Patent Publication Number: US-2023164376-A1

Title: Evolutionary parameter optimization for selecting optimal personalized screen carousels

Description:
This application is a continuation of U.S. patent application Ser. No. 16/561,654, filed Sep. 5, 2019, which is hereby incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     The present disclosure relates to systems and methods for providing media content recommendations, and more particularly to systems and related processes for evolutionary optimization of selection of content categories for a carousel stack and the number of carousels in a stack. 
     SUMMARY 
     Media distribution systems often need to provide media content item recommendations. The recommendations are often provided as a stack of carousels, where each carousel displays identifiers of media content from a certain content category (comedy, action, thriller, etc.). In one approach, content categories are selected manually by a human editor. Further, the number of categories for the stack of carousels is also selected manually. However, such an approach will often lead to popular content failing to appear in the carousels (e.g., because the popular item happens to be in a different category from the categories that are in the stack). Additionally, a sub-optimal selection of the carousels and the number of carousels can lead to the same content excessively appearing in multiple carousels, taking up limited user interface space and negatively impacting user experience. 
     To address these shortcomings, systems and methods are described herein that leverage an evolutionary approach to selection of a subset of content categories (e.g., that are to be displayed as a stack of carousels) with an optimal number of categories and optimal selection of categories. 
     The content recommendation application begins by selecting a first (e.g., experimental) plurality of subsets of content categories from a large superset of all available categories. Each subset of content categories contains the same first number of categories (e.g., 40 categories). For each category, in the plurality of subsets, the content recommendation application selects appropriate recommended content items (e.g., for content category action—actions movies and TV shows may be selected). In some embodiments, the recommended content items are selected based on what content items are available for consumption currently, while in some implementations the recommended content items are selected based on what content items will be available in the future (e.g., in a day or in a week). 
     The content recommendation application may calculate a reward score for each subset of content categories. The reward score of a subset of categories may be based on combined popularity scores of the recommended items in each category and based on the number of duplicative content items in the subset of categories. After the initial selection, the content recommendation application modifies the first plurality of subsets of content categories, using an evolutionary technique, to maximize the reward scores. For example, the content recommendation application may readjust the first plurality of subsets of content categories by modifying the subsets by dropping the lowest-performing subsets of content categories, while modifying or combining (e.g., by crossing together) high-performing subsets of categories and reevaluating the reward scores. 
     When the reward scores of the first plurality of subsets of content categories stop improving, the content recommendation application may analyze the outcome. For example, if the reward scores are still below a certain threshold, the content recommendation application selects a second plurality of subsets of content categories, each subset of content categories comprising a second number of content categories, different from the first number (e.g., 30 categories). 
     The content recommendation application repeats the steps of selecting recommended content items, calculating a reward score, and modifying the second plurality of subsets of content categories using the evolutionary technique as described above with respect to the first plurality of subsets of content categories. In some embodiments, this process may be repeated for a third number of content categories, fourth number of content categories, etc. In some embodiments, the process may be repeated until the highest possible reward scores are obtained. In this way, the optimal carousels, and the optimal number of content carousels are selected for a stack of carousels for presentation on a user device. The technique allows for presentation of the most popular items without excessive duplication. 
     The content recommendation application may then generate for display identifiers for recommended content items of a subset of content categories of the second plurality of subsets of content categories that has the highest reward score. For example, the content recommendation application may generate these identifiers as part of an EPG (electronic programming guide) of a cable system (e.g., Cox™ or Verizon™), or as part of an OTT (over-the-top) media streaming application (e.g., Netflix™ or Amazon™). For example, the identifiers for recommended content items may be displayed as a stack of carousels, each carousel including identifiers of content of a certain category. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other objects and advantages of the 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    depicts an illustrative scenario for a content recommendation application providing media content recommendations, in accordance with some embodiments of the disclosure; 
         FIG.  2    shows an illustrative block diagram of a system hosting the media delivery application, in accordance with some embodiments of the disclosure; 
         FIG.  3    is an illustrative block diagram showing additional details of a system hosting the media delivery application, in accordance with some embodiments of the disclosure; 
         FIG.  4    depicts an illustrative flowchart of a process for providing media content recommendations, in accordance with some embodiments of the disclosure; 
         FIG.  5    depicts an illustrative flowchart of a process for maximizing reward scores, in accordance with some embodiments of the disclosure; and 
         FIG.  6    depicts an illustrative flowchart of a process for determining an optimal number of categories in a subset of content categories, in accordance with some embodiments of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIG.  1    depicts an illustrative scenario  100  for a content recommendation application providing media content recommendations. Media content may refer to any kind of video, audio, text, or multimedia content, or any combination thereof. For example, a media content item may be a TV show, a movie, a song, a podcast, a video game, any other type of media or any combination thereof. 
     In particular,  FIG.  1    shows the operation of a content recommendation application. In some embodiments, the content recommendation application may be executing on a server of an OTT content provider (e.g., Netflix, YouTube, Pandora) that is generating a recommendation screen for a particular use of the OTT system. In another example, the content recommendation application is executing on a media device of the user. In another example, the content recommendation application is executing on equipment of a cable company for providing an EPG. 
     The content recommendation application may access a superset  102  of content categories (e.g., kids, educational, mystery, etc.). It should be noted that while superset  102  depicts certain categories, any set of categories can be used. The content recommendation application may perform subroutine  104  to create multiple subsets of categories  106 - 110 , each having the same first size (e.g., each having three categories) by selecting categories from superset  102 . The first size may be of any size (e.g., each subset may include 10, 20, 35, 40, or 50 categories). 
     For example, subset  106  may include categories {educational, news, documentary}, subset  108  may include categories {educational, mystery, news}, and subset  110  may include categories {educational, mystery, comedy}. While three subsets are shown, any number of subsets may be created. 
     The content recommendation application may perform subroutine  112  to select recommended content items for each of subsets  106 - 110 . In some embodiments, the content recommendation application may use a recommendation engine to retrieve recommended content items (e.g., via an API call). The content recommendation application may use any of the recommendation engines and recommendation techniques as described in U.S. Pat. No. 6,388,714 and in U.S. Pat. No. 6,756,997 (both of which are hereby incorporated by reference herein in their entirety). In some embodiments, the recommended content items are selected based on content that is currently available. In some embodiments, the content recommendation application may also specify a future time period in its call to the recommendation engine. In this case, the recommended content items are selected based on content that will be available in the specified future time period (e.g., in 3 days). 
     For example, for subset  106 , the content recommendation application may select recommended content items  114 . As shown, the content recommendation application selected content items “TED Talk” and “How It&#39;s Made” for the educational category, content items “ABC NEWS” and “20/20” for news category, and content items “Tower” and “How It&#39;s Made” for documentary category. In another example, for subset  108 , the content recommendation application may select recommended content items  116 . As shown, the content recommendation application selected content items “TED talk” and “How It&#39;s Made” for the educational category, and content items “Dark Mirror” and “True Detective” for the mystery category, and content items “ABC NEWS” and “20/20” for the news category. Content items may similarly be selected for other subsets in addition to or instead of subsets  106 - 110 . 
     The content recommendation application may perform subroutine  117  to calculate a reward score for each of subsets  106 - 110 . For example, the scores may be based on the popularity of each content item selected for that subset and based on amount of duplication of content items between categories in the same subset. In some embodiments, the score may increase based on popularity, but decreases based on the incidence of duplication. In some embodiments, a target duplication rate may be established (e.g., 5% contents items being duplicated may be the desired duplication rate), and the reward score may be modified based on how close the duplication rate comes to the target rate. The popularity may be calculated based on expected or real viewership of each content item; for example, the viewership numbers may be added and normalized based on the number of content items selected for the category. In some embodiments, the reward score may be adjusted based on how many content items are selected for each category. For example, the reward scores can be decreased when less than a threshold number of content items (e.g., less than five) are selected for a category. 
     As shown, content items  116  received a reward score of 25, which is higher than the reward score of 12 calculated for content items  114 . For example, content items  116  may have received the higher score due to including more popular content items (e.g., “Dark Mirror”), while the content items  116  may have received a lower score due to including only lower-rated content items and the content item “How It&#39;s Made” being a duplicate that appears in both the educational and documentary categories. 
     The content recommendation application may perform subroutine  118  including using an evolutionary algorithm to modify subsets  106 - 110  to maximize the reward scores of subsets  106 - 110 . The sets may be modified, e.g., by discarding the lowest performing subsets (e.g., subsets with lowest reward score) and replacing them with an adjusted version of higher performing sets. The adjustment may be replacing one of the categories in a subset with another category. For example, subset  122  may be an adjusted version of subset  106  where the education category was replaced with action category. The adjustment may also be a combination of two high performing subsets, e.g., subset  120  may be a merged version of subsets  108  and  110  (e.g., where some categories for subset  120  are picked from subset  108  and some from subset  110 ). 
     As shown, the first subset of categories now includes subsets  120 - 122 , and the rewards score for the subsets increased to 35 and 42, respectively. The evolutionary modification may be repeated until reward scores stop improving (e.g., when scores of last three iterations are within 5% of each other). 
     The content recommendation application may perform subroutine  124  to check if the process of steps involving  106 - 118  resulted in a sufficiently high reward score (e.g., a score below 100). If not, the content recommendation application may repeat any of the steps involving  106 - 118 , but the subsets will include a different number of categories (e.g., four). For example, as shown, a second plurality of subsets of categories  126 - 130  may be selected, each subset having four categories. This second plurality of subsets may be modified using subroutines  112 ,  117 , and  118  to increase the reward score as much as possible. In some embodiments, the content recommendation application may repeat subroutine  124  multiple times until a reward score is achieved that is above a threshold. In some embodiments, application may repeat subroutine  124  multiple times and select the size of the subsets that resulted in the highest score. For example, the content recommendation application may try all suitable sizes of a subset (e.g., 2-50) and pick the one that works best. 
     The content recommendation application may perform subroutine  132  to display the identifiers of the content items of the highest scoring subset. As shown, subset  130 , which includes categories {Mystery; News; Comedy; and Action}, was identified as a subset that resulted in the highest rewards score. As shown, the identifiers of content items of subset  130  are displayed on user device  134  in a stack of carousels arrangement. For example, the user interface of device  134  shows four carousels, (one for each of the categories of subset  130 ). Each carousel includes identifiers for content items identified during subroutine  112  for subset  130 . That is, the carousel “Mystery” includes identifiers for “Dark Mirror” and “True Detective,” the carousel “News” includes identifiers for “ABC News” and “20/20,” the carousel “Comedy” includes identifiers for “Seinfeld” and “Friends,” and carousel “Action” includes identifiers for “24,” and “True Detective.” 
     The carousels may appear as stacked on top of each other. In some embodiments, the identifiers may be textual, or may include visual elements (e.g., promotion materials for the item, such as a poster or screenshot). When one of the identifiers is selected, the corresponding content item may be shown on the display of device  134  (e.g., via a streaming service or via a broadcast). 
       FIG.  2    shows an illustrative block diagram of a system  200  for displaying content based on event monitoring, in accordance with some embodiments of the disclosure. In various aspects, system  200  includes one or more of server  202 , media content source  204 , media guidance data source  206 , communication network  208 , and one or more computing devices  210 , such as user television equipment  210   a  (e.g., a set-top box), user computer equipment  210   b  (e.g., a laptop), and/or wireless user communications device  210   c  (e.g., a smartphone device). Although  FIG.  2    shows one of each component, in various examples, system  200  may include fewer than the illustrated components and/or multiples of one or more illustrated components. Communication network  208  may be any type of communication network, such as 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  208  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  208  communicatively couples various components of system  200  to one another. For instance, server  202  may be communicatively coupled to media content source  204 , media guidance data source  206 , and/or computing device  210  via communication network  208 . 
     In some examples, media content source  204  and media guidance data source  206  may be integrated as one device. Media content source  204  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  204  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  204  may include cable sources, satellite providers, on-demand providers, Internet providers, over-the-top content providers, or other providers of content. Media content source  204  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  210 . 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  204  and media guidance data source  206  may provide content and/or media guidance data to computing device  210  and/or server  202  using any suitable approach. In some embodiments, media guidance data source  206  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  206  may provide program schedule data and other guidance data to computing device  210  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  202  manages 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  204  to computing device  210  via communication network  208 . For instance, in some embodiments, content from media content source  204  and/or guidance data from media guidance data source  206  may be provided to computing device  210  using a client/server approach. In such examples, computing device  210  may pull content and/or media guidance data from server  202  and/or server  202  may push content and/or media guidance data to computing device  210 . In some embodiments, a client application residing on computing device  210  may initiate sessions with server  202 , media content source  204 , and/or media guidance data source  206  to obtain content and/or guidance data when needed, e.g., when the guidance data is out of date or when computing device  210  receives a request from the user to receive content or guidance data. In various aspects, server  202  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  210 . Additionally, although  FIG.  2    shows media content source  204  and media guidance data source  206  as separate from server  202 , in some embodiments, media content source  204  and/or media guidance data source  206  may be integrated as one device with server  202 . 
     Content and/or media guidance data delivered to computing device  210  may be over-the-top (OTT) content. OTT content delivery allows Internet-enabled user devices, such as computing device  210 , 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  210 . 
       FIG.  3    is an illustrative block diagram showing additional details of the system  300  (which may be the same as system  200  of  FIG.  2   ), in accordance with some embodiments of the disclosure. In particular, server  301  (e.g., the same server as server  202 ) includes control circuitry  302  and I/O path  308 , and control circuitry  302  includes storage  304  and processing circuitry  306 . Computing device  360  (e.g., one or more of devices  210   a ,  210 , and  210   c ) includes control circuitry  310 , I/O path  316 , speaker  318 , display  320 , and user input interface  322 . Control circuitry  310  includes storage  312  and processing circuitry  314 . Control circuitry  302  and/or  310  may be based on any suitable processing circuitry such as processing circuitry  306  and/or  314 . 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  304 , storage  312 , and/or storages of other components of system  300  (e.g., storages of media content source  354 , media guidance data source  356 , and/or the like) may be an electronic storage device. In some embodiments, media content source  354  may be the same as media content source  204 . In some embodiments, media guidance data source  356  may be the same as media content source  206 . 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  304 , storage  312 , and/or storages of other components of system  300  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  304 ,  312  or instead of storages  304 ,  312 . In some embodiments, control circuitry  302  and/or  310  executes instructions for a content recommendation application stored in memory (e.g., storage  304  and/or  312 ). Specifically, control circuitry  302  and/or  310  may be instructed by the content recommendation application to perform the functions discussed herein. In some implementations, any action performed by control circuitry  302  and/or  310  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  304  and/or  312  and executed by control circuitry  302  and/or  310 . 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  360 , and a server content recommendation application resides on server  301 . 
     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  360 . In such an approach, instructions for the content recommendation application are stored locally (e.g., in storage  312 ), 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  310  may retrieve instructions for the content recommendation application from storage  312  and process the instructions to perform the functionality described herein. Based on the processed instructions, control circuitry  310  may determine what action to perform when input is received from user input interface  322 . 
     In client/server-based embodiments, control circuitry  310  may include communication circuitry suitable for communicating with a content recommendation application server (e.g., server  301 ) 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  358 ). In some embodiments, communication network  358  may be the same as network  208 . In another example of a client/server-based application, control circuitry  310  runs a web browser that interprets web pages provided by a remote server (e.g., server  301 ). 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  302 ) and generate the displays discussed above and below. Computing device  360  may receive the displays generated by the remote server and may display the content of the displays locally via display  320 . This way, the processing of the instructions is performed remotely (e.g., by server  301 ) while the resulting displays, such as the display windows described elsewhere herein, are provided locally on computing device  360 . For example, computing device  360  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  360  may receive inputs from the user via input interface  322  and transmit those inputs to the remote server for processing and generating the corresponding displays. 
     A user may send instructions to control circuitry  302  and/or  310  using user input interface  322 . User input interface  322  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  322  may be integrated with or combined with display  320 , which may be a monitor, television, liquid crystal display (LCD), electronic ink display, or any other equipment suitable for displaying visual images. 
     Server  301  and computing device  360  may receive content and data via input/output (hereinafter “I/O”) path  308  and  316 , respectively. For instance, I/O path  316  may include circuitry that includes one or more of communication port configured to receive a live content stream from server  301  and/or media content source  354  via a communication network  358 . Storage  312  may be configured to buffer the received live content stream for playback, and display  320  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  308 ,  316  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  302 ,  310 . Control circuitry  302 ,  310  may be used to send and receive commands, requests, and other suitable data using I/O paths  308 ,  316 . I/O paths  308 ,  316  may connect control circuitry  302 ,  310  (and specifically processing circuitry  306 ,  314 ) 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.  3    to avoid overcomplicating the drawing. 
     Having described systems  200  and  300 , reference is now made to  FIG.  4   , which depicts an illustrative flowchart of process  400  for providing media content recommendations that may be implemented by using systems  200  and  300 , in accordance with some embodiments of the disclosure. In various embodiments, the individual steps of process  400  may be implemented by one or more components of systems  200  and  300 . Although the present disclosure may describe certain steps of process  400  (and of other processes described herein) as being implemented by certain components of systems  200  and  300 , this is for purposes of illustration only, and it should be understood that other components of systems  200  and  300  may implement those steps instead. For example, steps of process  400  may be executed by server  303  and/or by computing device  360  to provide content recommendations. 
     At step  402 , control circuitry  310  selects a first plurality (e.g.,  10 ) of subsets of content categories, each subset of content categories comprising a first number of content categories (e.g.,  40 ). In some embodiments, the subsets are initially selected at random from a set of available categories stored at media guidance data sources  206 . In some embodiments, the most popular categories overall are selected. 
     At step  404 , control circuitry  310  selects recommended content items for each category that is found in at least one of the selected subsets of categories. For example, control circuitry  310  may issue an API call to media guidance data sources  206  via network  208  to provide recommended content items for each content category. In some embodiments, the content items are selected based on current availability. In some embodiments, the content items are selected based on future availability (e.g., wherein the control circuitry  310  specifies the time period). For example, control circuitry  310  may request content recommendation for tomorrow, or for next week, or for any other future time period. In some embodiments, the recommended content items are selected for an individual user. In some embodiments, the recommended content items are selected for a group of users (e.g., a group of users that share a demographic, e.g., all male users aged 18-47). 
     At  406 , control circuitry  310  may calculate a reward score for each subset of the first plurality of subsets. The reward score may be computed based on the popularity score of the content items and a number of duplicative content items in each respective subset of content categories. For example, the reward score may be increased for a respective subset proportionally to the popularity score of each content item selected for that subset. For example, the expected or recorded viewership of each content may be summed and normalized based on the total number of unique content items. In some embodiments, other factors may be used to compute the reward score (e.g., a low number of items selected for a category may lower a reward score of the subset that includes that category). 
     In some embodiments, control circuitry  310  may modify the reward score of the respective subset in reverse proportion to the amount of duplicative content items in the respective subset. For example, for a respective subset, control circuitry  310  may calculate the percentage or fraction of content items that were selected for more than one content category of that subset. The reward score may be scaled down proportionally to the percentage or fraction. For example, if 10% of the content items are non-unique, the reward score may be lowered by 10%. In some embodiments, the reward score may be scaled based on how closely the percentage or fraction matches the predetermined target percentage or fraction of duplication. For example, if the desired amount of duplication is 5%, the observed duplication rate of 7% may be penalized similarly to an observed duplication rate of 3%, because both are 2% away from the target. 
     At  408 , control circuitry  310  may modify the first plurality of subsets using an evolutionary technique to maximize the reward score. For example, subsets can be iteratively modified with the goal of achieving a reward score increase. An exemplary evolutionary technique is described below in relation to  FIG.  5   . 
     At  410 , control circuitry  310  may check if the reward scores have stopped improving due to operations of the evolutionary technique. For example, while step  410  is being performed, control circuitry  310  may maintain a historical high rewards (e.g., the value of the highest reward score for a subset is stored in storage  312 ) for each iteration cycle. After each repetition of steps  410 , control circuitry  310  may compare all historical high evaluation metrics of previous cycles. For example, the last five cycles may have resulted in historical high evaluation rewards scores of {40, 50, 55, 55, 55}. Because last three historical high rewards scores ({55, 55, 55}) are not increasing, control circuitry  310  may determine that rewards scores has stopped improving. In some embodiments, improvements that are lower than a certain margin (e.g., less than 1%) may be treated the same as historical high evaluation metrics that has stopped increasing. For example, the last five cycles may have resulted in historical high rewards scores {40, 50, 55, 56, 57}. Because the last three historical high rewards scores ({55, 56, 57}) are not increasing by a sufficiently high margin, control circuitry  310  may determine that rewards scores have stopped improving. 
     At  412 , control circuitry  310  may further evaluate the first plurality of content categories that has been subject to the evolutionary technique at step  410 . For example, control circuitry  310  may compare the reward score of the highest scoring subset of categories. In some embodiments, circuitry  310  may compare the reward score of the highest scoring subset of categories to a predetermined or dynamic threshold. In some embodiments, the threshold may be selected based on steps  402 - 410  performed for subsets with a different number of categories (e.g., as will be explained in relation to  FIG.  6   ). 
     At  414 , control circuitry  310  may determine if the reward score is sufficiently high (e.g., by checking if the reward score is above or below a threshold). Control circuitry  310  may proceed to  420  if the reward score is sufficiently high (e.g., the reward score is above the threshold). Otherwise, control circuitry  310  proceeds to step  416 . At  416 , control circuitry  310  selects a different number of categories than was previously used in step  402 . For example, if subsets in step  402  had 40 content categories, control circuitry  310  may select the number “30.” In some embodiments, all numbers that were tried are stored, and control circuitry  310  selects a new number not previously used. Then, control circuitry  310  selects a second plurality (e.g.,  10 ) of subsets of content categories, each subset of content categories comprising a second number of content categories (e.g.,  30 ). 
     At  418 , control circuitry  310  may repeat steps  404 - 414  for the second plurality of subsets. Eventually, control circuitry  310  will identify an optimal number of the content categories for each subset and the evaluation at step  414  will succeed. In that case, as described above, control circuitry  310  will proceed to step  420 . 
     Control circuitry  310  may, at step  420 , generate for display identifiers based on the final plurality of subsets of content categories. For example, control circuitry  310  may generate for display identifiers of for recommended content items of a subset of content categories of the second plurality of subsets of content categories that has the highest reward score. For example, the identifiers of recommended content items may be generated for display on display  320  by server  301  or media content source  354 . In some embodiments, the user may select content for consumption by interacting with the identifiers (for example, by clicking on the identifiers). Identifiers may be simple text identifiers, or graphical identifiers that include cover art and/or other data (release date, actors list, etc.). In some embodiments, the final set of recommended content items may be used for other purposes. For example, content that is predicted to be requested by a large demographic may be placed in edge distribution servers for faster delivery based on predicted demand for some content items. 
       FIG.  5    depicts an illustrative flowchart of process  500  for modifying a plurality of subsets that may be implemented by using systems  200  and  300 , in accordance with some embodiments of the disclosure. In various embodiments, individual steps of process  500  may be implemented by one or more components of systems  200  and  300 . 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  200  and  300 , this is for purposes of illustration only, and it should be understood that other components of systems  200  and  300  may implement those steps instead. For example, steps of process  500  may be executed by server  202  and/or by computing device  360  to provide content recommendations. In some embodiments, control circuitry  310  may use process  500  as part of process  400  of  FIG.  4    (e.g., in addition to or instead of steps  410 - 412 ). 
     At step  502 , control circuitry  310  may select a first plurality of subsets of content categories, each subset of content categories comprising a first number of content categories (e.g., as described in step  402 ). In some embodiments, this may be done at random. Any number of subsets of content categories may be generated. At step  504 , a set of media content items is selected for each content category. For example, step  504  may result in generation of subset  1  at step  506 , subset  2  at step  508 , and subset  3 , at step  510 . While only three subsets are shown, any number of subsets may be generated. For example, subsets may be generated as described with relation to subsets  106 - 110  in  FIG.  1   . 
     At step  512 , control circuitry  310  calculates reward scores for each subset generated in steps  506 - 510 . For example, the method described above in step  406  may be used to calculate reward scores. 
     Steps  514 - 524  may be performed for each subset (e.g., for subsets  106 ,  108 , and  110  of  FIG.  1   ). At  514 , control circuitry  310  may check if the reward score for a particular subset is low (e.g., low compared to other scores). For example, control circuitry  310  may select only the subsets that scored the lowest for replacement. In some embodiments, control circuitry  310  may select all subsets that scored below a threshold for replacement. If the subsets&#39; score is not low, then, at step  518 , control circuitry  310  may keep that subset in storage  312 . At  516 , subsets with a low reward score (e.g., the lowest reward score) may be eliminated. For example, at  520 , control circuitry  310  may completely delete the lowest scoring subset from storage  312 . Additionally, at  518 , control circuitry  310  may generate a replacement subset in any one of the ways described at step  522  or step  524  below. 
     For example, at  522 , control circuitry  310  may generate a new subset by mutating one of the other subsets. For example, control circuitry  310  may construct new modified subset  2  by removing one or more of the content categories from subset  2  and replacing it or them with a new category or categories (e.g., as shown in  FIG.  1    in element  118 ). At  524 , control circuitry  310  may generate a new subset by merging two other subsets (e.g., as shown in  FIG.  1   ). For example, to construct a modified subset  3 , control circuitry  310  may combine some categories from subset  2  and from subset  3 . 
     At  526 , control circuitry  310  may repeat steps  502 - 524  for newly replaced subsets until reward scores stop improving (e.g., if the historically high reward scores stagnate over a certain number of cycles). 
       FIG.  6    depicts an illustrative flowchart of process  600  for determining an optimal number of categories in a subset of content categories that may be implemented by using systems  200  and  300 , 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  200  and  300 . 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  200  and  300 , this is for purposes of illustration only, and it should be understood that other components of systems  200  and  300  may implement those steps instead. For example, the steps of process  600  may be executed by server  302  and/or by computing device  360  to provide content recommendations. In some embodiments, control circuitry  310  may use process  600  as a part of process  400  of  FIG.  4    (e.g., in addition to or instead of steps  414 - 418 ). 
     At  602 , control circuitry  310  may select a first plurality of subsets of content categories, each subset of content categories comprising a first number of content categories (e.g., as described in step  402 ), each category having a first number (e.g., N) categories. At  604 , control circuitry  310  may modify the first plurality of subsets to maximize the reward scores using an evolutionary technique (e.g., as described in  FIG.  5   ). 
     After the rewards scores are maximized for value N, control circuitry  310  checks if a sufficiently high reward score is achieved at step  606 . For example, the reward score may be compared to a pre-set threshold. In another example, control circuitry  310  may keep the highest reward score obtained for different values of N. In this case, the current highest reward score of a subset would need to exceed the historically high value of the rewards score for all values of N. If the reward score is low, control circuitry  310  proceeds to step  608 , where the value of N is modified, and steps  602 - 604  are repeated for subsets with N categories (where the value of N is new). 
     When the reward score is sufficiently high, process  600  ends at  610 . In some embodiments, the value of N that led to step  610  may be used by control circuitry  310  to generate the number of carousels to be displayed in a stack of carousels to be displayed on a user device (e.g., as shown in device  134  of  FIG.  1   , or on display  320 ). 
     The systems and processes discussed above are intended to be illustrative and not limiting. One skilled in the art would appreciate that the actions of the processes discussed herein may be omitted, modified, combined, and/or rearranged, and any additional actions 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 disclosure 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.