Abstract:
A device and server for participating in a network for sharing media consumption activity is disclosed. For example, the device receives information identifying a media item as a result of playing the media item on a second user device associated with a friend of the plurality of friends and the friend of a plurality of friends. The device presents information identifying the media item selected for play on the second user device and the friend associated with the second user device and identifying a plurality of other media items and a subset of the plurality of friends. The device obtains a selection of a second media item for play, the second media item, and initiates play of the media item. The device provides, as a result of playing the second media item, information identifying the second media item and the user for sharing with the plurality of friends of the user.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is a continuation of co-pending U.S. patent application Ser. No. 13/693,785, filed Dec. 4, 2012, entitled GRAPHICAL USER INTERFACE SYSTEM FOR ALLOWING MANAGEMENT OF A MEDIA ITEM PLAYLIST BASED ON A PREFERENCE SCORING SYSTEM, which is a continuation of U.S. patent application Ser. No. 11/750,002, filed May 17, 2007, entitled GRAPHICAL USER INTERFACE SYSTEM FOR ALLOWING MANAGEMENT OF A MEDIA ITEM PLAYLIST BASED ON A PREFERENCE SCORING SYSTEM, which is a continuation-in-part patent application of U.S. application Ser. No. 11/484,130, filed Jul. 11, 2006, entitled P2P NETWORK FOR PROVIDING REAL TIME MEDIA RECOMMENDATIONS, all of which are incorporated herein by reference in their entirety. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention relates to graphical user interfaces (GUIs) and related systems provided on a peer device in a network, such as a peer-to-peer network, to allow a user to define user preferences and/or develop a user profile. The preferences and profile are used to filter media item recommendations received by the peer device, score the media item recommendations according to the user preferences and/or user profile, and allow management of a media item playlist on the peer device based on media item scoring. 
       BACKGROUND 
       [0003]    In recent years, there has been an enormous increase in the amount of digital media, such as music, available online. Services such as Apple&#39;s iTunes enable users to legally purchase and download music. Other services such as Yahoo! Music Unlimited and RealNetwork&#39;s Rhapsody provide access to millions of songs for a monthly subscription fee. As a result, music has become much more accessible to listeners worldwide. In this regard, graphical user interfaces are often provided to user devices to allow the user to retrieve, navigate and otherwise manage their media collection. However, the increased accessibility of music has only heightened a long-standing problem for the music industry, which is namely the issue of linking audiophiles with new music that matches their listening preferences. 
         [0004]    Many companies, technologies, and approaches have emerged to address this issue of music recommendation. Some companies have taken an analytical approach. They review various attributes of a song, such as melody, harmony, lyrics, orchestration, vocal character, and the like, and assign a rating to each attribute. The ratings for each attribute are then assembled to create a holistic classification for the song that is then used by a recommendation engine. The recommendation engine typically requires that the user first identify a song that he or she likes. The recommendation engine then suggests other songs with similar attributions. Companies using this type of approach include Pandora (pandora.com), SoundFlavor (soundflavor.com), MusicIP (musicip.com), and MongoMusic (purchased by Microsoft in 2000). 
         [0005]    Other companies take a communal approach. They make recommendations based on the collective wisdom of a group of users with similar musical tastes. These solutions first profile the listening habits of a particular user and then search similar profiles of other users to determine recommendations. Profiles are generally created in a variety of ways such as looking at a user&#39;s complete collection, the playcounts of their songs, their favorite playlists, and the like. Companies using this technology include Last.fm (last.fm), Music Strands (musicstrands.com), WebJay (webjay.org), Mercora (mercora.com), betterPropaganda (betterpropaganda.com), Loomia (loomia.com), eMusic (emusic.com), musicmatch (mmguide.musicmatch.com), genielab (genielab.com/), upto11 (upto11.net/), Napster (napster.com), and iTunes (itunes.com) with its celebrity playlists. 
         [0006]    The problem with these traditional recommendation systems is that they fail to consider peer influences. For example, the media items that a particular teenager listens to and/or views may be highly influenced by the media items listened to or viewed by a group of the teenager&#39;s peers, such as his or her friends. Media item recommendations from a user&#39;s peers may be provided through a social network, such as, for example, a peer-to-peer network. 
         [0007]    Similar to a company generating media item recommendations based on a user&#39;s profile, a user may desire to filter peer media item recommendations received by his or her peer device based on the user&#39;s preferences and profile. However, to effectively filter peer media item recommendations, the user has to provide information to the peer device from which user preferences may be determined and a user profile may be developed. In addition, the user may desire the ability to control the manner in which his or her preferences and profile are applied to the peer media item recommendations, and, generally, to manage the peer media item recommendations on the peer device. 
         [0008]    Further, even though media item recommendations can be provided as an effective tool to target media items sent to a user, such as in a peer-to-peer network, the user may not desire to listen to or view all of the peer recommendations received by the user&#39;s peer device. The user must navigate through his or her media item collection on a graphical user interface to select media items of interest. The user&#39;s media collection, which may consist of user directed selections and received media item selections, may contain hundreds if not thousands of media items to navigate. 
         [0009]    Thus, there exists a need to provide a mechanism to allow a user at a peer device to effectively provide user preferences and profile information used to generate media item recommendations as well as a system and method to allow a user to more effectively navigate among media item recommendations among a vast media collection. 
       SUMMARY 
       [0010]    The present invention provides a system and a user device for participating in a network for sharing media consumption activity. In an embodiment, the device comprises a processor and a control system component associated with the processor. The control system is adapted to receive information identifying a media item and a friend of a plurality of friends, wherein the information is provided as a result of playing the media item on a second user device associated with the friend of the plurality of friends. The control system is further adapted to present information comprising information identifying the media item selected for play on the second user device and the friend associated with the second user device and information identifying a plurality of other media items and a subset of the plurality of friends, wherein play of the other media items was initiated on a corresponding plurality of other user devices associated with the subset of the plurality of friends. The control system is further adapted to; obtain a selection of a second media item for play; obtain the second media item; and initiate play of the second media item. The control system is further adapted to provide, as a result of playing the second media item, information identifying the second media item and the user for sharing with the plurality of friends of the user. 
         [0011]    In an embodiment, a user device comprises a processor and a control system component associated with the processor. The control system is adapted to receive information identifying a media item and a friend of a plurality of friends, wherein the information is provided as a result of playing the media item on a second user device associated with the friend of the plurality of friends. The control system is further adapted to present information comprising information identifying the media item selected for play on the second user device and the friend associated with the second user device and information identifying a plurality of other media items and a subset of the plurality of friends, wherein play of the other media items was initiated on a corresponding plurality of other user devices associated with the subset of the plurality of friends. The control system is further adapted to: obtain the media item from a remote source and initiate play of the media item. The control system is further adapted to provide, as a result of playing the media item, information identifying the media item and the user for sharing with the plurality of friends of the user. 
         [0012]    In an embodiment, a user device comprises a processor and a control system component associated with the processor. The control system is adapted to receive information identifying a media item and a friend of a plurality of friends, wherein the information is provided in response to playing the media item on a second user device associated with the friend of the plurality of friends. The control system is further adapted to present information identifying the media item selected for play on the second user device and the friend associated with the second user device. The control system is further adapted to display information identifying a plurality of other media items. The control system is further adapted to display information identifying a source of each of the plurality of other media items. The control system is further adapted to initiate play of the media item. The control system is further adapted to provide, in response to playing the media item, information identifying the media item and the user for sharing with the plurality of friends of the user. 
         [0013]    In an embodiment, a user device comprises a processor and a control system component associated with the processor. The control system is adapted to receive information identifying a media item and a friend of a plurality of friends, wherein the information is provided as a result of playing the media item on a second user device associated with the friend of the plurality of friends. The control system is further adapted to automatically add the media item to a pre-existing list of media items, to provide an updated list of media items. The control system is further adapted to present information derived from the updated list of media items comprising information identifying the media item selected for play on the second user device and the friend associated with the second user device and information identifying a plurality of other media items and a subset of the plurality of friends, wherein play of the other media items was initiated on a corresponding plurality of other user devices associated with the subset of the plurality of friends. The pre-existing list of media items is a list of media items existing prior to receiving the information identifying a media item and a friend of the plurality of friends, wherein the information is provided as a result of playing the media item on a second user device associated with the friend of the plurality of friends. 
         [0014]    In an embodiment, a user device comprises a processor and a control system component associated with the processor. The control system is further adapted to receive information identifying a media item, a friend of a plurality of friends, and a timestamp, wherein the information is provided as a result of the media item having been played at a time of play indicated by the timestamp on a second user device associated with the friend of the plurality of friends. The control system is further adapted to present information comprising information identifying the media item selected for play on the second user device, the friend associated with the second user device, and information indicating the time of play of the media item and information identifying a plurality of other media items and a subset of the plurality of friends wherein play of the other media items was initiated on a corresponding plurality of other user devices associated with the subset of the plurality of friends, and wherein at least one of the plurality of other media items is displayed with information indicating an other time of play derived from a timestamp of the at least one of the plurality of other media items at a corresponding other user device. 
         [0015]    In an embodiment a system for distributing music comprises a user device and a server. The user device is adapted to. obtain information identifying a user associated with the user device; obtain information identifying a plurality of friends of the user based on the information identifying the user; receive information identifying a media item and a friend of the plurality of friends, wherein the information is provided in response to playing the media item on a second user device associated with the friend of the plurality of friends; present information comprising information identifying the media item selected for play on the second user device and the friend associated with the second user device and information identifying a plurality of other media items and a subset of the plurality of friends, wherein play of the other media items was initiated on a corresponding plurality of other user devices associated with the subset of the plurality of friends; obtain a selection of a second media item for play; request the second media item from a server; and provide in response to playing the second media item, information identifying the second media item and the user for sharing with the plurality of friends of the user. The server is adapted to: provide a subscription based music service to the user device and the second user device; facilitate communication between the user device and the second user device; and provide the second media item to the user device in response to receiving the request. 
         [0016]    In an embodiment, a user device comprises a processor and a control system component associated with the processor. The control system is adapted to receive information identifying: a plurality of media items; times at which the plurality of media items were played at user devices of friends of a plurality of friends; and users of a plurality of user devices at which the plurality of media items were played. The control system is further adapted to for ones of the plurality of media items, display by the user device, information identifying: the media item; the time at which the media item was played at the user devices of the friends of the plurality of friends; the user of the plurality of user devices at which the media item was played; and a source of the media item. 
         [0017]    Those skilled in the art will appreciate the scope of the present invention and realize additional aspects thereof after reading the following detailed description of the preferred embodiments in association with the accompanying drawing figures. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
         [0018]    The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the invention, and together with the description serve to explain the principles of the invention. 
           [0019]      FIG. 1  illustrates a system incorporating a peer-to-peer (P2P) network for real time media recommendations according to one embodiment of the present invention; 
           [0020]      FIG. 2  is a flow chart illustrating the operation of the peer devices of  FIG. 1  according to one embodiment of the present invention; 
           [0021]      FIG. 3  illustrates the operation of the system of  FIG. 1  according to one embodiment of the present invention; 
           [0022]      FIG. 4  illustrates a system incorporating a P2P network for real time media recommendations according to a second embodiment of the present invention; 
           [0023]      FIG. 5  illustrates the operation of the system of  FIG. 4  according to one embodiment of the present invention; 
           [0024]      FIG. 6  is a flow chart illustrating a method for automatically selecting media to play based on recommendations from peer devices and user preferences according to one embodiment of the present invention; 
           [0025]      FIG. 7  illustrates an exemplary graphical user interface (GUI) for configuring user preferences according to one embodiment of the present invention; 
           [0026]      FIG. 8  illustrates an exemplary GUI for assigning weights to various categories of media content as part of configuring the user preferences according to one embodiment of the present invention; 
           [0027]      FIG. 9  illustrates an exemplary GUI for assigning weights to individual users within a user category as part of configuring the user preferences according to one embodiment of the present invention; 
           [0028]      FIG. 10  illustrates an exemplary GUI for assigning weights to individual genres from a genre category as part of configuring the user preferences according to one embodiment of the present invention; 
           [0029]      FIG. 11  illustrates an exemplary GUI for assigning weights to individual decades from a decade category as part of configuring the user preferences according to one embodiment of the present invention; 
           [0030]      FIG. 12  illustrates an exemplary GUI for assigning weights to individual availability types from an availability type category as part of configuring the user preferences according to one embodiment of the present invention; 
           [0031]      FIG. 13  illustrates an exemplary GUI displaying a playlist including both songs from a local music collection of a peer device and recommended songs from other peer devices, where the songs are sorted by a score determined based on user preferences according to one embodiment of the present invention; 
           [0032]      FIG. 14  illustrates an exemplary GUI displaying a playlist including both songs from a local music collection of a peer device and recommended songs from other peer devices, where the songs are sorted by a both genre and score according to one embodiment of the present invention; 
           [0033]      FIG. 15  illustrates an exemplary GUI for selecting the maximum number of media items in a recommendation queue according to one embodiment of the present invention; 
           [0034]      FIG. 16  illustrates an exemplary GUI for providing user-defined thresholds for a media item download queue according to one embodiment of the present invention; 
           [0035]      FIG. 17  is a block diagram of a peer device of  FIG. 1  according to one embodiment of the present invention; and 
           [0036]      FIG. 18  is a block diagram of a peer device of  FIG. 4  according to one embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0037]    The embodiments set forth below represent the necessary information to enable those skilled in the art to practice the invention and illustrate the best mode of practicing the invention. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the invention and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims. 
         [0038]    In an embodiment, the present invention provides graphical user interfaces (GUIs) and related systems for a peer device to provide user preferences and profile information used to filter media item recommendations. The GUIs also allow a user to navigate through and sort his or her media item collection containing such media item recommendations based on a preference scoring system generated as a result of the user preferences and profile selections made by the user. In this manner, the peer device may be contained within a peer-to-peer (P2P) network. A client application executing on the peer device provides and enables the GUIs. One or more GUIs enable the user to provide information to weight various media item categories and attributes within the media item categories. The user provided weighting information is used to configure the user preferences. 
         [0039]    Another of the GUIs may display a media item playlist containing a preference scoring column to allow the user to display and sort media recommendations on the GUI by preference score. The media item playlist GUI also displays a list of the users subscribing to the P2P network, the title of and information concerning media items recommended by the users, and media items stored locally on the peer device, and other related information. The score may be determined by applying preferences defined by information provided by the user of the peer device via the one or more GUIs provided by the embodiment of the present invention. 
         [0040]    Before discussing the particular GUI systems provided as part of the embodiment of the present invention to enable a user to define preferences, a user profile, and display and sort media items by preference scoring, a discussion of a P2P system and network that allows the user to obtain media item recommendations is first discussed. Examples of the GUIs  42 ,  50 ,  92 ,  100 ,  118 ,  132 ,  142 ,  184 , and  187  are illustrated in  FIGS. 7 ,  8 ,  9 ,  10 ,  11 ,  12 ,  13 ,  14 ,  15 , and  16 , respectively, and described in more detail later in this application. 
         [0041]      FIG. 1  illustrates a system  10  incorporating a P2P network for providing real time song recommendations according to one embodiment of the present invention. Note that while the discussion herein focuses on song recommendations for clarity and ease of discussion, the present invention is equally applicable to providing recommendations for other types of media presentations such as video presentations, as will be apparent to one of ordinary skill in the art upon reading this disclosure. Exemplary video presentations are movies, television programs, and the like. In general, the system  10  includes a number of peer devices  12 - 16  which are optionally connected to a subscription music service  18  via a network  20 , which may be a distributed public network such as, but not limited to, the Internet. Note that while three peer devices  12 - 16  are illustrated, the present invention may be used with any number of two or more peer devices. 
         [0042]    In this embodiment, the peer devices  12 - 16  are preferably portable devices such as, but not limited to, portable audio players, mobile telephones, Personal Digital Assistants (PDAs), or the like having audio playback capabilities. However, the peer devices  12 - 16  may alternatively be stationary devices such as a personal computer or the like. The peer devices  12 - 16  include local wireless communication interfaces ( FIG. 15 ) communicatively coupling the peer devices  12 - 16  to form a peer-to-peer (P2P) network. The wireless communication interfaces may provide wireless communication according to, for example, one of the suite of IEEE 802.11 standards, the Bluetooth standard, or the like. 
         [0043]    The peer device  12  includes a music player  22 , a recommendation engine  24 , and a music collection  26 . The music player  22  may be implemented in software, hardware, or a combination of hardware and software. In general, the music player  22  operates to play songs from the music collection  26 . The recommendation engine  24  may be implemented in software, hardware, or a combination of hardware and software. The recommendation engine  24  may alternatively be incorporated into the music player  22 . The music collection  26  includes any number of song files stored in one or more digital storage units such as, for example, one or more hard-disc drives, one or more memory cards, internal Random-Access Memory (RAM), one or more associated external digital storage devices, or the like. 
         [0044]    In operation, each time a song is played by the music player  22 , the recommendation engine  24  operates to provide a recommendation identifying the song to the other peer devices  14 ,  16  via the P2P network. The recommendation does not include the song. In one embodiment, the recommendation may be a recommendation file including information identifying the song. In addition, as discussed below in detail, the recommendation engine  24  operates to programmatically, or automatically, select a next song to be played by the music player  22  based on the recommendations received from the other peer device  14 ,  16  identifying songs recently played by the other peer devices  14 ,  16  and user preferences associated with the user of the peer device  12 . 
         [0045]    Like the peer device  12 , the peer device  14  includes a music player  28 , a recommendation engine  30 , and a music collection  32 , and the peer device  16  includes a music player  34 , a recommendation engine  36 , and a music collection  38 . 
         [0046]    The subscription music service  18  may be a service hosted by a server connected to the network  20 . Exemplary subscription based music services that may be modified to operate according to the present invention are Yahoo! Music Unlimited digital music service and RealNetwork&#39;s Rhapsody digital music service. 
         [0047]      FIG. 2  illustrates the operation of the peer device  12  according to one embodiment of the present invention. However, the following discussion is equally applicable to the other peer devices  14 ,  16 . First, the peer devices  12 - 16  cooperate to establish a P2P network (step  200 ). The P2P network may be initiated using, for example, an electronic or verbal invitation. Invitations may be desirable when the user wishes to establish the P2P network with a particular group of other users, such as his or her friends. Note that this may be beneficial when the user desires that the music he or she listens to be influenced only by the songs listened to by, for example, the user&#39;s friends. Invitations may also be desirable when the number of peer devices within a local wireless coverage area of the peer device  12  is large. As another example, the peer device  12  may maintain a “buddy list” identifying friends of the user of the peer device  12 , where the peer device  12  may automatically establish a P2P network with the peer devices of the users identified by the “buddy list” when the peer devices are within a local wireless coverage area of the peer device  12 . 
         [0048]    Alternatively, the peer device  12  may establish an ad-hoc P2P network with the other peer devices  14 ,  16  by detecting the other peer devices  14 ,  16  within the local wireless coverage area of the peer device  12  and automatically establishing the P2P network with at least a subset of the detected peer devices  14 ,  16 . In order to control the number of peer devices within the ad-hoc P2P network, the peer device  12  may compare user profiles of the users of the other peer devices  14 ,  16  with a user profile of the user of the peer device  12  and determine whether to permit the other peer devices  14 ,  16  to enter the P2P network based on the similarities of the user profiles. 
         [0049]    At some point after the P2P network is established, the peer device  12  plays a song (step  202 ). Initially, before any recommendations have been received from the other peer devices  14 ,  16 , the song may be a song from the music collection  26  selected by the user of the peer device  12 . Prior to, during, or after playback of the song, the recommendation engine  24  sends a recommendation identifying the song to the other peer devices  14 ,  16  (step  204 ). The recommendation may include, but is not limited to, information identifying the song such as a Globally Unique Identifier (GUID) for the song, title of the song, or the like; a Uniform Resource Locator (URL) enabling other peer devices to obtain the song such as a URL enabling download or streaming of the song from the subscription music service  18  or a URL enabling purchase and download of the song from an e-commerce service; a URL enabling download or streaming of a preview of the song from the subscription music service  18  or a similar e-commerce service; metadata describing the song such as ID3 tags including, for example, genre, the title of the song, the artist of the song, the album on which the song can be found, the date of release of the song or album, the lyrics, and the like. 
         [0050]    The recommendation may also include a list of recommenders including information identifying each user having previously recommended the song and a timestamp for each recommendation. For example, if the song was originally played at the peer device  14  and then played at the peer device  16  in response to a recommendation from the peer device  14 , the list of recommenders may include information identifying the user of the peer device  14  or the peer device  14  and a timestamp identifying a time at which the song was played or recommended by the peer device  14 , and information identifying the user of the peer device  16  or the peer device  16  and a timestamp identifying a time at which the song was played or recommended by the peer device  16 . Likewise, if the peer device  12  then selects the song for playback, information identifying the user of the peer device  12  or the peer device  12  and a corresponding timestamp may be appended to the list of recommenders. 
         [0051]    The peer device  12 , and more specifically the recommendation engine  24 , also receives recommendations from the other peer devices  14 ,  16  (step  206 ). The recommendations from the other peer devices  14 ,  16  identify songs played by the other peer devices  14 ,  16 . Optionally, the recommendation engine  24  may filter the recommendations from the other peer devices  14 ,  16  based on, for example, user, genre, artist, title, album, lyrics, date of release, or the like (step  208 ). 
         [0052]    The recommendation engine  24  then automatically selects a next song to play from the songs identified by the recommendations received from the other peer devices  14 ,  16 , optionally songs identified by previously received recommendations, and one or more songs from the music collection  26  based on user preferences (step  210 ). In one embodiment, the recommendation engine  24  considers only those songs identified by recommendations received since a previous song selection. For example, if the song played in step  202  was a song selected by the recommendation engine  24  based on prior recommendations from the peer devices  14 ,  16 , the recommendation engine  24  may only consider the songs identified in new recommendations received after the song was selected for playback in step  202  and may not consider the songs identified in the prior recommendations. This may be beneficial if the complexity of the recommendation engine  24  is desired to be minimal such as when the peer device  12  is a mobile terminal or the like having limited processing and memory capabilities. In another embodiment, the recommendation engine  24  may consider all previously received recommendations, where the recommendations may expire after a predetermined or user defined period of time. 
         [0053]    As discussed below, the user preferences used to select the next song to play may include a weight or priority assigned to each of a number of categories such as user, genre, decade of release, and availability. Generally, availability identifies whether songs are stored locally in the music collection  26 ; available via the subscription music service  18 ; available for download, and optionally purchase, from an e-commerce service or one of the other peer devices  14 ,  16 ; or are not currently available where the user may search for the songs if desired. The user preferences may be stored locally at the peer device  12  or obtained from a central server via the network  20 . If the peer device  12  is a portable device, the user preferences may be configured on an associated user system, such as a personal computer, and transferred to the peer device  12  during a synchronization process. The user preferences may alternatively be automatically provided or suggested by the recommendation engine  24  based on a play history of the peer device  12 . In the preferred embodiment discussed below, the songs identified by the recommendations from the other peer devices  14 ,  16  and the songs from the music collection  26  are scored or ranked based on the user preferences. Then, based on the scores, the recommendation engine  24  selects the next song to play. 
         [0054]    Once the next song to play is selected, the peer device  12  obtains the selected song (step  212 ). If the selected song is part of the music collection  26 , the peer device  12  obtains the selected song from the music collection  26 . If the selected song is not part of the music collection  26 , the recommendation engine  24  obtains the selected song from the subscription music service  18 , an e-commerce service, or one of the other peer devices  14 ,  16 . For example, the recommendation for the song may include a URL providing a link to a source from which the song may be obtained, and the peer device  12  may obtain the selected song from the source identified in the recommendation for the song. Once obtained, the selected song is played and the process repeats (steps  202 - 212 ). 
         [0055]      FIG. 3  illustrates the operation of the peer devices  12 - 16  to provide real time song recommendations according to one embodiment of the present invention. The illustrated process is the same as discussed above with respect to  FIG. 2 . As such, the details will not be repeated. In general, the peer devices  14 ,  16  play songs and, in response, provide song recommendations to the peer device  12  (steps  300 - 306 ). The peer device  12  may optionally filter the recommendations from the peer devices  14 ,  16  (step  308 ). The recommendation engine  24  of the peer device  12  then automatically selects the next song to play from the songs identified by the recommendations, optionally songs identified by prior recommendations from the peer devices  14 ,  16 , and locally stored songs from the music collection  26  based on user preferences of the user of the peer device  12  (step  310 ). The peer device  12  then obtains and plays the selected song (steps  312 - 314 ). Either prior to, during, or after playback of the selected song, the recommendation engine  24  of the peer device  12  provides a recommendation identifying the selected song to the other peer devices  14 ,  16  (step  316 - 318 ). 
         [0056]      FIG. 4  illustrates the system  10 ′ according to second embodiment of the present invention. In this embodiment, the peer devices  12 ′- 16 ′ form a P2P network via the network  20  and a proxy server  40 . The peer devices  12 ′- 16 ′ may be any device having a connection to the network  20  and audio playback capabilities. For example, the peer devices  12 ′- 16 ′ may be personal computers, laptop computers, mobile telephones, portable audio players, PDAs, or the like having either a wired or wireless connection to the network  20 . As discussed above with respect to the peer device  12 , the peer device  12 ′ includes music player  22 ′, a recommendation engine  24 ′, and a music collection  26 ′. Likewise, the peer device  14 ′ includes a music player  28 ′, a recommendation engine  30 ′, and a music collection  32 ′, and the peer device  16 ′ includes a music player  34 ′, a recommendation engine  36 ′, and a music collection  38 . 
         [0057]      FIG. 5  illustrates the operation of the system  10 ′ of  FIG. 4 . Prior to beginning the process, the peer devices  12 ′- 16 ′ form a P2P network. Since the number of peer devices  12 ′- 16 ′ that may be connected to the network  20  may be very large, the peer devices  12 ′- 16 ′ may implement some technique for identifying a desired group of peer devices for the P2P network. For example, the P2P network may be initiated using, for example, an electronic or verbal invitation. As another example, the peer device  12 ′ may maintain a “buddy list” identifying friends of the user of the peer device  12 ′, where the peer device  12 ′ may automatically establish a P2P network with the peer devices of the users identified by the “buddy list” when the peer devices are connected to the network  20 . Alternatively, the peer devices  12 ′- 16 ′ may form an ad-hoc network where the participants for the ad-hoc network are selected based on similarities in user profiles. 
         [0058]    In this example, once the P2P network is established, the peer device  14 ′ plays a song and, in response, provides a song recommendation identifying the song to the peer device  12 ′ via the proxy server  40  (steps  400 - 404 ). While not illustrated for clarity, the peer device  14 ′ also sends the recommendation for the song to the peer device  16 ′ via the proxy server  40 . The peer device  16 ′ also plays a song and sends a song recommendation to the peer device  12 ′ via the proxy server  40  (steps  406 - 410 ). Again, while not illustrated for clarity, the peer device  16 ′ also sends the recommendation for the song to the peer device  14 ′ via the proxy server  40 . 
         [0059]    From this point, the process continues as discussed above. More specifically, the recommendation engine  24 ′ may optionally filter the recommendations from the other peer devices  14 ′,  16 ′ based on, for example, user, genre, artist, title, album, lyrics, date of release, or the like (step  412 ). The recommendation engine  24 ′ then automatically selects a next song to play from the songs identified by the recommendations received from the other peer devices  14 ′- 16 ′, optionally songs identified by previously received recommendations from the peer devices  14 ′- 16 ′, and one or more songs from the music collection  26 ′ based on user preferences (step  414 ). In the preferred embodiment discussed below, the songs identified by the recommendations from the other peer devices  14 ′- 16 ′ and the songs from the music collection  26 ′ are scored based on the user preferences. Then, based on the scores, the recommendation engine  24 ′ selects the next song to play. 
         [0060]    Once the next song to play is selected, the peer device  12 ′ obtains the selected song (step  416 ). If the selected song is part of the music collection  26 ′, the peer device  12 ′ obtains the selected song from the music collection  26 ′. If the selected song is not part of the music collection  26 ′, the recommendation engine  24 ′ obtains the selected song from the subscription music service  18 , an e-commerce service, or one of the other peer devices  14 ′- 16 ′. For example, the selected song may be obtained from a source identified in the recommendation for the song. Once obtained, the selected song is played and a recommendation for the song is provided to the other peer devices  14 ′- 16 ′ via the proxy server  40  (steps  418 - 426 ). 
         [0061]      FIG. 6  illustrates the process of automatically selecting a song to play from the received recommendations and locally stored songs at the peer device  12 ′ according to one embodiment of the present invention. However, the following discussion is equally applicable to the peer devices  12 - 16  of  FIG. 1 , as well as the other peer devices  14 ′- 16 ′ of  FIG. 4 . First, the user preferences for the user of the peer device  12 ′ are obtained (step  500 ). The user preferences may include a weight or priority assigned to each of a number of categories such as, but not limited to, user, genre, decade of release, and availability. The user preferences may be obtained from the user during an initial configuration of the recommendation engine  24 ′. In addition, the user preferences may be updated by the user as desired. The user preferences may alternatively be suggested by the recommendation engine  24 ′ or the proxy server  40  based on a play history of the peer device  12 ′. Note that that proxy server  40  may ascertain the play history of the peer device  12 ′ by monitoring the recommendations from the peer device  12 ′ as the recommendations pass through the proxy server  40  on their way to the other peer devices  14 ′- 16 ′. The user preferences may be stored locally at the peer device  12 ′ or obtained from a central server, such as the proxy server  40 , via the network  20 . 
         [0062]    Once recommendations are received from the other peer devices  14 ′- 16 ′, the recommendation engine  24 ′ of the peer device  12 ′ scores the songs identified by the recommendations based on the user preferences (step  502 ). The recommendation engine  24 ′ also scores one or more local songs from the music collection  26 ′ (step  504 ). The recommendation engine  24 ′ then selects the next song to play based, at least on part, on the scores of the recommended and local songs (step  506 ). 
         [0063]      FIG. 7  illustrates an exemplary graphical user interface (GUI)  42  for configuring user preferences using a plurality of selectors. First, the user assigns a weight to various categories. In this example, the categories are users, genre, decade, and availability. However, the present invention is not limited thereto. The weights for the categories may be assigned alphabetically by selecting radio button  44 , customized by the user by selecting radio button  46 , or automatically suggested based on a user profile of the user by selecting radio button  48 . If alphabetical weighting is selected, the weights are assigned by alphabetically sorting the categories and assigning a weight to each of the categories based on its position in the alphabetically sorted list of categories. As illustrated in  FIG. 8 , if customized weighting is selected, the user may be presented with a GUI  50  for customizing the weighting of the categories. As illustrated in the exemplary embodiment of  FIG. 8 , the weights of the categories may be assigned by adjusting corresponding sliding bars  52 - 58 . Sliding bar  60  may be adjusted to assign a weight to a “no repeat factor.” The no repeat factor is a dampening factor used to alter a song&#39;s score based on when the song was previously played at the peer device  12 ′ in order to prevent the same song from being continually repeated. 
         [0064]    Once the weights are assigned, the user may select an OK button  62  to return to the GUI  42  of  FIG. 7  or select a REVERT button  64  to return the weights of the categories to their previous settings. In addition, the user may select a SUGGEST FROM PROFILE button  66  to have the recommendation engine  24 ′ or the proxy server  40  suggest weights for the categories based on a user profile. Note that the button  66  has the same effect as the radio button  48  of  FIG. 7 . 
         [0065]    Returning to  FIG. 7 , radio buttons  68 - 72  are used to select a desired method for assigning weights to each user in the P2P network, radio buttons  74 - 78  are used to select a desired method for assigning weights to each of a number of genres of music, radio buttons  80 - 84  are used to select the desired method for assigning weights to each of a number of decades, and radio buttons  86 - 90  are used to select the desired method for assigning weights to a number of song availability types. 
         [0066]    Regarding users, if the radio button  68  is selected, the users are assigned weights based on their respective positions in an alphabetically sorted list of users. If the radio button  70  is selected, a GUI  92  ( FIG. 9 ) enabling the user to customize the weights assigned to a number of users from which recommendations are received. An exemplary embodiment of the GUI  92  is illustrated in  FIG. 9 , where sliding bars  94 - 98  enable the user to assign customized weights to corresponding users. Returning to  FIG. 7 , if the radio button  72  is selected, the recommendation engine  24 ′ or the proxy server  40  generates suggested weights for the users based on a user profile associated with the peer device  12 ′. 
         [0067]    Regarding genres, if the radio button  74  is selected, the genres are assigned weights based on their respective positions in an alphabetically sorted list of genres. If the radio button  76  is selected, a GUI  100  ( FIG. 10 ) enabling the user to customize the weights assigned to a number of genres. An exemplary embodiment of the GUI  100  is illustrated in  FIG. 10 , where sliding bars  102 - 116  enable the user to assign customized weights to corresponding genres. Returning to  FIG. 7 , if the radio button  78  is selected, the recommendation engine  24 ′ or the proxy server  40  generates suggested weights for the genres based on a user profile associated with the peer device  12 ′. 
         [0068]    Regarding decades, if the radio button  80  is selected, the decades are assigned weights based on their respective positions in a chronologically sorted list of decades. If the radio button  82  is selected, a GUI  118  ( FIG. 11 ) enabling the user to customize the weights assigned to a number of decades. An exemplary embodiment of the GUI  118  is illustrated in  FIG. 11 , where sliding bars  120 - 130  enable the user to assign customized weights to corresponding decades. Returning to  FIG. 7 , if the radio button  84  is selected, the recommendation engine  24 ′ or the proxy server  40  generates suggested weights for the decades based on a user profile associated with the peer device  12 ′. 
         [0069]    Regarding availability, if the radio button  86  is selected, the availability types are assigned weights based on their respective positions in an alphabetically sorted list of availability types. If the radio button  88  is selected, a GUI  132  ( FIG. 12 ) enabling the user to customize the weights assigned to a number of availability types. An exemplary embodiment of the GUI  132  is illustrated in  FIG. 12 , where sliding bars  134 - 140  enable the user to assign customized weights to corresponding availability types. Returning to  FIG. 7 , if the radio button  90  is selected, the recommendation engine  24 ′ or the proxy server  40  generates suggested weights for the availability types based on a user profile associated with the peer device  12 ′. 
         [0070]    An exemplary equation for scoring a particular song is: 
         [0000]      Score=NRF·( WU·WUA+WG·WGA+WD·WDA+WA·WAA )·100,
 
         [0000]    where NRF is the “no repeat factor”; WU is the weight assigned to the user category; WUA is the weight assigned to the user attribute of the song, which is the user recommending the song; WG is the weight assigned to the genre category; WGA is the weight assigned to the genre attribute of the song, which is the genre of the song; WD is the weight assigned to the decade category; WDA is the weight assigned to the decade attribute of the song, which is the decade in which the song or the album associated with the song was released; WA is the weight assigned to the availability category; and WAA is the weight assigned to the availability attribute of the song, which is the availability of the song. 
         [0071]    The NRF may, for example, be computed as: 
         [0000]    
       
         
           
             NRF 
             = 
             
               
                 MIN 
                  
                 
                   ( 
                   
                     
                       10 
                       · 
                       NRFW 
                     
                     , 
                     LASTREPEAT_INDEX 
                   
                   ) 
                 
               
               
                 10 
                 · 
                 NRFW 
               
             
           
         
       
     
         [0072]    As an example, assume that the following category weights have been assigned: 
         [0000]                                                User Category   1           Genre Category   7           Decade Category   7           Availability Type Category   5           NRFW   9                        
Further assume that the attributes for the categories have been assigned weights as follows:
 
         [0000]                                                                          User   Genre   Decade   Availability                                User A   5   Alternative   8   1950s   2   Local   8       User B   5   Classic Rock   5   1960s   4   Subscription Network   2       User C   5   Arena Rock   5   1970s   7   Buy/Download   1               Jazz   5   1980s   9   Find   1               New Wave   2   1990s   5               Punk   4   2000s   5               Dance   2               Country   2                    
Thus, if a particular song to be scored is recommended by the user “User C,” is from the “Alternative Genre,” is from the “1980s” decade, and is available from the subscription music service  18 , the score of the song may be computed as:
 
         [0000]    
       
         
           
             Score 
             = 
             
               NRF 
               · 
               
                 ( 
                 
                   
                     
                       1 
                       20 
                     
                     · 
                     
                       5 
                       10 
                     
                   
                   + 
                   
                     
                       7 
                       20 
                     
                     · 
                     
                       8 
                       10 
                     
                   
                   + 
                   
                     
                       7 
                       20 
                     
                     · 
                     
                       9 
                       10 
                     
                   
                   + 
                   
                     
                       5 
                       20 
                     
                     · 
                     
                       2 
                       10 
                     
                   
                 
                 ) 
               
               · 
               100 
             
           
         
       
     
         [0000]    where if the song was last played 88 songs ago, 
         [0000]    
       
         
           
             NRF 
             = 
             
               
                 
                   MIN 
                    
                   
                     ( 
                     
                       
                         10 
                         · 
                         9 
                       
                       , 
                       88 
                     
                     ) 
                   
                 
                 
                   10 
                   · 
                   9 
                 
               
               = 
               
                 
                   88 
                   90 
                 
                 . 
               
             
           
         
       
     
         [0000]    Thus, the score for the song is 
         [0000]    
       
         
           
             Score 
             = 
             
               
                 
                   88 
                   90 
                 
                 · 
                 
                   ( 
                   
                     
                       
                         1 
                         20 
                       
                       · 
                       
                         5 
                         10 
                       
                     
                     + 
                     
                       
                         7 
                         20 
                       
                       · 
                       
                         8 
                         10 
                       
                     
                     + 
                     
                       
                         7 
                         20 
                       
                       · 
                       
                         9 
                         10 
                       
                     
                     + 
                     
                       
                         5 
                         20 
                       
                       · 
                       
                         2 
                         10 
                       
                     
                   
                   ) 
                 
                 · 
                 100 
               
               = 
               
                 65.5 
                 . 
               
             
           
         
       
     
         [0073]    The present invention provides GUIs to allow the user to navigate through and sort his or her media item collection containing the media item recommendations based on a preference scoring system described above. 
         [0074]      FIG. 13  is an exemplary GUI  142  showing a playlist for the peer device  12 ′ including both local and recommended songs according to the present invention. However, note that a similar list may be maintained internally by the peer device  12  of  FIG. 1  and potentially optimized to display at least a portion of the GUI  142  on the display of the peer device  12 . In this example, both the local and recommended songs are scored, as described above, and sorted according to their scores. In addition, as illustrated in  FIG. 14 , the songs may be sorted based on another criterion, which in the illustrated example is genre, and score. 
         [0075]    The GUI  142  may optionally allow the user to block songs having particular identified fields. In the examples of  FIGS. 13 and 14 , the user has identified the genre “country” and the artist “iron maiden” as fields to be blocked, as illustrated by the underlining. The user may select fields to block by, for example, clicking on or otherwise selecting the desired fields. Songs having the blocked fields are still scored but are not obtained or played by the peer device  12 ′. 
         [0076]    Referring to  FIGS. 13 and 14 , in one embodiment, the GUI  142  may display the identity of the user  163  of the peer device  12 ′ in the upper right corner. The playlist on the GUI  142  may be displayed utilizing one column or a plurality of columns of information concerning the media items on the playlist.  FIGS. 13 and 14  show eight columns  164 - 178 , each column having a descriptive heading for the information displayed in the column. The GUI  142  includes a user column  164 , a song column  166 , an artist column  168 , a genre column  170 , a decade column  172 , a time column  174 , an availability column  176 , and a score column  178 . The information in the columns may be organized in rows with the information in each row aligned to the related media items displayed in the song column  166 . 
         [0077]    The user column  164  displays a list of users who have subscribed to the client application. The users displayed in the user column  164  may be the user and/or peers or friends of the user. Individual users may appear more than once in the user column  164  based on the number of recommendations from the user, with respect to the peers or friends of the user, and/or based on the number of media items stored locally in peer device  12 ′, with respect to the user. The song column  166  is a list of media item titles for the media items recommended by the users and the media items stored locally in peer device  12 ′. Although the song column  166  in  FIGS. 13 and 14  lists songs, it should be understood that any media items, such as movies and television shows, may be listed, and the present invention should not be limited to just songs. Optionally, the song column  166  may also be referred to and have a descriptive heading of “Title” column. Also, the media items that are stored locally in peer device  12 ′ are interleaved with the media items that are recommendations from a friend or peer of the user. 
         [0078]    The artist column  168  displays a list of the names of the artists associated with the particular media item. The genre column  170  displays a list of the genre categories in which the media items may be defined. The decade column  172  displays a list of the beginning year of the decade in which the media item was released. Optionally, the decade column  172  may be a “Year” column with the actual year of the release of the media item displayed. The time column  174  displays the time since that particular media item was played by the associated user in the user column  164 . The availability column  176  comprises information regarding the location of the media items, as discussed above. The score column  178  displays the score for the associated media item, which may be determined using the user preference information as discussed in detail above. 
         [0079]    The media items displayed in the song column  166  are sorted in an order depending on the media item&#39;s score, which is displayed in the score column  178  from the highest score to the lowest score.  FIG. 13  shows “Sweet Emotion” positioned first in the score column  178  with the highest score of “95” and “Something More” positioned last in score column  178  with the lowest score of “25.” Optionally, the user may elect to have the scores displayed in the score column  178  in reverse order, having the lowest score displayed at the top and the highest score displayed at the bottom. The user may elect to have the scores displayed in reverse order by clicking on the descriptive heading of the score column  178 . If a new recommendation is received by the peer device  12 ′ and/or a new media item is stored locally in the peer device  12 ′, the playlist is re-sorted, if necessary, to maintain the order based on the score. 
         [0080]    The user may sort the playlist based on several different criteria. The sorting criteria may include, for example, user, title, artist, genre, year of release, and availability. The user may sort the playlist according to the criterion by selecting the column associated with the criterion. The user may select the column for sorting by clicking on the descriptive header for that column. For example, if the user elects to sort the playlist by title, the user may click on the descriptive heading for the song column  166 , and the media items displayed on the playlist are displayed in an order based on an alphabetical listing of the titles of the media items. If the user selects the artist column  168 , the media items are displayed in an order based on an alphabetical listing of the artists. If an artist has more than one media item on the playlist, the media items for that artist are displayed according to the media item score in descending order. 
         [0081]    If the user selects another column, for example, the genre column  170 , the media items in the playlist are sorted according to genre in a descending order with the media items defined as being in the user&#39;s most preferred genre category positioned at the top of the displayed playlist, and the media items defined as being in the user&#39;s least preferred genre category at the bottom of the displayed playlist. If there is more than one media item within a genre, the media items within the genre are sorted by the media item&#39;s score in descending order. 
         [0082]    An example of the playlist sorted by the genre criterion is shown in  FIG. 14 . The media items are shown sorted according to genre categories according to score. An alternative genre  180  is shown as the most preferred and a country genre  182  is shown as the least preferred with media items positioned in an order within each particular genre category based on the score. For example, in the alternative genre  180  category, four media items are listed in descending order according to their score. “Dance In My Sleep” with a score of “92” is displayed first in the alternative genre  180  category and “Alison” with a score of “65” is displayed last in the ‘alternative’ genre  180  category, which is the fourth position on the playlist. The playlist may be similarly sorted based on any of the columns  164 - 176  by clicking on the descriptive header for that column. By selecting the score column  178  again, the genre sorting criterion is removed and the playlist is sorted according to score as shown on  FIG. 13 . 
         [0083]    The media items may be displayed on the playlist in the order in which the media items are played. A play order of the media items depends on and follows the order in which the media items are positioned on the playlist. For example, in  FIG. 13 , “Sweet Emotion” is positioned first on the playlist and, therefore, is played first with the other songs played in the order they are positioned in the song column. The media item in the last position, “Something More,” is played last. Similarly, in  FIG. 14 , “Dance In My Sleep,” the media item in the first position, is played first and “Something More” is played last. If the user changes the sorting of the playlist, as discussed above, the play order of the media items also changes to follow the manner in which the media items are positioned on the playlist based on the changed sorting. Also, as discussed above, if a new recommendation is received by the peer device  12 ′ and/or a new media item is stored locally in the peer device  12 ′, the playlist is re-sorted, if necessary, to maintain the order based on the score and the user&#39;s sorting criterion, and, therefore, the play order may also change. 
         [0084]    In one embodiment, the user may choose the number of recommendations that are displayed on the playlist. The user may do this by setting the maximum number of recommendations in a recommendation queue.  FIG. 15  illustrates a GUI  184  with a selector  185 . The selector may be a sliding bar  185 . The user may select the maximum number of recommendations in the queue by positioning the “Max Media Items in Recommendation Queue” sliding bar  185 . For purposes of describing the present invention,  FIG. 17  illustrates a range of settings for the “Max Media Items in Recommendation Queue” sliding bar  185  of “50” to “unlimited.” However, it is understood that any number of recommendations may be selected and the present invention should not be limited to the numbers illustrated on  FIG. 15 . 
         [0085]    Optionally, the GUI  184  may include a “Remove Media Items Based On” plurality of selectors  186 . The “Remove Media Items Based On” selector  186  allows the user to select the basis on which recommendations are removed from the playlist.  FIG. 15  illustrates three bases on which to remove recommendations: by “Score,” by “Timestamp,” and by “Current Sort.” If the user selects “Score,” the number of recommendations selected with the highest scores are retained and other recommendations are removed from the playlist. For example, if the user sets the “Max Media Items in Recommendation Queue” sliding bar  185  to “50,” the fifty recommendations with the best score are retained and the rest of the recommendations are removed from the playlist. Similarly, with respect to this example, if the user selects “Timestamp,” the fifty newest received recommendations are retained and the recommendations that have been on the playlist longer are removed. Also, if the user selects “Current Sort,” the fifty recommendations based on the user&#39;s selected sorting criterion are retained and the rest of the recommendations are removed. 
         [0086]    In one embodiment, the recommendation engine  24 ′ of the peer device  12 ′ may provide a download queue containing all songs to be downloaded, and optionally purchased, from an external source such as the subscription music service  18 , an e-commerce service, or another peer device  14 ′- 16 ′. Songs in the download queue having scores above a first predetermined or user defined threshold and previews of other songs in the download queue having scores above a second predetermined or user defined threshold but below the first threshold may be automatically downloaded to the peer device  12 ′. 
         [0087]      FIG. 16  illustrates an exemplary GUI  187  for the user to provide user-defined thresholds for the download queue. The GUI  187  provides a plurality of selectors, which may be sliding bars, for the user to use to establish the thresholds utilizing an “Auto-download Media Item When Score Is Above” sliding bar  188  and “Auto-download Preview When Media Item Is Above” sliding bar  189 . The user selects the threshold value by manipulating one or both of the sliding bars. 
         [0088]    Also included on the GUI  187  may be a “Flush On Rescoring” selector  190 , a “Max Download Sessions” sliding bar  191 , a “Max Download Attempts” sliding bar  192 , a “Retry Interval” sliding bar  193 , and an “Omit Media Items With Block Fields From Download” selector  194 . If the user actuates the “Flush On Rescoring” selector  190 , the songs are deleted from the download queue if the user re-sorts the playlist based on a criterion. The “Max Download Sessions” sliding bar  191  allows the user to select the maximum number of sockets for downloading songs to the peer device  12 ′. The “Max Download Attempts” sliding bar  192  and the “Retry Interval” sliding bar  193  allow the user to control the number of retry attempts to download songs and the time interval between the retry attempts, respectively. The “Omit Media Items With Block Fields From Download” selector  194  allows the user to designate media items on the media item playlist to be omitted from the download queue as discussed in detail above. 
         [0089]      FIG. 17  is a block diagram of an exemplary embodiment of the peer device  12  of  FIG. 1 . However, the following discussion is equally applicable to the other peer devices  14 ,  16 . In general, the peer device  12  includes a control system  144  having associated memory  146 . In this example, the music player  22  and the recommendation engine  24  are at least partially implemented in software and stored in the memory  146 . The peer device  12  also includes a storage unit  148  operating to store the music collection  26  ( FIG. 1 ). The storage unit  148  may be any number of digital storage devices such as, for example, one or more hard-disc drives, one or more memory cards, RAM, one or more external digital storage devices, or the like. The music collection  26  may alternatively be stored in the memory  146 . The peer device  12  also includes a communication interface  150 . The communication interface  150  includes a local wireless communication interface for establishing the P2P network with the other peer devices  14 ,  16 . The local wireless interface may operate according to, for example, one of the suite of IEEE 802.11 standards, the Bluetooth standard, or the like. The communication interface  150  may also include a network interface communicatively coupling the peer device  12  to the network  20  ( FIG. 1 ). The peer device  12  also includes a user interface  152 , which may include components such as a display, speakers, a user input device, and the like. 
         [0090]      FIG. 18  is a block diagram of an exemplary embodiment of the peer device  12 ′ of  FIG. 4 . However, the following discussion is equally applicable to the other peer devices  14 ′- 16 ′. In general, the peer device  12 ′ includes a control system  154  having associated memory  156 . In this example, the music player  22 ′ and the recommendation engine  24 ′ are at least partially implemented in software and stored in the memory  156 . The peer device  12 ′ also includes a storage unit  158  operating to store the music collection  26 ′ ( FIG. 4 ). The storage unit  158  may be any number of digital storage devices such as, for example, one or more hard-disc drives, one or more memory cards, RAM, one or more external digital storage devices, or the like. The music collection  26 ′ may alternatively be stored in the memory  156 . The peer device  12 ′ also includes a communication interface  160 . The communication interface  160  includes a network interface communicatively coupling the peer device  12 ′ to the network  20  ( FIG. 4 ). The peer device  12 ′ also includes a user interface  162 , which may include components such as a display, speakers, a user input device, and the like. 
         [0091]    The present invention provides substantial opportunity for variation without departing from the spirit or scope of the present invention. For example, while  FIG. 1  illustrates the peer devices  12 - 16  forming the P2P network via local wireless communication and  FIG. 4  illustrates the peer devices  12 ′- 16 ′ forming the P2P network via the network  20 , the present invention is not limited to either a local wireless P2P network or a WAN P2P network in the alternative. More specifically, a particular peer device, such as the peer device  12 , may form a P2P network with other peer devices using both local wireless communication and the network  20 . Thus, for example, the peer device  12  may receive recommendations from both the peer devices  14 ,  16  ( FIG. 1 ) via local wireless communication and from the peer devices  14 ′- 16 ′ ( FIG. 4 ) via the network  20 . 
         [0092]    As another example, while the discussion herein focuses on song recommendations, the present invention is not limited thereto. The present invention is equally applicable to recommendations for other types of media presentations such as video presentations. Thus, the present invention may additionally or alternatively provide movie recommendations, television program recommendations, or the like. 
         [0093]    Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the present invention. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow.