i.e., the selected content at block  402  of  FIG. 4 ) for the selected friend may be determined. In at least one embodiment, the recommendation score of the selected content for the selected friend may be determined by recursively employing blocks  404 ,  406 , and  408  of  FIG. 4  for the selected friend. Accordingly, calculating the recommendation score of the selected content for the user, as described by blocks  404 ,  406 , and  408  of  FIG. 4 , may be determined by traversing one or more levels of the social network tree of the user. 
     In some embodiments, recommendation score for the selected friend may be recursively determined up to a predetermined degree of friends of the user. For example, recommendations scores may be recursively determined up to third-degree friends of the user. In at least one embodiment, the recommendation score for a friend at the predetermined degree of friends may be determined based on the intrinsic value (as calculated by block  404  of  FIG. 4 ) of the content for those friends and not the social value (as calculated by block  406  of  FIG. 4 ). For example, if the predetermined degree of friends is first-degree friends of the user, then the recommendation score of the content for the selected friend (i.e., first-degree friend) may be an intrinsic value of the content to the selected friend. In some embodiments, ending this recursive calculation may be referred to a trimming the social network tree of the user. 
     In any event, process  500  next proceeds to block  506 , where an interest probability for the selected friend may be determined. In at least one of various embodiments, the interest probability may be a probability that the user and the selected friend will perform an action on the selected content. In some embodiments, the interest probability may be based on the selected friend performing an action on a piece of content after the user performs an action on the same piece of content. In other embodiments, the interest probability may be based on the user performing an action on a piece of content after the selected friend performs an action on the same piece of content. Actions performed on the content may include, but are not limited to, accessing the content, sharing the content, or the like. 
     In at least one embodiment, the interest probability for the selected friend may be determined by employing one or more statistical tools on historical data associated with the user and the selected friend. However, embodiments are not so limited and other methods and/or algorithms may be employed to determine the interest probability for the selected friend for the selected content. In some embodiments, the historical data may include actions performed by the user and the selected friend, including which content was read by the user, which friends accessed the content (e.g., if the user shared the content with the friends), or the like. In some embodiments, a unique link may be generated to share content from the user to a friend, which may be utilized to determine which friends accessed the content. 
     In other embodiments, the historical data may identify actions performed on different features of the content, including but not limited to, given pieces of content, types of content (e.g., videos, articles, or the like), categories of content (e.g., sports, health, or the like), authors of content, or the like. In some embodiments, the features may be determined based on a natural language search of content, tags of content, collaborative filters, or the like. In some embodiments, the historical data may indicate who performed an action on the content first, the timeframe between actions, or the like. In some embodiments, the historical data may be utilized to generate a feature to friend matrix, which may determine the interest probability for the selected content for the selected friend. 
     In some embodiments, the interest probability may be unique for different content features (e.g., types of content, categories of content, authors of content, or the like) for the selected friend. In other embodiments, the interest probability for the selected friend may be an average of these different feature probabilities. However, embodiments are not so limited and other methods and/or algorithms may be employed to determine the interest probability. 
     In any event, the interest probability for the selected friend may be static or dynamic. In at least one embodiment, the interest probability of the content and the selected friend may change over time based on actions by the friend and the user. For example, if the user tends to read the same sports articles that Friend 1 reads, then the interest probability may increase for sports articles. 
     Process  500  continues at block  508 , where a weight (i.e., a social weight) of the selected friend may be determined. In at least one embodiment, the weight may indicate how influential the selected friend is to the user. In another embodiment, the weight may indicate how much the user values and/or appreciates the selected friend&#39;s&#39; opinion. In some embodiments, each friend of the user (i.e., first-degree friend) may be associated with a unique weight. 
     In some other embodiments, the weight may indicate how influential the user is on the selected friend. In at least one such embodiment, a publisher may be able to indirectly attract additional readers by employing an increased weight if a user is very influential on a given friend. For example, if the publisher knows that the user is very influential on the selected friend, then there may be an increased likelihood that the user will tell the friend about content the user accessed, which may result in the friend later accessing the same content. 
     The weight of the selected friend may be static or dynamic. In at least one embodiment, the weight for each friend may be determined, such as, for example, by an operator, publisher, or the like. In one non-limiting, non-exhaustive example, the initial weight may be determined such that each friend is weighted as equally important to the user. In at least one embodiment, the weight of a friend may change (increase and/or decrease) over time based on actions by the friend and the user. For example, if the user tends to read the same articles that Friend 1 reads, then Friend 1 may be very influential to the user, which may result in an increase in the weight. In some other embodiments, the user may be enabled to select the weight of a friend. For example, the user may be enabled to indicate that Friend 2 is more influential than Friend 1, but less influential than Friend 3, and so on. 
     Process  500  proceeds to block  510 , where the individual social value of the content for the selected friend may be calculated. In at least one of various embodiments, the individual social value for the selected friend may be calculated based on a combination of the weight (as determined at block  508 ), the recommendation score for the selected friend (as determined at block  504 ), and the interest probability for the selected friend (as determined at block  506 ). In some embodiments, the weight, recommendation score, and interest probability may be multiplied together. 
     In some embodiments, the individual social value may be an indication of the overall perception of the user within the user&#39;s social network. For example, in some situations, a user may appear smarter to his/her friends (i.e., the user&#39;s social network) if the user reads an article before the friends do and the article is of interest to the friends. In this situation, if a friend later reads the article, then the interest probability for the friends may increase for similar content, which may increase the individual social value of similar content for the friend. In other situations, a user may be more engaged in a discussion with his/her friends if the user reads an article that was also read by the friends. In both of these situations, the interest probability for friends that also access the content may increase for similar content, which may increase the individual social value of similar content for the friend. 
     In any event, process  500  contains at decision block  512 , where a determination may be made whether to select another friend of the user. In some embodiments, another friend may be selected until all first degree friends of the user have been selected. In at least one of various embodiments, a user&#39;s friends may be grouped into subsets of friends, such as, but not limited to, school friends, online social friends, work friends, family members, or the like. In one such embodiment, another friend may be selected until a subset of the first-degree friends of the user has been selected. If another friend may be selected, then process  500  may loop to block  502  to select another friend; otherwise, process  500  may flow to block  514 . 
     At block  514 , the individual social value for each friend may be combined to calculate the social value of the content to the user. In some embodiments, the social value may be the sum of the individual social value of each first-degree friend of the user. 
     After block  514 , process  500  may return to a calling process to perform other actions. 
       FIG. 6  illustrates a logical flow diagram generally showing one embodiment of a process for determining content to provide to a user. In some embodiments, process  600  of  FIG. 6  may be implemented by and/or executed on a single network device, such as network device  300  of  FIG. 3 . In other embodiments, process  600  or portions of process  600  of  FIG. 6  or may be implemented by and/or executed on a plurality of network devices, such as network device  300  of  FIG. 3 . 
     Process  600  begins, after a start block, at block  602 , where a plurality of possible content may be determined. In some embodiments, the plurality of possible content may be selected by a publisher, editor, or the like. In at least one embodiment, the possible content may include content that can be provided to a user at a particular time, for a particular day, when a user accesses a channel, or the like. Examples of the plurality of possible content may include, but are not limited to, “today&#39;s top stories,” content regarding breaking news, most popular content among readers, editorially-curated content of special importance, randomly selected content, or the like. 
     In at least one of various embodiments, the plurality of possible content may include content associated with a plurality of different categories, such as, for example, sports, politics, economics, and the like. In at least one such embodiment, each category may include sub-categories. For example, the sports category may include sub-categories for different types of sports (e.g., tennis, basketball, football, or the like). 
     Process proceeds to block  604 , where one or more social networks associated with the user may be identified. These social networks may include, but are not limited to, family network, work network, college network, close personal friend network, or the like. In some embodiments, these different social networks may be determined by the user (e.g., if the user identifies which friends are members of which social network). 
     In other embodiments, the different social networks may be automatically determined based on clusters of friends. For example, various clustering algorithms may be employed to determine one or more social networks based on a correlation of friends and friends of friends (e.g., users A, B and C may be clustered into a same social network if user A is friends with users B and C, and user B is friends with user C. However, embodiments are not so limited, and other algorithms and/or mechanisms may be employed to determine and/or identify the users one or more social networks. It should be recognized that friends of the user may be members of more than one social network of the user. 
     Process  600  continues at decision block  606 , where one of the identified social networks may be selected. In some embodiments, each different social network may be separately selected, such that each social network associated with the user may be selected once. If an identified social network is selected, then process  600  may flow to decision block  608  for the selected social network; otherwise, process  600  may flow to block  614 . 
     At decision block  608 , possible content may be selected. In some embodiments, each of the plurality of possible content may be separately selected, such that each piece of content may be selected once for each separate social network. If a possible content is selected, then process  600  may flow to block  610 ; otherwise, process  600  may flow to block  612 . 
     At block  610 , a recommendation score of the selected content may be determined for the user for the selected social network. In at least one of various embodiments, block  610  may employ embodiments of blocks  404 ,  406 , and  408  of  FIG. 4  to determine the recommendation score of the selected content. Since each piece of content may be selected for each social network, each piece of possible content may have a separate recommendation score determined for each corresponding social network. After block  610 , process  600  may loop to decision block  608  to select another piece of possible content. 
     If, at decision block  608 , another piece of possible content is not selected, then process  600  may flow from decision block  608  to block  612 . At block  612 , the plurality of possible content may be rank ordered based on the determined recommendation scores for the selected social network. After block  612 , process  600  may loop to decision block  606  to select another identified social network. 
     If, at decision block  606 , another social network of the user is not selected, then process  600  may proceed from decision block  606  to block  614 . At block  614 , a portion of the possible content may be provided to the use. In various embodiments, one or more pieces of ranked content for each social network may be provided to the user. In some embodiments, a predetermined number of top ranked possible content for each social network (e.g., top two ranked pieces of content for each social network) may be provided to the user. For example, the user may be provided two pieces of content with a highest rank for their work network, two other pieces of content with a highest rank for their family network, and so on. Since various social networks may have different social interests, such embodiments may allow a user to view content that is most valuable and/or applicable to the interests (e.g., highest ranking) of each of the different social networks of the user. 
     In other embodiments, the number of pieces of content to provide to the user for each social network may be determined based on a user&#39;s interests and/or weights for each social network. In at least one embodiment, these social network weights may be determined by the user. For example, the user may indicate that he would like to receive four highest-ranked pieces of content for his work network and two highest-ranked pieces of content for his family network. In at least one such embodiment, the user may assign a higher social network weight to his work network than to his family network. 
     In another embodiment, the social network weights may be determined based on actions performed by the user. For example, if the user views more content associated with his close personal friend network, than his work network, then the close personal friend network may be assigned a higher weight than their work network. 
     However, embodiments are not so limited and other mechanisms and/or algorithms may be employed to determine which of the ranked content for each social network may be provided to the user. For example, in other embodiments, content that is ranked above a predetermined threshold ranking (e.g., top five ranking) for a plurality of different social networks may be provided to the user. Such an embodiment may provide content to the user that is applicable to multiple social networks of the user. The various embodiments described herein may enable a user to receive more content that is relevant to specific social networks than other social networks, but may still receive content that is relevant to at least some of the other social networks. 
     After block  614 , process  600  may return to a calling process to perform other actions. 
     It will be understood that each block of the flowchart illustration, and combinations of blocks in the flowchart illustration, can be implemented by computer program instructions. These program instructions may be provided to a processor to produce a machine, such that the instructions, which execute on the processor, create means for implementing the actions specified in the flowchart block or blocks. The computer program instructions may be executed by a processor to cause a series of operational steps to be performed by the processor to produce a computer-implemented process such that the instructions, which execute on the processor to provide steps for implementing the actions specified in the flowchart block or blocks. The computer program instructions may also cause at least some or the operational steps shown in the blocks of the flowchart to be performed in parallel. Moreover, some of the steps may also be performed across more than one processor, such as might arise in a multi-processor computer system. In addition, one or more blocks or combinations of blocks in the flowchart illustration may also be performed concurrently with other blocks of combinations of blocks, or even in a different sequence than illustrated without departing from the scope or spirit of the invention. 
     Accordingly, blocks of the flowchart illustration support combinations of means for performing the specified actions, combinations of steps for performing the specified actions and program instruction means for performing the specified actions. It will also be understood that each block of the flowchart illustration, and combination of blocks in the flowchart illustration, can be implemented by special purpose hardware-based systems, which perform the specified actions or steps, or combinations of special purpose hardware and computer instructions. The foregoing example should not be construed as limiting and/or exhaustive, but rather, an illustrative use case to show an implementation of at least one of the various embodiments of the invention. 
     Illustrative Use Case Recommendation Score Algorithm 
     As described above, a recommendation score (which may also be referred to as a Social Value Recommendation Score) may be determined for a given piece of content for a particular user. An embodiment for determining the Social Value Recommendation Score may be recursively defined as: 
               S   ⁡     (     X   ,   U     )       =         a   0     *     N   ⁡     (     X   ,   U     )         +       ∑     i   =   1     n     ⁢     (       a   i     *     P   ⁡     (       F   i     ,   X   ,   U     )       *     S   ⁡     (     X   ,     F   i       )         )               
where, each component of the equation is described in more detail below.
 
     X may refer to a given piece of content. In some embodiments, content X may be content that may be provided to user U, such as content displayed on a homepage (or other webpage) of user U. 
     U may refer to a particular user that may be enabled to access content X. In some embodiments, the S(X,U), as described above, may be determined for content X when user U accesses a webpage. In some embodiments user U may be identified when the user logs into a webpage, based on a cookie or other unique identifier, or the like. 
     S(X,U) may be the Social Value Recommendation Score assigned to content X for user U. Embodiments may be employed to maximize S(X,U) with respect to parameters a i  and/or a 0  using a known training set of data. In at least one embodiment, the training set of data may include content (e.g., content X) and/or a social graph of users. In some embodiments, the social graph of users may include friends of user U, which may include first degree friends, second degree friends, or the like. In various embodiments, the overall score may be a function that determines how to combine scores (S(X,U)) of each of the user&#39;s friends by employing weights a i  and the intrinsic value (N(X,U)) to the user. 
     N(X,U) may be the intrinsic value that user U obtains from content X. In at least one embodiment, N(X,U) may be calculated by employing embodiments of block  404  of  FIG. 4 . In some embodiments, the intrinsic value may be calculated based on a time spent by user U accessing content similar to content X. In other embodiments, the intrinsic value may be calculated based on a number of times user U may share content similar to content X. In some embodiments content similar to content X may be based on keywords within the content, a category of the content (e.g., sports, politics, or the like), type of content (e.g., news article, video, blog post, or the like), or the like, or any combination thereof. 
     a 0  may be the weigh of the intrinsic value for user U. In some embodiments, a user may be impacted greater by the intrinsic value (N(X,U)) (i.e., the intrinsic value may be more important to the user) than the social value and thus, may have a higher value of a 0 , than a user who may be impacted more by the social perception (i.e., the social value) than the intrinsic value. 
     n may be the number of friends that user U has. In at least one embodiment, n may be the number of first-degree friends in the social network of user U. 
     F i  may be each friend of user U. In at least one embodiment, F i  may be each first degree friend of user U. 
     Σ i=1   n (a i *P(F i ,X,U)*S(X,F i )) may be the social value of friends F i  of user U for content X. In at least one embodiment, this social value may be calculated by employing embodiments of block  406  of  FIG. 4 . In some embodiments, a i *P(F i ,X,U)*S(X,F i ) may be referred to as the individual social value of content X for friend F i . 
     S(X,F i ) may be the Social Value Recommendation Score assigned to content X for friend F i . In at least one embodiment, S(X,F i ) may be calculated by employing embodiments of block  504  of  FIG. 4 , which may include employing embodiments of blocks  404 ,  406 , and  408  of  FIG. 4 , where F i  is the user (i.e., user U in the recommendation score equation above). 
     P(F i ,X,U) may be the probability that user U will be interested in and/or will access content X after friend F i  accesses content X. In at least one embodiment, this probability may be calculated by employing embodiments of block  506  of  FIG. 5 . In some embodiments, the probability may be determined from mining historical behavior of user U in response to actions of friend F i , combined with factor analysis of content similar to content X. 
     a i  may be the weight of how influential friend F i  is to user U. In at least one embodiment, a i  may be calculated by employing embodiments of block  508  of  FIG. 5 . In some embodiments, one or more of friends F i  may be more influential to user U, which may have more impact on the overall score (S(X,U)), than less influential friends. For example, a friend that is closer to user U in the offline world may be more influential and have a higher weight a i , than an online-only friend. 
     In some embodiments, S(X,F i ) may be specified using a linear combination of weights, such as described above. In other embodiments, S(X,F i ) may be based on and/or include second and higher order effects into the intrinsic value placed by the user&#39;s friends. At least one such embodiment may model the fact that if several of friends F i  are interested in topic T, then there may be a non-linear return on investment for user U to become knowledgeable and/or share content X that covers topic T. 
     As described above, S(X,F i ) may be recursively calculated. In some embodiments, the function may be recursively performed a predetermined number of times (i.e., prune the tree of social value after a certain number of recursive steps away from the original user). In at least one embodiment, the number of time S(X,F i ) may recursively be performed may be determine experimentally for a given social graph, by performing a statistical evaluation of a given social graph, or the like. For example, in one embodiment, a contribution of each time S(X,F i ) is recursively performed may be calculated. S(X,F i ) may continue to be recursively performed until the calculated contribution is less than a threshold value. In some embodiments, the more recursive steps performed, the more computationally expensive the algorithm may become. In at least one embodiment, probabilities P(F i ,X,U) and social weights a i  may be values bounded by 0 and 1. In such an embodiment, each recursive step away from user U may provide less and less incremental value to S(X,F i ), which may result in S(X,F i ) converging on a particular value (e.g., when the calculated contribution is less than a defined threshold value). 
     Illustrative Use Case Social Network 
       FIG. 7  illustrates a use case embodiment of a social network of a user that may be utilized to determine a recommendation score for a user. Social network  700  may be a tree of friends of user  702 . Social network  700  may include user  702 , first-degree friends  726  and second-degree friends  726 . First-degree friends  726  may be first-degree friends of user  702  and may include friends  704 - 707 . Second-degree friends  728  may be second-degree friends of user  702 . Although only two degrees of friends are illustrated, additional degrees of friends may also be employed. 
     As described above, a recommendation score of content for user  702  may be calculated based on a combination of an intrinsic value and a social value. The intrinsic value for user  702  may include intrinsic value  703 , which may be an embodiment of N(X,U), as described above. The social value for user  702  may be calculated based on a combination of an individual social value for each of the user&#39;s friends (friends  704 - 707 ). The individual social value for each friend may be determined based on a combination of a social weight, an interest probability, and a recommendation score. As illustrated, each friend may be associated with a unique social weight, interest probability, and/or recommendation score for the given content. 
     For example, the individual social value for friend  704  may be calculated based on a combination of weight  710 , interest probability  712 , and recommendation score  714 . Weight  710  may be an embodiment of a i , as described above. Interest probability may be an embodiment of P(F i ,X,U), as described above. Recommendation score  714  may be an embodiment of S(X,F i ), as described above. 
     In some embodiments, recommendation score  714  may be determined for friend  704  by combining intrinsic value  715  for friend  704  (e.g., as determined at block  404  of  FIG. 4 ) and a social value of the content to friend  704  (e.g., as determined at block  406  of  FIG. 4 ). As described above, the social value for user  704  may be calculated based on a combination of an individual social value for each friend of friend  704  (including friend  722 ). The individual social value for friend  722  may be determined based on a combination of weight  716 , interest probability  718 , and recommendation score  720 . Although  FIG. 7  illustrates ellipses to indicate additional degrees of separation from user  702 , embodiments may be constrained to a predetermined degree of separation. If the recursive function for determining the recommendation score for user  702  is stopped at second-degree friends  728 , then recommendation score  720  may be based on intrinsic value  723  for friend  722 . 
     The above specification, examples, and data provide a complete description of the composition, manufacture, and use of the invention. Since many embodiments of the invention can be made without departing from the spirit and scope of the invention, the invention resides in the claims hereinafter appended.