Patent Publication Number: US-11641506-B2

Title: Systems and methods for providing media recommendations

Description:
BACKGROUND 
     This disclosure relates to recommending media assets to a user, and more particularly, to systems and methods for identifying users by facial recognition and presenting media content recommendations to the users, and systems and methods for performing an action related to an identifier of recommended media content presented to a user, based on a detected emotional indicator of the user. 
     SUMMARY 
     Modern media distribution systems enable a user to access more media content than ever before. However, given the large variety of media providers and media assets available to a user, it may a challenging task for users of media services (e.g., cable, broadcast, satellite, over-the-top provider) to efficiently locate content he or she is interested in. 
     In one approach, recommended content may be provided to a user based on other content the user has consumed. However, many viewers prefer consuming content with friends and family, and recommended content based on viewing habits of only one of the users may not be useful in finding content that would be enjoyable to multiple users with different interests. In another approach, a user may be permitted to scroll through various recommended content items in order to locate a content item he or she is interested in. However, such approach merely enables a user to passively navigate a static set of recommended content items, without taking into account whether the user, in real time, is interested in any of the content items. This may frustrate the user, such that the user may decide not to consume media at all. In such instance, the next time the user attempts to consume content he or she may merely be provided with the same recommendations that did not interest him or her (e.g., since his or her viewing history is unchanged). 
     In some embodiments, to overcome one or more of these problems, systems and methods are provided herein for presenting a graphical user interface (GUI) including identifiers for media assets recommended for each of multiple users detected to be in the vicinity of the user equipment. A content recommendation application identifies, using facial recognition, a plurality of users (including a first user and a second user) in a vicinity of user equipment, and determines a first recommended media asset for the first user and a second recommended media asset based on respective user profiles of the first and second user. The content recommendation application generates for presentation the GUI including a first identifier selectable to access the first recommended media asset and a second identifier selectable to access the second recommended media asset, and in response to receiving selection of the first identifier or the second identifier, generates for presentation the recommended media asset associated with the selected identifier. Such aspects allow simultaneous presentation of recommended media assets for each user that is interested in consuming media, to facilitate selection of content each user can enjoy. In addition, even if one of the users (e.g., the second user) has never used a device (e.g., a television at the first user&#39;s home) on which content is to be consumed, recommended content for such user can conveniently be presented without requiring any effort on the part of the user. 
     In some embodiments, to overcome one or more of the above problems, systems and methods are also provided herein for performing an action related to an identifier for a recommended media asset based on a detected emotional indicator of a user. A content recommendation application may generate for presentation to a user an identifier for an initial recommended media asset, and capture one or more images of the user while generating for presentation the identifier for the initial recommended media asset to the user. The content recommendation application may detect an emotional indicator of the user based on the one or more captured images, and perform, based on the detected emotional indicator, an action related to the identifier for the initial recommended media asset. Such aspects enable a suitable action (e.g., presenting an identifier for an updated recommended media asset, selecting the identifier for a media asset, presenting a preview of the recommended media asset, refraining from updating the media asset, etc.) to be dynamically performed based on an emotion being exhibited by the user (e.g., while reviewing one or more identifiers for recommended media assets). 
     In some embodiments, the content recommendation application may determine a third recommended media asset for the first user and the second user based on the user profile of the first user and the user profile of the second user. A third identifier selectable to access the third recommended media asset may be generated for presentation, and the third recommended media asset may be generated for presentation in response to receiving selection of the third identifier. 
     In some aspects of this disclosure, the GUI may further include a first category identifier associated with a first plurality of recommended media assets determined based on the user profile of the first user, where the first plurality of recommended media assets includes the first recommended media asset. The GUI may further include a second category identifier associated with a second plurality of recommended media assets determined based on the user profile of the second user, where the second plurality of recommended media assets may include the second recommended media asset. In some embodiments, the GUI may further include a third category identifier associated with a third plurality of recommended media assets (including the third recommended media asset) determined based on the user profile of the first user and the user profile of the second user. 
     In some embodiments, at least one recommended media asset included in the third plurality of recommended media assets may not be included in the first plurality of recommended media assets and the second plurality of recommended media assets. User profiles of each user may include a viewing history of the user, and the user profiles may be updated based on selection of the third identifier. 
     The GUI may further include a first view associated with the first user in which the first identifier is presented more prominently than the second identifier and the third identifier, a second view associated with the second user in which the second identifier is presented more prominently than the first identifier and the third identifier, and a third view in which the third identifier is presented more prominently than the first identifier and the second identifier. The content recommendation application may generate for presentation a selectable option to navigate from the first view to the second view (and/or from the first view to the third view, and/or the second view to the third view, and/or vice versa). 
     In some embodiments, the content recommendation application may detect whether the second user remains within the vicinity of the user equipment, and the content recommendation application may, in response to determining that the second user has not been within the vicinity of the user equipment for a predefined period of time, cease the generating for presentation of the identifier of the second recommended media asset (and/or the third recommended media asset). 
     In some aspects of this disclosure, the content recommendation application may determine that the detected emotional indicator indicates the user is not interested in the initial recommended media asset, and the action to be performed based on the detected indicator may comprise generating for presentation an identifier for an updated recommended media asset. The updated recommended media asset associated with the identifier may be determined based on a retrieved user profile and the detected emotional indicator. 
     In some embodiments, the content recommendation application may determine the detected emotional indicator indicates the user is interested in the initial recommended media asset, and the action may comprise selecting the identifier for the initial recommended media asset. 
     Detecting the emotional indicator of the user may comprise identifying at least one of a facial expression of the user or body language of the user. The content recommendation application may detect an initial emotional indicator of the user prior to generating for presentation the identifier for the initial recommended media asset, where the identifier for the initial recommended media asset is generated for presentation based on the initial emotional indicator of the user. 
     In some embodiments, the content recommendation application may generate for presentation a plurality of identifiers for respective initial recommended media assets, where the plurality of identifiers for the respective initial recommended media assets includes the identifier for the initial recommended media asset. The emotional indicator of the user may be detected while receiving a command from the user to scroll through the identifiers of the plurality of initial recommended media assets. 
     In some embodiments, the content recommendation application may store in memory a table of facial characteristics and corresponding emotional indicators, and detecting the emotional indicator based on the one or more captured images may comprise identifying facial characteristics of a face of the user in the one or more captured images; comparing the identified facial characteristics to the stored facial characteristics; determining, based on the comparison, whether the identified facial characteristics match the stored facial characteristics; and in response to determining the identified facial characteristics match the stored facial characteristics, determining the emotional indicator of the user is the emotional indicator that corresponds to the matched facial characteristic. 
     The content recommendation application may identify a plurality of users in a vicinity of user equipment, where the user is included in the plurality of users, and at least one of the plurality of users is detected by facial recognition; capture one or more images of the plurality of users while generating for presentation the identifier for the initial recommended media asset to the users; detect, based on the captured images, respective emotional indicators of the plurality of users while generating for presentation the identifier for the initial recommended media asset to the users; and determine an aggregate emotional indicator of the plurality of users; wherein the action to be performed is determined based on the aggregate emotional indicator of the plurality of users. In some embodiments, detecting respective emotional indicators of the plurality of users comprises identifying at least one of the facial expressions of the users or body language of the users. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other objects and advantages of the present disclosure will be apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout, and in which: 
         FIG.  1    shows an example of generating identifiers for recommended media assets for multiple users detected to be in a vicinity of user equipment, in accordance with some embodiments of this disclosure; 
         FIG.  2 A  shows an example of performing, based on a detected emotional indicator, an action related to an identifier for a recommended media asset, in accordance with some embodiments of this disclosure; 
         FIG.  2 B  shows an example of performing, based on a detected emotional indicator, an action related to an identifier for a recommended media asset, in accordance with some embodiments of this disclosure; 
         FIG.  2 C  shows an example of performing, based on a detected emotional indicator, an action related to an identifier for a recommended media asset, in accordance with some embodiments of this disclosure; 
         FIG.  2 D  shows an example of performing, based on a detected emotional indicator, an action related to an identifier for a recommended media asset, in accordance with some embodiments of this disclosure; 
         FIG.  3    is a block diagram of an illustrative system in accordance with some embodiments of the disclosure; 
         FIG.  4    is another block diagram of an illustrative system in accordance with some embodiments of the disclosure; 
         FIG.  5    is a flowchart of a detailed illustrative process for generating identifiers for recommended media assets for multiple users detected to be in a vicinity of user equipment, in accordance with some embodiments of this disclosure; 
         FIG.  6    is a flowchart of a detailed illustrative process for generating identifiers for recommended media assets for multiple users detected to be in a vicinity of user equipment, in accordance with some embodiments of this disclosure; 
         FIG.  7    is a flowchart of a detailed illustrative process for performing, based on a detected emotional indicator, an action related to an identifier for a recommended media asset, in accordance with some embodiments of this disclosure; and 
         FIG.  8    is a flowchart of a detailed illustrative process for performing, based on a detected emotional indicator, an action related to an identifier for a recommended media asset, in accordance with some embodiments of this disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIG.  1    shows an example of system  100  generating identifiers for recommended media assets for multiple users detected to be in a vicinity of user equipment, in accordance with some embodiments of this disclosure. System  100  may include user equipment  106  (e.g., a television, mobile device, phone, tablet, computer, or any other computing device) and sensor  108  (e.g., a camera) communicatively coupled to (or included as part of) user equipment  106 . User equipment  106  may include a graphical user interface (GUI), which may include one or more GUIs  110 ,  120 , enabling users to interact with a content recommendation application. User equipment  106 , sensor  108 , biometric database  116 , and user profile database  118  may be communicatively coupled via a network (e.g., network  308  of  FIG.  3   , network  458  of  FIG.  4   ). As referred to herein, the term “media asset” should be understood to mean an electronically consumable user asset, such as television programming, as well as pay-per-view programs, on-demand programs (as in video-on-demand (VOD) systems), Internet content (e.g., streaming content, downloadable content, webcasts, etc.), videos, video clips, audio, playlists, websites, articles, electronic books, blogs, social media, applications, games, and/or any other media or multimedia, and/or combination of the same. In some embodiments, biometric database  116  and user profile database  118  may be included in any of server  302 , media content source  304 , and/or media guidance data source  306  of  FIG.  3   . 
     Users  102  and  104  may be viewing recommended content GUI  110  provided by the content recommendation application, and sensor  108  may capture in real time one or more images of users  102  and  104 . The content recommendation application may analyze image  112  of a face of user  102  and image  114  of a face of user  104 , in order to identify facial characteristics of users  102  and  104 . For example, the content recommendation application may utilize any suitable facial recognition algorithm and/or image processing techniques to identify or extract various features (e.g., distance between eyes, distance from chin to forehead, shape of jawline, depth of eye sockets, height of check bones, overall arrangement of facial features, size and shape of facial features, etc.) of the face of user  102  in image  112  and the face of user  104  in image  114 . 
     The content recommendation application may compare the identified facial features of user  102  to facial feature information of users stored in biometric database  116 , and may compare the identified facial features of user  104  to one or more tables of facial feature information corresponding to users stored in biometric database  116 . Based on such comparisons, the content recommendation application may determine whether there is a match between identified facial features of users  102 ,  104  and facial features of users stored in the biometric database. In some embodiments, the content recommendation application may compute a similarity score for each comparison, and may determine that there is a match if a computed similarity score exceeds a certain threshold. 
     In some embodiments, the content recommendation application may generate an image signature or facial signature of user  102  and user  104 . For example, the facial signature may comprise a feature vector including numerical values representing the various detected facial features (e.g., a numerical value representing a distance between eyes, a numerical value representing a shape of jawline, etc.) and such feature vector may be compared to feature vectors associated with known faces of users in biometric database  116 . 
     The content recommendation application may determine based on the above comparison that the identified facial features of image  112  match biometric data for user  102 , and that the identified facial features of image  114  match biometric data for user  104 . In response to such determinations, the content recommendation application may retrieve user profiles for each of user  102  and  104  from user profile database  118 . The user profiles may indicate, e.g., various interests of the user, viewing history of the user, prior search queries of the user, prior interactions with media assets by the user, social media interactions by the user related to media assets, etc. Although user profile database  118  and biometric database  116  are depicted as separate databases, it should be appreciated that user profile database  118  and biometric database  116  may be a single database. 
     GUI  120  may be generated for presentation to users  102  and  104 , including identifiers for media assets  134 ,  136 , and  138 , recommended based on the retrieved user profiles of users  102 ,  104 . GUI  120  may include identifier  128  indicating a category of one or more media assets recommended for user  102  (“John”), identifier  130  indicating a category of one or more media assets recommended for user  104  (“Mike”), and identifier  132  indicating a category of one or more media assets recommended for both user  102  and user  104  (e.g., a blended recommendation tailored to appeal to each of user  102  and user  104  by taking into consideration viewing history and/or interests of each of user  102  and user  104 ). Although  FIG.  1    shows a single media asset identifier for each category being generated for presentation by the content recommendation application to avoid overcomplicating the drawing, it should be appreciated that any number of identifiers for media assets may be generated for presentation for each category. The identifiers for the recommended media assets, and the media assets, may be retrieved from, e.g., server  302 , media content source  304 , and/or media guidance data source  306  of  FIG.  3   . 
     GUI  120  may provide identifiers  134 ,  136  of media assets recommended to users  102 ,  104 , respectively, enabling each user to simultaneously be provided with a recommended media asset. For example, even if users  102  and  104  are accessing the content recommendation application under a profile associated with only user  102 , recommendations tailored to user  104  may additionally be provided without requiring any effort form user  104  (e.g., since user  104  may be identified based on facial recognition, which may be used to log in to a profile associated with user  104 ). In some embodiments, if a user is already accessing his or her profile when a new user is detected by sensor  108 , the content recommendation application may update GUI  120  to additionally include an identifier for recommended media assets for the new user. Alternatively, none of the users may be accessing his or her profile prior to the content recommendation application initiating the process shown in system  100 . 
     In some embodiments, options  122 ,  124 ,  126  may be selectable by a user to alter presentation of GUI  120 . For example, if the content recommendation application receives user selection of option  124  (associated with user  104 , “Mike”), GUI  120  may be updated such that the identifier for recommended media asset  136  may be moved to a more prominent position (e.g., switched with the identifier for recommended media asset  134 , presented as larger relative to the other identifiers, etc.). Similarly, option  126  may be selected to cause GUI  120  to more prominently present the identifier for recommended media asset  138  relative to the other identifiers. 
     Media assets in category identifier  132  may be recommended by the content recommendation application based on a comparison of media assets  134  and  136 , and/or based on information in the retrieved user profiles of users  102  and  104  identified via facial recognition and image processing techniques. For example, the content recommendation application may recommend media asset  138  at least in part due to media asset  138  sharing features with media asset  134  (e.g., each starring the actor Christian Bale) recommended to user  102  and media asset  136  (e.g., each directed by Christopher Nolan) recommended to user  104 . The content recommendation application may determine that media asset  138  is a “compromise” recommendation, e.g., while user  104  (“Mike”) may not be interested in horror movies like media asset  134  (“American Psycho”) recommended to user  102 , user  104  still enjoys thrillers (e.g., such as media asset  138 , “The Dark Knight”), and while user  102  (“John”) prefers horror movies, he also enjoys the actor Christian Bale (cast in both media asset  134  and media asset  138 ). The content recommendation application may generate for presentation a media asset (e.g., from among media assets  134 ,  136 ,  138 ) selected by users  102 ,  104 . 
     In some embodiments, at least one media asset may be recommended under category identifier  132  that may not otherwise be recommended to user  102  or user  104  under categories  128  and  130 , respectively. Additionally or alternatively, a media asset recommended to one of user  102  and  104  may be determined to be suitable as a group recommendation, and/or a media asset recommended to each of user  102  and  104  may be generated for presentation as a group recommendation in category  132 . Upon receiving selection of content included in category  132 , the content recommendation application may update the user profiles of at least one of users  102  and  104  based on the selection. Alternatively, the content recommendation application may refrain from updating the profiles of the users when content is selected from category  132 . 
     In some embodiments, if the content recommendation application does not detect a user (e.g., user  104 ) for a predefined period of time (e.g., 5 minutes), the content recommendation application may cease generating for presentation an identifier associated with media asset  136  for such user, and optionally remove category  130  from GUI  120 . The content recommendation application may additionally or alternatively remove category  132  in response to failing to detect user  104  after a predefined period of time. 
     Although sensor  108  is depicted in the example of  FIG.  1    as a camera, in some aspects of this disclosure, additional or alternative types of sensors may be employed in connection with the content recommendation application. For example, the content recommendation application may identify a voice of a known user by comparing sampled audio (e.g., detected via a microphone) to an audio signature stored for the user in a database, in order to retrieve recommended content for such user. In some embodiments, any combination of biometric devices may be used (e.g., to detect a fingerprint of a user, gaze of a user, etc.) in order to identify a user. 
     In some embodiments, GUI  120  may be configured to provide a tab option, which enables a user to switch between recommended content for user  102 , recommended content for user  104 , and recommended content for the group. For example, a first screen may show only content recommended for user  102 , and a user may select an option (e.g., option  124 ) to navigate from the first screen to a second screen, which may show only content recommended for user  104 , or an option (e.g., option  126 ) to navigate to a third screen, which may only show content recommended collectively for the group. 
       FIG.  2 A  shows an example of system  200  performing, based on a detected emotional indicator, an action related to an identifier for a recommended media asset, in accordance with some embodiments of this disclosure. User  202  may be viewing GUI  210  generated for presentation on user equipment  206  to provide user  202  with initial recommended media asset  207 . In some embodiments, recommended media asset  207  may be provided based on a user profile of user  202  (e.g., retrieved using the techniques discussed in  FIG.  1   ), and/or based on a detected emotional indicator of a user. The user may have provided other input (e.g., entered log-in via a button, a remote control, text or voice) to access his or her content recommendation application profile. 
     While user  202  is viewing GUI  210 , sensor  208  (e.g., a camera) may capture in real time, and analyze, one or more images  212  of user  202 , and/or capture in real time and analyze other biometric feedback received from the user (e.g., analyze audio of the user detected by a microphone, or any other biometric response or combination thereof). The content recommendation application may analyze the one or more images  212  to identify or extract information regarding various features in the face of user  202  (e.g., facial expressions, gaze patterns, body language, position of eyes, mouth, nose, etc.). The identified or extracted features may be compared to one or more tables of facial features and corresponding emotional indicators stored in emotional indicators database  214  to determine which emotional indicator the identified or extracted features in the one or more images  212  of user  202  correspond to. In some embodiments, the content recommendation application may determine a match if comparison results indicate at least a partial match above a certain threshold (e.g., 50). In some embodiments, a feature vector may be computed for the identified or extracted features in the one or more images  212  of user  202 , and compared to feature vectors of facial characteristics corresponding to respective emotional states (e.g., happy, interested, neutral, sad, disinterested, surprised) stored in emotional indicator database  214 . 
     In some embodiments, the content recommendation application may compute a confidence level (e.g., 80% chance the user is laughing or smiling, 75% chance the user is angry) based on the detected facial features or characteristics, which may be used in the detecting of an appropriate emotional indicator (e.g., interested, neutral, not interested) with respect to presented identifiers of recommended media asset  207 . In some embodiments, movement patterns by the user may be captured (e.g., including facial expressions, body language, hand gestures) in determining an emotional state of the user. For example, analysis of captured image  212  of the user may indicate the user is shaking his or her head no, indicating he or she is not interested in the initial media asset recommendation  207 . 
     If the content recommendation application determines there is match  218  between the facial features identified or extracted from image  212  of the user and features associated with an emotional indicator  216  (e.g., an emotion of “sad”), the content recommendation application may determine that user  202  is unhappy with and otherwise disinterested in initial recommended asset  207 . Thus, the content recommendation application may perform an action related to the identifier of media asset  207  in accordance with the detected emotional indicator of user  202 . For example, the content recommendation application may reference user profile database  220  in order to obtain media preferences of user  202 , and may use such media preferences to recommend one or more new media assets (e.g., from server  302 , media content source  304 , and/or media guidance data source  306  of  FIG.  3   ) that may be of more interest to the user than media asset  207 . In some embodiments, the identifier for media asset  207  that user  202  is determined to be disinterested in may be replaced in GUI  222  by the identifier for updated media asset recommendation  236 . Alternatively, the user may be prompted as to whether he or she would like to remove media asset  207  from GUI  222  and/or be eliminated from impacting future recommendations, or the identifier for updated media asset recommendation  236  may be added to GUI  222  without removing the identifier for initial media asset recommendation  207 . In some embodiments, emotional indicator database  214  and user profile database  220  may be included in any of server  302 , media content source  304 , media guidance data source  306  of  FIG.  3   . 
     If multiple images of user  202  are captured during a user session, the content recommendation application may compare each set of facial characteristics associated with respective captured images to determine respective emotional indicators for each image. Such respective emotional indicators may be used to determine an aggregate emotional indicator of the user during the user session, such as by utilizing one or more of a variety of techniques (e.g., an average emotional indicator of the detected emotional indicators over the time period, the most common emotional indicator detected over the time period, the most recent emotional indicator detected during the time period, the emotional indicator having the highest confidence score over the time period, or any combination thereof). 
     In some embodiments, the content recommendation application may wait a predetermined period of time (e.g., 10 seconds) prior to updating a media asset recommendation based on a detected emotional state of the user. For example, the content recommendation application may update recommended content upon determining that the emotional indicators over such predetermined period of time indicate the user is consistently not interested in recommended content while scrolling through recommendations. 
     In some embodiments, a plurality of identifiers for respective initial recommended media assets may be presented to the user, and the emotional indicator of the user may be detected while the user is scrolling through the plurality of initial recommended media assets. Prior to performing an action related to the identifiers, the content recommendation application may wait until a predefined time has elapsed (e.g., 10 seconds). If GUI  210  or  222  includes a plurality of identifiers for media assets, the content recommendation application may determine a media asset of interest based on which identifier is highlighted by the user via a cursor or selector. If the content recommendation application determines user  202  is not interested in the highlighted media asset (e.g., if the user had highlighted the media asset to see more details or a description of the media asset listing), such media asset identifier may be replaced with an identifier for an updated media asset. 
       FIG.  2 B  shows an example of system  201  performing, based on a detected emotional indicator, an action related to an identifier for a recommended media asset, in accordance with some embodiments of this disclosure. The example of  FIG.  2 B  is similar to the example of  FIG.  2 A , except the content recommendation application, after analyzing one or more images  224  of user  202 , may determine that the identified or extracted facial characteristics of user  202  match  228  stored characteristics of a “happy” or interested emotional indicator  226  stored in emotional indicator database  214 . In this instance, the content recommendation application determines the user is interested in one or more media assets currently presented, and may perform a suitable action (e.g., the content recommendation application may automatically generate for presentation recommended media asset  207  on GUI  232 , prompt user  202  to indicate whether he or she desires to consume media asset  207 , provide a countdown until media asset  207  is to be generated for presentation, and/or generate for presentation a preview of recommended media asset  207 ). 
     In some embodiments, prior to taking action, the content recommendation application may wait until the user has exhibited emotional indicator  226  for the majority of a time period (e.g., 3 seconds out of 5 seconds) of viewing GUI  210 , or the average emotional indicator for the user over a certain time period indicates he or she is interested in the media asset. In some embodiments, the content recommendation application may automatically add, or prompt the user to add, media asset  207  to his or her watch list or favorite list associated with a user profile of user  202 , when emotional indicator  226  indicates the user is interested in a media asset. Additionally or alternatively, the content recommendation application may generate for presentation identifiers, at a current time or a later time, recommending other media assets sharing characteristics with media asset  207 , and/or update the profile of the user based on media asset  207 . 
       FIG.  2 C  shows an example of system  203  performing, based on a detected emotional indicator, an action related to an identifier for a recommended media asset, in accordance with some embodiments of this disclosure. The example of  FIG.  2 C  is similar to the examples of  FIGS.  2 A and  2 B , except the content recommendation application, after analyzing one or more images  234  of user  202 , may determine that the identified or extracted facial characteristics of user  202  match  235  stored characteristics of a “neutral” emotional indicator  237  stored in emotional indicator database  214 . In this instance, it may be inconclusive whether user  202  is interested or not in media asset  207 , and the content recommendation application may perform one or more of various actions, e.g., refrain from performing an action until a more conclusive emotional indicator is detected from the user, add an identifier for another recommended media asset  236  to GUI, show a preview or more information related to media asset  207 , etc. 
       FIG.  2 D  shows an example of system  203  performing, based on a detected emotional indicator, an action related to an identifier for a recommended media asset, in accordance with some embodiments of this disclosure. In this example, GUI  210  generated for presentation by the content recommendation application may be viewed by multiple users  202  and  204 . The content recommendation application may use techniques similar to those discussed in  FIGS.  2 A- 2 C  to detect respective emotional indicators associated with each of user  202  and user  204  while GUI  210 , including initial recommended media asset  207 , is being generated for display. As shown in  FIG.  2 D , the content recommendation application may determine that image  240  of facial characteristics of user  202  matches  246  a “sad” or negative emotional indicator  244 , and that image  242  of facial characteristics of user  204  matches  250  a “happy”, interested or positive emotional indicator  248  stored in emotional indicator database  214 . 
     Since the emotional indicators of user  202  and  204  conflict (e.g., user  204  is interested whereas user  202  is not interested), the content recommendation application may perform an action to address the conflict. For example, the content recommendation application may generate for display an identifier for one or more updated media asset recommendations  236  (e.g., based on a user profile of user  202 , user  204 , or a combination thereof, and/or the emotional indicator itself), and subsequently monitor emotional indicators related to the new recommended media asset. As another example, the content recommendation application may generate for presentation a preview of the media asset  207 , and monitor emotional indicators of users  202  and  204  to determine subsequent action to be taken. In some embodiments, if one or more of the users detected by the content recommendation application does not have a user profile associated with the media service, other techniques may be used to generate for presentation updated recommendations (e.g., based on trending or popular programming, prompting such user to create a profile and enter his or her interests, etc.). Identifiers for recommended media assets, and media assets, may be retrieved from, e.g., server  302 , media content source  304 , and/or media guidance data source  306  of  FIG.  3   . 
     In some embodiments, a selector cursor or highlight icon may be used by the content recommendation application to determine which recommended media asset the user is reacting to. For example, in GUI  252 , if a selector cursor or highlight icon (e.g., being controlled by the user via input, or placed on a particular media asset when the user begins accessing the GUI of media asset identifiers) is associated with the identifier for recommended media asset  236 , the content recommendation application may determine that any detected emotional indicators of user  202  and  204  correspond to media asset  236 . If each of users  202  and  204  have the same or similar reactions to a media asset (e.g., there is no conflict in the emotional indicators of the users), an action consistent with the same or similar emotional indicator may be taken by the content recommendation application. 
     Although the example of  FIG.  2 D  shows two users  202  and  204  interacting with the content recommendation application, it should be appreciated that emotional indicators of any number of users may be detected by sensor  208  and used in performing an action related to an initial recommended media asset. In some embodiments, an aggregate emotional indicator of multiple users during the user session may be detected. For example, the emotional indicator exhibited by a majority of users in the captured images may determine the action to be performed, or an average emotional indicator for the users in the captured images may determine the action to be performed. Additionally or alternatively, a priority user may be designated (e.g., as the user holding a remote control for user equipment  206 , which may be captured in the one or more images, or the primary user associated with the particular account for the media provider), such that the emotional indicator of the priority user takes precedence in determining the aggregate emotional indicator. In some embodiments, an emotional indicator of a newly detected user may be detected (e.g., for at least a predetermined period of time) and may impact the action to be performed. In addition, if the content recommendation application detects that a user has exited the vicinity of user equipment (e.g., for at least a predetermined period of time), the emotional indicator associated with such user may be disregarded in determining an action to be performed related to recommending media assets. 
     In some embodiments, emotional indicator database  214  may store historical pictures of users (e.g., tagged or associated with a particular emotional indicator). When determining emotional indicators for a particular user, the content recommendation application may perform facial recognition to identify the user, and may compare the image of the identified user to past images of such user stored in emotional indicator database  214 . If the content recommendation application determines there is a close match between the image of the user and an image in emotional indicator database  214  (e.g., a similarity above a predefined threshold), the content recommendation application may determine that the current emotional state of the user corresponds to the emotional indicator associated with the image stored in emotional indicator database  214 . 
       FIG.  3    shows an illustrative block diagram of a system  300  for displaying content, in accordance with some embodiments of the disclosure. In various aspects, system  300  includes one or more of server  302 , media content source  304 , media guidance data source  306 , communication network  308 , and one or more computing devices or user equipment  310 , e.g., user television equipment  310   a  (e.g., a set-top box), user computer equipment  310   b  (e.g., a desktop or laptop), and/or wireless user communications device  310   c  (e.g., a smartphone device or tablet). The computing device or user equipment  310  may correspond to user equipment  106  and  206  in  FIGS.  1  and  2 A- 2 D , and may include one or more sensors or devices (e.g., a camera, a microphone, eye scanner, fingerprint scanner, remote control, etc.) to collect biometric data of users. Although  FIG.  3    shows one of each component, in various examples, system  300  may include fewer than the illustrated components, multiples of one or more illustrated components, and/or additional components. Communication network  308  may be any type of communication network, e.g., the Internet, a mobile phone network, mobile voice or data network (e.g., a 4G or LTE network), cable network, public switched telephone network, or any combination of two or more of such communication networks. Communication network  308  includes one or more communication paths, such as a satellite path, a fiber-optic path, a cable path, a path that supports Internet communications (e.g., IPTV), free-space connections (e.g., for broadcast or other wireless signals), or any other suitable wired or wireless communication path or combination of such paths. Communication network  308  communicatively couples various components of system  300  to one another. For instance, server  302  may be communicatively coupled to media content source  304 , media guidance data source  306 , and/or computing device  310  via communication network  308 . 
     In some examples, media content source  304  and media guidance data source  306  may be integrated as one device. Media content source  304  may include one or more types of content distribution equipment including a television distribution facility, cable system headend, satellite distribution facility, programming sources (e.g., television broadcasters, such as NBC, ABC, HBO, etc.), intermediate distribution facilities and/or servers, Internet providers, on-demand media servers, and other content providers. NBC is a trademark owned by the National Broadcasting Company, Inc.; ABC is a trademark owned by the American Broadcasting Company, Inc.; and HBO is a trademark owned by the Home Box Office, Inc. Media content source  304  may be the originator of content (e.g., a television broadcaster, a Webcast provider, etc.) or may not be the originator of content (e.g., an on-demand content provider, an Internet provider of content of broadcast programs for downloading, etc.). Media content source  304  may include cable sources, satellite providers, on-demand providers, Internet providers, over-the-top content providers, or other providers of content. Media content source  304  may also include a remote media server used to store different types of content (e.g., including video content selected by a user) in a location remote from computing device  310 . Systems and methods for remote storage of content and providing remotely stored content to user equipment are discussed in greater detail in connection with Ellis et al., U.S. Pat. No. 7,761,892, issued Jul. 20, 2010, which is hereby incorporated by reference herein in its entirety. 
     Media content source  304  and media guidance data source  306  may provide content and/or media guidance data to computing device  310  and/or server  302  using any suitable approach. In some embodiments, media guidance data source  306  may provide a stand-alone interactive television program guide that receives program guide data via a data feed (e.g., a continuous feed or trickle feed). In some examples, media guidance data source  306  may provide program schedule data and other guidance data to computing device  310  on a television channel sideband, using an in-band digital signal, an out-of-band digital signal, or any other suitable data transmission technique. 
     As described in further detail below, server  302  may manage the communication of a live content stream (e.g., a live sporting event broadcast, a live news broadcast, or the like) and recorded streams from media content source  304  to computing device  310  via communication network  308 . For instance, in some embodiments, content from media content source  304  and/or guidance data from media guidance data source  306  may be provided to computing device  310  using a client/server approach. In such examples, computing device  310  may pull content and/or media guidance data from server  302  and/or server  302  may push content and/or media guidance data to computing device  310 . In some embodiments, a client application residing on computing device  310  may initiate sessions with server  302 , media content source  304 , and/or media guidance data source  306  to obtain content and/or guidance data when needed, e.g., when the guidance data is out of date or when computing device  310  receives a request from the user to receive content or guidance data. In various aspects, server  302  may also be configured to detect events within the live content stream and, based on the detected events, control the display of content and/or navigation menu options via computing device  310 . Additionally, although  FIG.  3    shows media content source  304  and media guidance data source  306  as separate from server  302 , in some embodiments, media content source  304  and/or media guidance data source  306  may be integrated as one device with server  302 . 
     Content and/or media guidance data delivered to computing device  310  may be over-the-top (OTT) content. OTT content delivery allows Internet-enabled user devices, such as computing device  310 , to receive content that is transferred over the Internet, including any content described above, in addition to content received over cable or satellite connections. OTT content is delivered via an Internet connection provided by an Internet service provider (ISP), but a third party distributes the content. The ISP may not be responsible for the viewing abilities, copyrights, or redistribution of the content, and may transfer only IP packets provided by the OTT content provider. Examples of OTT content providers include FACEBOOK, AMAZON, YOUTUBE, NETFLIX, and HULU, which provide audio and video via IP packets. YouTube is a trademark owned by Google LLC; Netflix is a trademark owned by Netflix, Inc.; Hulu is a trademark owned by Hulu, LLC; Facebook is a trademark owned by Facebook, Inc.; and Amazon is a trademark owned by Amazon.com, Inc. OTT content providers may also include any other OTT content provider. OTT content providers may additionally or alternatively provide media guidance data described above. In addition to content and/or media guidance data, providers of OTT content can distribute applications (e.g., web-based applications or cloud-based applications), or the content can be displayed by applications stored on computing device  310 . 
       FIG.  4    is an illustrative block diagram showing additional details of the system  400  (which may be the same as system  300  of  FIG.  3   ), in accordance with some embodiments of the disclosure. In particular, server  401  (e.g., the same server as server  302 ) includes control circuitry  402  and I/O path  408 , and control circuitry  402  includes storage  404  and processing circuitry  406 . Computing device  460  (e.g., one or more of devices  310   a ,  310 , and  310   c ) includes control circuitry  410 , I/O path  416 , speaker  418 , display  420  (as well circuitry for generating images for display on display  420 ), and user input interface  422 . Control circuitry  410  includes storage  412  and processing circuitry  414 . Control circuitry  402  and/or  410  may be based on any suitable processing circuitry such as processing circuitry  406  and/or  414 . As referred to herein, processing circuitry should be understood to mean circuitry based on one or more microprocessors, microcontrollers, digital signal processors, programmable logic devices, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc., and may include a multi-core processor (e.g., dual-core, quad-core, hexa-core, or any suitable number of cores). In some embodiments, processing circuitry may be distributed across multiple separate processors, for example, multiple of the same type of processors (e.g., two Intel Core i9 processors) or multiple different processors (e.g., an Intel Core i7 processor and an Intel Core i9 processor). 
     Each of storage  404 , storage  412 , and/or storages of other components of system  300  (e.g., storages of media content source  454 , media guidance data source  456 , and/or the like) may be an electronic storage device. In some embodiments, media content source  454  may be the same as media content source  304 . In some embodiments, media guidance data source  456  may be the same as media content source  306 . As referred to herein, the phrase “electronic storage device” or “storage device” should be understood to mean any device for storing electronic data, computer software, or firmware, such as random-access memory, read-only memory, hard drives, optical drives, digital video disc (DVD) recorders, compact disc (CD) recorders, BLU-RAY disc (BD) recorders, BLU-RAY 3D disc recorders, digital video recorders (DVRs, sometimes called a personal video recorders, or PVRs), solid state devices, quantum storage devices, gaming consoles, gaming media, or any other suitable fixed or removable storage devices, and/or any combination of the same. Each of storage  404 , storage  412 , and/or storages of other components of system  400  may be used to store various types of content, media guidance data, and or other types of data. Non-volatile memory may also be used (e.g., to launch a boot-up routine and other instructions). Cloud-based storage may be used to supplement storages  404 ,  412  or instead of storages  404 ,  412 . In some embodiments, control circuitry  402  and/or  410  executes instructions for a content recommendation application stored in memory (e.g., storage  404  and/or  412 ). Specifically, control circuitry  402  and/or  410  may be instructed by the content recommendation application to perform the functions discussed herein. In some implementations, any action performed by control circuitry  402  and/or  410  may be based on instructions received from the content recommendation application. For example, the content recommendation application may be implemented as software or a set of executable instructions that may be stored in storage  404  and/or  312  and executed by control circuitry  402  and/or  410 . In some embodiments, the content recommendation application may be a client/server content recommendation application where only a client content recommendation application resides on computing device  460 , and a server content recommendation application resides on server  401 . 
     The content recommendation application may be implemented using any suitable architecture. For example, it may be a stand-alone content recommendation application wholly implemented on computing device  460 . In such an approach, instructions for the content recommendation application are stored locally (e.g., in storage  412 ), and data for use by the content recommendation application is downloaded on a periodic basis (e.g., from an out-of-band feed, from an Internet resource, or using another suitable approach). Control circuitry  410  may retrieve instructions for the content recommendation application from storage  412  and process the instructions to perform the functionality described herein. Based on the processed instructions, control circuitry  410  may determine what action to perform when input is received from user input interface  422 . 
     In client/server-based embodiments, control circuitry  410  may include communication circuitry suitable for communicating with a content recommendation application server (e.g., server  401 ) or other networks or servers. The instructions for carrying out the functionality described herein may be stored on the application server. Communication circuitry may include a cable modem, an integrated services digital network (ISDN) modem, a digital subscriber line (DSL) modem, a telephone modem, an Ethernet card, a wireless modem for communication with other equipment, or any other suitable communication circuitry. Such communication may involve the Internet or any other suitable communication networks or paths (e.g., communication network  458 ). In some embodiments, communication network  458  may be the same as network  308 . In another example of a client/server-based application, control circuitry  410  runs a web browser that interprets web pages provided by a remote server (e.g., server  401 ). For example, the remote server may store the instructions for the application in a storage device. The remote server may process the stored instructions using circuitry (e.g., control circuitry  402 ) and generate the displays discussed above and below. Computing device  460  may receive the displays generated by the remote server and may display the content of the displays locally via display  420 . This way, the processing of the instructions is performed remotely (e.g., by server  401 ) while the resulting displays, such as the display windows described elsewhere herein, are provided locally on computing device  460 . For example, computing device  460  may include display circuitry (e.g., video card circuitry or combination motherboard and video card circuitry) configured to generate for display the display windows. Computing device  460  may receive inputs from the user via input interface  422  and transmit those inputs to the remote server for processing and generating the corresponding displays. 
     A user may send instructions to control circuitry  402  and/or  410  using user input interface  422 . User input interface  422  may be any suitable user interface, such as a remote control, trackball, keypad, keyboard, touchscreen, touchpad, stylus input, joystick, voice recognition interface, or other user input interfaces. User input interface  422  may be integrated with or combined with display  420 , which may be a monitor, television, liquid crystal display (LCD), electronic ink display, or any other equipment suitable for displaying visual images. 
     Server  401  and computing device  460  may receive content and data via input/output (hereinafter “I/O”) path  408  and  416 , respectively. For instance, I/O path  416  may include circuitry that includes one or more of communication port configured to receive a live content stream from server  401  and/or media content source  454  via a communication network  458 . Storage  412  may be configured to buffer the received live content stream for playback, and display  420  may be configured to present the buffered content, navigation options, alerts, and/or the like via a primary display window and/or a secondary display window. I/O paths  408 ,  416  may provide content (e.g., a live stream of content, broadcast programming, on-demand programming, Internet content, content available over a local area network (LAN) or wide area network (WAN), and/or other content) and data to control circuitry  402 ,  410 . Control circuitry  402 ,  410  may be used to send and receive commands, requests, and other suitable data using I/O paths  408 ,  416 . I/O paths  408 ,  416  may connect control circuitry  402 ,  410  (and specifically processing circuitry  406 ,  414 ) to one or more communication paths (described below). I/O functions may be provided by one or more of these communication paths but are shown as single paths in  FIG.  4    to avoid overcomplicating the drawing. 
     Having described systems  300  and  400 , reference is now made to  FIG.  5   , which depicts an illustrative flowchart of process  500  for providing media content recommendations that may be implemented by using systems  300  and  400 , in accordance with some embodiments of the disclosure. In various embodiments, the individual steps of process  500  may be implemented by one or more components of systems  300  and  400 . Although the present disclosure may describe certain steps of process  500  (and of other processes described herein) as being implemented by certain components of systems  300  and  400 , this is for purposes of illustration only, and it should be understood that other components of systems  300  and  400  may implement those steps instead. For example, the steps of process  500  may be executed by server  401  and/or by computing device  460  to provide content recommendations. 
     At  502 , control circuitry  410  may identify, by facial recognition, multiple users (e.g., users  102  and  104  of  FIG.  1   ) in a vicinity of user equipment (e.g., user equipment  106  of  FIG.  1   ). Facial recognition may be facilitated by capturing one or more images (e.g., images  102  and  104  of  FIG.  1   ) of each user by way of a sensor (e.g., sensor  108  of  FIG.  1   , which may be an image sensor), and performing image processing and facial recognition techniques on the captured images to extract and/or identify features of the faces of the users. The extracted facial characteristics may be compared to facial characteristics of users in a database (e.g., biometric database  116  of  FIG.  1   ) to determine whether there is a match. In some embodiments, upon setting up a user profile, the user may be prompted to provide an image of himself or herself, in order to populate the database for subsequent matching. 
     In some embodiments, artificial intelligence (e.g., machine learning) techniques may be employed by control circuitry  410  and/or control circuitry  402  in matching facial characteristics of users to known users in a database (e.g., biometric database  116  of  FIG.  1   ). For example, a neural network or convolutional neural network machine learning model (e.g., stored in local memory  412  of  FIG.  4    and/or remote server  401  of  FIG.  4   ) may be trained to accept as input two sets of facial feature characteristics (e.g., feature vectors), and output a match probability (e.g., by identifying key features or patterns predictive of a match). Training data may include image pairs labeled as matches (e.g., by human reviewers). The match probability may be compared to a threshold value to determine whether there is a match between the two sets of facial characteristics. Neural networks are discussed in greater detail in connection with Brehm, U.S. Patent Application Publication No. US 2020/0183773 A1, published Jun. 11, 2020, which is hereby incorporated by reference herein in its entirety. In some embodiments, other machine learning models may additionally or alternatively be employed (e.g., classifier algorithms, K-nearest neighbors, etc.) 
     At  504 , control circuitry  410  may query a database (e.g., user profile database  118  of  FIG.  1   ) based on the identified users, to obtain user preferences and/or a viewing history associated with user profiles of the identified users (e.g., users  102  and  104  of  FIG.  1   ). In some embodiments, the user profile database and the biometric database may be a single database (e.g., at server  302  of  FIG.  3   ). 
     At  506 , control circuitry  410  and/or control circuitry  402  may determine recommended media assets for the identified users (e.g., users  102 ,  104  of  FIG.  1   ). In some embodiments, if a particular user does not have a user profile previously registered with a media provider, other factors may be taken into account to create a user profile for the user in order to provide media assets recommendations (e.g., a demographic of the user identified by the facial recognition, popular or trending programming, etc.). 
     At  508 , the control circuitry may generate for presentation a GUI (e.g., GUI  120  of  FIG.  1   ) including identifiers for each recommended media asset (e.g., respective identifiers of media assets  134 ,  136 ,  138 ). In some embodiments, the recommended media assets may be categorized based on an associated user (e.g., category  128  may correspond to user  102  of  FIG.  1   , category  130  may correspond to user  104  of  FIG.  1   ). 
     At  510 , the control circuitry may receive user selection of one of the identifiers generated for presentation on a GUI (e.g., GUI  120  of  FIG.  1   ). In some embodiments, the GUI may include selectable options (e.g., option  128  associated with user  102 , option  130  associated with user  104 ) the selection of which enables an identifier for a particular media asset recommended to a user (e.g., user  102 ) to be presented more prominently than that of another identified user (e.g., user  104 ). In some aspects of this disclosure, the selectable option may be a tab to toggle between identifiers of recommended media assets for the identified users. 
     If, at  510 , user selection of an identifier is received, control circuitry generates for presentation the media asset associated with the selected identifier, at  512 . If such a selection has not yet been received, the control circuitry may continue to generate for presentation the identifiers of the media assets, to wait for user selection of one of the identifiers. 
     In some embodiments, control circuitry  410 , communicatively coupled to a sensor (e.g., sensor  108  of  FIG.  1   ), may continuously check whether one of the identified users has exited the vicinity of the user equipment, or whether a new user has entered the vicinity of the user equipment. Control circuitry  410  may, if one of the identified users exits the vicinity of the user equipment, cease generating for presentation recommended content for such user. On the other hand, if control circuitry  410  detects that a new user has entered the vicinity of the user equipment, the process of identifying the user by facial recognition and generating predicted recommendations for such user based on a retrieved user profile may be initiated, and the GUI (e.g., GUI  120  of  FIG.  1   ) may be updated to include a media asset recommendation for the new user (and optionally a category identifier associated with the new user). 
       FIG.  6    depicts an illustrative flowchart of process  600  for providing media content recommendations that may be implemented by using systems  300  and  400 , in accordance with some embodiments of the disclosure. In various embodiments, the individual steps of process  600  may be implemented by one or more components of systems  300  and  400 . Although the present disclosure may describe certain steps of process  600  (and of other processes described herein) as being implemented by certain components of systems  300  and  400 , this is for purposes of illustration only, and it should be understood that other components of systems  300  and  400  may implement those steps instead. For example, the steps of process  600  may be executed by server  401  and/or by computing device  460  to provide content recommendations. 
     Steps  602  and  604  may be performed in a similar fashion to steps  502  and  504  described above. Step  606  may be performed in a similar fashion to step  506  described above, and may additionally include determining recommended media assets based on the user profiles of the identified users (e.g., users  102  and  104 ) blended together. For example, control circuitry (e.g., control circuitry  410  and/or control circuitry  402 ) may identify overlap (e.g., a same media asset or a similar media asset, similar interests as between users. etc.) between the user profiles and/or recommended media assets associated with the users (e.g., users  102  and  104 ), and determine a media asset most likely to interest each of the users. 
     Step  608  may be performed in a similar fashion to step  508  described above, and may additionally include generating for presentation a GUI (e.g., GUI  120  of  FIG.  1   ) including one or more identifiers for media assets recommended based on the blended user profiles of the users. The one or more identifiers (e.g., associated with media asset  138  of  FIG.  1   ) may be presented as part of a category (e.g., associated with category identifier  132 ) associated with the blended user profile recommendation. 
     At  610 , the control circuitry  410  may determine whether one of the identified users left the vicinity of user equipment (e.g., user equipment  106  of  FIG.  1   ). For example, if one of the users is absent from the vicinity of the user equipment for a predetermined period of time (e.g., 2 minutes), the recommended content for such user may be removed from the GUI, and the blended recommendation may be removed (e.g., if the group includes only 2 users before the user exited) or altered (e.g., if the group still includes multiple users after the user exits, in order to prevent the interests of the absent user from impacting the media assets recommended to the group). In some embodiments, a user may be interested in what his or her friends are watching (e.g., if a user&#39;s friend is known as a movie buff), and may select an option to receive recommended media assets (and/or blended recommended media assets) based on his or her friend&#39;s profile (e.g., even if the friend is not present or is now absent from the vicinity of the user equipment), which may be separately or simultaneously displayed with his or her own recommendations. If no users left the vicinity of the user equipment, processing may continue at step  612 . 
     At  612 , the control circuitry may determine that no users have left the vicinity of the user equipment, and may additionally or alternatively determine whether a new user entered a vicinity of the user equipment (e.g., in the time period since identifiers of media assets  134 ,  136 ,  138  of  FIG.  1    were generated for presentation). In some embodiments, if a new user stays in the vicinity of the user equipment for a threshold period of time (e.g., 15 seconds), the control circuitry may perform facial recognition on the new user, and update the GUI to include a recommended media asset for the new user, as well as update the blended recommendations for the group of users, taking into account user preferences of the user profile of the new user in addition to the preferences of the other identified users in the group. If no users have left the vicinity of the user equipment, processing may continue at step  614 . 
     At  614 , control circuitry  410  may determine whether selection of one of the presented identifiers (e.g., an identifier associated with media asset  138  of  FIG.  1   ) is received. If such a selection is received, processing may move to step  616 . If such a selection is not received, the control circuitry may wait until such a selection is detected. 
     At  616 , the control circuitry may generate for presentation the media asset (e.g., media asset  138  of  FIG.  1   ) associated with selected identifier. In some embodiments, selection of the blended recommended media asset causes the user profiles of at least a subset of the group of users in the vicinity of the user equipment to be updated. 
       FIG.  7    depicts an illustrative flowchart of process  700  for providing media content recommendations that may be implemented by using systems  300  and  400 , in accordance with some embodiments of the disclosure. In various embodiments, the individual steps of process  700  may be implemented by one or more components of systems  300  and  400 . Although the present disclosure may describe certain steps of process  700  (and of other processes described herein) as being implemented by certain components of systems  300  and  400 , this is for purposes of illustration only, and it should be understood that other components of systems  300  and  400  may implement those steps instead. For example, the steps of process  700  may be executed by server  401  and/or by computing device  460  to provide content recommendations. 
     At  702 , control circuitry  410  may generate for presentation to a user (e.g., user  202  of  FIGS.  2 A- 2 D ) one or more identifiers for respective initial recommended media assets (e.g., media asset  207  of  FIG.  2 A ). The recommended media asset may be provided based on a user profile of a user retrieved from a database (e.g., user profile database  220  of  FIG.  2 A ). In some embodiments, the user and associated user profile may be identified by a media provider based on biometric data (e.g., facial recognition) or other input (e.g., remote control, text, voice, etc.). 
     At  704 , control circuitry  410  may be communicatively coupled with a sensor (e.g., sensor  208  of  FIG.  2 A ), which captures one or more images (e.g., image  212  of  FIG.  2 A ) of the user (e.g., user  202  of  FIG.  2 A ) while the user is viewing the identifier for the initial recommended media asset (e.g., media asset  207 ). 
     At  706 , control circuitry  410  may perform image processing and/or facial recognition techniques on the one or more captured images (e.g., image  212  of a face of user  202  of FIG.  2 A) to extract or identify various features (e.g., facial expressions, body language, position of facial features, etc.). 
     At  708 , control circuitry  410  may compare such identified or extracted facial features to records in a database (e.g., emotional indicator database  214  of  FIG.  2 A ) indicating relationships between facial features and emotional indicators, to determine whether the identified or extracted facial features match a particular emotional indicator. For example, various numerical values may be assigned to identified or extracted facial characteristics in a feature vector, and such feature vector may be compared to feature vectors stored in database records associated with respective emotional indicators (e.g., interested, neutral, not interested). Control circuitry  410  may compute a similarity score based on the comparison, and may determine there is a match if the computed score is above a certain threshold. 
     In some aspects of this disclosure (e.g., where multiple images of the user are captured), control circuitry  410  may employ a variety of techniques to determine an aggregate emotional indicator of the user during a particular time period of the user session (e.g., when the user views a particular identifier of an initial media asset recommendation). The control circuitry may detect respective emotional indicators that match each of detected facial characteristics, and such emotional indicators may be used to determine an aggregate emotional indicator of the user during the particular time period of the user session, based on a variety of techniques (e.g., an average emotional indicator of the user among the detected emotional indicators over the time period, the most common emotional indicator of the user detected over the time period, the most recent emotional indicator of the user detected during the time period, the emotional indicator of the user having the highest confidence score over the time period, or any combination thereof). 
     In some embodiments, artificial intelligence (e.g., machine learning) techniques may be employed by control circuitry  402  and/or control circuitry  410  in matching facial characteristics of users to emotional indicators in a database (e.g., emotional indicator database  214  of  FIG.  2 A ). For example, a neural network or convolutional neural network machine learning model (e.g., stored in local memory  412  of  FIG.  4    and/or remote server  401  of  FIG.  4   ) may be trained to accept as input two sets of facial feature characteristics (e.g., feature vectors representing the one or more captured images, and feature vectors typical of a user representing a particular emotion), and output a match probability (e.g., by identifying key features or patterns predictive of a match). Training data may include image pairs labeled as matches (e.g., by human reviewers). The match probability may be compared to a threshold value to determine whether there is a match between the two sets of facial characteristics. 
     At  710 , control circuitry  410  may determine whether the extracted or identified facial characteristics match a neutral emotional indicator (e.g., emotional indicator  237  of  FIG.  2 C , which may be stored in emotional indicator database  214 ). If the control circuitry determines there is a match (e.g., match  235  of  FIG.  2 C ) with the neutral emotional indicator, processing may move to  716 . If the control circuitry determines there is not a match with the neutral emotional indicator, processing may move to  712 . A neutral emotional indicator may correspond to facial characteristics that do not exhibit much emotion (e.g., a blank stare), if the user is looking away from the GUI (e.g., checking his or her mobile device), or exhibiting facial characterizes or body language that do not demonstrate one way or another that the user is interested or disinterested in the recommended media asset (e.g., media asset  207  of  FIG.  2 C ). 
     At  712 , control circuitry  410  may determine whether the extracted or identified facial characteristics match an interested emotional indicator (e.g., emotional indicator  226  of  FIG.  2 B  stored in emotional indicator database  214 ). If the control circuitry determines there is a match with the interested emotional indicator, processing may move to  716 . If the control circuitry determines there is not a match with the interested emotional indicator, processing may move to  714 . An interested emotional indicator may correspond to facial characteristics suggesting the user is happy (e.g., smiling and/or laughing) or excited, and/or movement (e.g., nodding his or her head) suggesting the user is interested in a particular recommended media asset (e.g., media asset  207  in  FIG.  2 A ). 
     At  714 , control circuitry  410  may determine that the user is not interested in the media asset (e.g., since the comparison in each of  710  and  712  may not have resulted in a match). A “not interested” emotional indicator (e.g., indicator  216  of  FIG.  2 A ) may correspond to facial characteristics suggesting the user is unhappy (e.g., frowning or upset) and/or movement (e.g., shaking his or her head), suggesting the user is not interested in a particular recommended media asset (e.g., media asset  207  in  FIG.  2 A ). In some embodiments, control circuitry  410  may determine a highest similarity score, computed based on comparisons to possible emotional states (e.g., interested, neutral, not interested), and the detected emotional indicator of the user may correspond to the highest similarity score. 
     At  716 , control circuitry  410  may perform a suitable action based on the detected emotional indicator. For example, if control circuitry  410  determines at  710  the user is neutral towards the recommended media asset (e.g., media asset  207  of  FIG.  2 C ), control circuitry  410  may perform one or more actions from among a variety of suitable actions related to a currently presented identifier of the recommended media asset (e.g., wait for the user to demonstrate a more meaningful emotion, generate for display an identifier for an updated media asset, refrain from taking action, etc.). If control circuitry  410  determines at  712  the user is interested in the recommended media asset (e.g., media asset  207  of  FIG.  2 B ), control circuitry  410  may perform one or more actions from among a variety of suitable actions related to the currently presented identifier of the recommended media asset (e.g., cause the recommended media asset to be presented to the user, add the media asset to a user playlist or watch list, remind the user to watch the media asset at a later time, present a preview of the media asset, etc.). If control circuitry  410  determines at  714  the user is not interested in the recommended media asset, control circuitry  410  may perform one or more actions from among a variety of suitable actions related to the currently presented identifier of the recommended media asset (e.g., generate for display an identifier for one or more updated media assets, in addition to or replacing the currently displayed identifier, modify user preferences to avoid a media asset with similar characteristics from being recommended to the user again, etc.). 
     In some embodiments, the detected emotional indicator, along with the retrieved user profile of the user, may be used in selecting an updated recommended media asset to present to the user. For example, if control circuitry  410  determines the emotional indicator suggests the user is sad or angry about the initial recommended media asset, an updated recommended media asset may be provided to improve the mood of the user (e.g., a comedy may be recommended to the user, and the user profile of the user may also be taken into account in selecting the particular comedy). In some embodiments, prior to presenting identifiers for recommended media assets, control circuitry  410  may determine an initial emotional indicator of the user (e.g., when the user accesses a media provider application or turns on the user equipment), and the initial media asset recommendation may be based on the initial emotional indicator of the user (e.g., if the user is in a happy mood, a recommendation for a comedy may be recommended; if control circuitry detects a user is accompanied by his or her significant other, a recommendation for romantic comedy may be presented; if a mother, father, daughter and son are detected, a recommendation for family-friendly content may be presented). 
       FIG.  8    depicts an illustrative flowchart of process  800  for providing media content recommendations that may be implemented by using systems  300  and  400 , in accordance with some embodiments of the disclosure. In various embodiments, the individual steps of process  800  may be implemented by one or more components of systems  300  and  400 . Although the present disclosure may describe certain steps of process  800  (and of other processes described herein) as being implemented by certain components of systems  300  and  400 , this is for purposes of illustration only, and it should be understood that other components of systems  300  and  400  may implement those steps instead. For example, the steps of process  800  may be executed by server  401  and/or by computing device  460  to provide content recommendations. 
     At  802 , control circuitry  410  may identify, by facial recognition, multiple users (e.g., users  202  and  204  of  FIG.  2 D ) in a vicinity of user equipment (e.g., user equipment  206  of  FIG.  2 D ). In some embodiments, at least one of such users may be identified by facial recognition. Facial recognition may be facilitated by capturing one or more images (e.g., images  240  and  242  of  FIG.  2 D ) of each user by way of a sensor (e.g., sensor  208  of  FIG.  2 D , which may be a camera), and performing image processing and facial recognition techniques on the captured images to extract and/or identify features of a face of the users. The extracted facial characteristics may be compared to facial characteristics of users in a database (e.g., biometric database  116  of  FIG.  1   ) to determine whether there is a match. In some embodiments, upon setting up a user profile, the user may be prompted to provide an image of himself or herself, in order to populate the database for subsequent matching, and control circuitry  410  may use such images to perform facial recognition of users. 
     Step  804  may be similar to step  702  described above in connection with  FIG.  7   , and may additionally include retrieving user profiles for each of the identified users, and presenting recommended content (e.g., initial recommended media asset  207 ) based on aggregate preferences of the users (e.g., a media asset having characteristics each of the users is likely to be interested in). 
     Step  806  may be similar to step  704  described above in connection with  FIG.  7   , and may additionally include capturing images of each of the multiple users (e.g., users  202  and  204  of  FIG.  2 D ) while presenting the identifier for the initial recommended media asset. Step  808  may be similar to step  706  described above in connection with  FIG.  7   , and may additionally include extracting or identifying facial characteristics of each of the multiple users from the captured images (e.g., images  240  and  242  of  FIG.  2 D ). Step  810  may be similar to step  708  described above in connection with  FIG.  7   , where the comparison to database records may additionally be performed for each user and respective images associated with each user. 
     At  812 , an aggregate emotional indicator of the multiple users during the user session may be detected. For example, the emotional indicator exhibited by a majority of users in the captured images may determine the aggregate emotional indicator of the group, or an average emotional indicator for the users in the captured images may determine the aggregate emotional indicator of the group. Additionally or alternatively, a priority user may be designated (e.g., the user holding a remote control for user equipment  206 , or the primary user associated with the particular account for the media provider), such that the emotional indicator of the priority user takes precedence in determining the aggregate emotional indicator. 
     Steps  814 - 820  of  FIG.  8    may be performed in a similar manner to steps  710 - 716 , respectively, of  FIG.  7   . In some embodiments, preferences of each of the detected users may be taken into consideration (e.g., in a case that performing the action related to the identifier for the initial media asset recommendation corresponds to generating for presentation an identifier for an updated media asset recommendation). Once a media asset is selected, user profiles of at least a subset of the users may be updated based on the selected media asset. In some embodiments, if a user of the plurality of users exits the vicinity of user equipment (e.g., user equipment  206 ), for at least a predetermined period of time (e.g., 10 seconds) the emotional indicator and user profile associated with that user may be disregarded in determining an action to be performed (e.g., the aggregate emotional indicator may be updated for the remaining group members). In some embodiments, if the content recommendation application determines a new user has entered the vicinity of the user equipment, the identify of such user may be detected (e.g., by sensor  208 ) and used in determining a new aggregate emotional indicator and the action to be performed related to the identifier of the currently presented media asset (e.g., media asset  207  of  FIG.  2 D ). 
     The processes discussed above are intended to be illustrative and not limiting. One skilled in the art would appreciate that the steps of the processes discussed herein may be omitted, modified, combined and/or rearranged, and any additional steps may be performed without departing from the scope of the invention. More generally, the above disclosure is meant to be exemplary and not limiting. Only the claims that follow are meant to set bounds as to what the present invention includes. Furthermore, it should be noted that the features and limitations described in any one embodiment may be applied to any other embodiment herein, and flowcharts or examples relating to one embodiment may be combined with any other embodiment in a suitable manner, done in different orders, or done in parallel. In addition, the systems and methods described herein may be performed in real time. It should also be noted that the systems and/or methods described above may be applied to, or used in accordance with, other systems and/or methods.