Systems and methods for providing media content recommendations

Systems and associated methods are described for providing content recommendations. The system accesses a plurality of recommendation algorithms and assigns a plurality of weight values to each prediction algorithm. Then, the system generates a set of candidate weight combinations, such that each candidate combination includes a weight value assigned to each prediction algorithm. Then requests for content items are received over a predetermined period of time. For each combination, the system generates a set of recommended content items and an evaluation metric that is based on matches with requests. Afterwards, the system replaces a candidate combination that resulted in a generation of a lowest evaluation metric. The aforementioned steps are repeated until the evaluation metrics stop improving. Then display identifiers are displayed for a set of recommended content items generated for a candidate combination with the highest evaluation metric.

BACKGROUND

The present disclosure relates to systems and methods for providing media content recommendations, and more particularly to systems and related processes for arbitrating between multiple content recommendation algorithms.

SUMMARY

Modern media distribution systems often need to provide media content item recommendations and predictions. Many different algorithms and techniques may be used to select media content items that are relevant to a user (or to certain user demographics) to maximize the probability of displaying recommendations that are relevant (e.g., by presenting identifiers of media content items that the user is likely to watch). For example, a media distribution system may recommend media content based on content previously watched by the user, partially watched episodes, popularity of content among many users, or a variety of other prediction algorithms.

Media distribution systems are commonly unable to select an optimal algorithm among many available choices. In one approach, the algorithms are selected by a human curator on an ad-hoc basis. For example, a human administrator may manually select media content to be recommended to a specific user demographic. For example, a human curator may choose to present some results based on a “previously watched content” prediction algorithm and some results based on a “popularity” prediction algorithm. However, such a manual approach to algorithm use is inherently inefficient and time-consuming. It is unlikely that a human administrator would be able to select an optimal or near-optimal combination of algorithms that would result in an effective list of media content recommendations.

To address these shortcomings, systems and methods are described herein that leverage differential evolution techniques to select an optimal or near-optimal combination of recommendation algorithms for selecting recommended content. For example, a content recommendation application may use an evolutionary weight selection approach to select media content items that a user is most likely to watch (predicted media content) and generate for display identifiers of those content items by iteratively adjusting weights for a combination of recommendation algorithms. A content recommendation application may have access to several recommendation algorithms (e.g., content request prediction algorithms). Initially, the content recommendation application may assign some weight values to each prediction algorithm. For example, initial values may be selected randomly, or based on predetermined heuristics (e.g., algorithms used commonly in the past may be weighted more highly), or by using a specified algorithm. Then the weights are iteratively adjusted based on empirical data until an optimal or near-optimal combination of algorithms is achieved.

For example, the content recommendation application may select several weight combinations for empirical testing. For example, each candidate weight combination may have a weight value assigned to each prediction algorithm. The content recommendation application may then generate a set of recommended content items for each weight combination, using the results generated by each prediction algorithm and weight values of that candidate combination. Next, the content recommendation application may monitor requests for content items over a certain period of time (e.g., a day, or a week). For example, the content recommendation application may track requests from a single user or from several (e.g., 3000) active users in the same demographical category.

The matches between requests and predictions generated by each candidate combination are scored to calculate an evaluation metric for each candidate combination. The evaluation metric is then used to adjust weight combinations, remove weight combinations, and/or generate new weight combinations. For example, new weight combinations are generated using differential evolution techniques to attempt to maximize the calculated evaluation metrics. For example, the content recommendation application may remove the lowest scoring candidate weight combination and replace it with a new weight combination. In one implementation, the new weight combination is generated using a mutation of a more successful candidate weight combination. In another example, the new weight combination is generated using a crossover of two or more other more successful candidate weight combinations.

The content recommendation application may then repeat the steps of generating candidate weight combinations and empirically evaluating the results until the evaluation metrics stop improving compared to previous cycles. For example, the repetition may cease if the improvement in the evaluation metrics is less than 1% over a certain number of cycles (e.g., over three cycles).

Once the best weight combination is determined, the content recommendation application uses that weight combination to acquire optimized media content recommendations and generate for display identifiers for these content items. 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™).

DETAILED DESCRIPTION

FIG. 1depicts an illustrative scenario 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.

At step102, the content recommendation application may access several recommendation algorithms (e.g., algorithms110,112, and114). For example, algorithm110may provide a list of content items that are predicted to be watched by a user (or a certain demographic of users) based on TV series that were previously watched (for example, the user may be predicted to request content items that are similar to series that the user has watched in the past). Algorithm112may provide a list of content items that are predicted to be watched by a user (or a certain demographic of users) based on TV episodes that were partially watched in the past (for example, the user may be predicted to request content items that the user has watched, but did not finish watching). Algorithm114may provide a list of content items that are predicted to be watched by a user (or a certain demographic of users) based on general popularity of certain content items. In some embodiments, the recommendation algorithms may be available locally or accessible via an API (Application Program Interface) call. Each of the algorithms may be based on more granular factors that are also weighted. For example, algorithm114may weigh the popularity of a content item with a matching demographic more highly than its popularity with other demographics.

The content recommendation application may the generate several candidate weight combinations104,106,108. For example, to create combination104, the content recommendation application assigns weight 0.5 to algorithm110, weight 0 to algorithm112, and weight 0.3 to algorithm114. Similarly, to create combination106, the content recommendation application assigns weight 0 to algorithm110, weight 0.7 to algorithm112, and weight 0.8 to algorithm114. Further, to create combination108, the content recommendation application assigns weight 0.3 to algorithm110, weight 0.3 to algorithm112, and weight 0.3 to algorithm114. These weights may initially be selected at random, using a heuristic, or via human input. The weight values may be similar to parameter value used in other known differential evolution techniques. The content recommendation application may use any of the recommendations engines and recommendations 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). The content recommendation application may also use any of the predictive engines or techniques as described in U.S. Pat. No. 8,613,024 and in U.S. Patent Application Publication No. 2018/0189826 (both of which are hereby incorporated by reference herein in their entirety).

At step116, the content recommendation application may generate sets of predicted media content items based on each of candidate weight combinations104,106,108. For example, each of the algorithms may generate its own preliminary list of media content items, and then weights of each combination may be used to pick content items for each candidate weight combination (e.g., as explained in more detail inFIGS. 6 and 7).

For example, candidate combination104may result in a set of predicted content items118that includes a “Game of Thrones” episode and a “20/20” episode. Candidate combination106may result in a set of predicated content items120that includes a “Today” episode and the movie “Spellbound”. Candidate combination108may result in a set of predicated content items122that includes a “Game of Thrones” episode and a “Today” episode.

At124, the content recommendation application may track requests by a user or a set of users. For example, requests by a single user can be tracked for a week. In some embodiments, a group of users (e.g., 1000 users) can be selected for tracking, where the group includes active users (i.e., those who consume media content often) and who belong to the same demographic category (e.g., 18-45 age group). In the case of group tracking, a shorter time period may be used (e.g., one day). The matches between requested content items and predictions118,120,122are scored to create an evaluation metric for each candidate combination. For example, the content recommendation application may assign higher evaluation metric scores to weight combinations that resulted in the most matches with requested content.

Candidate weight combination104may have resulted126in a score of 50 (e.g., because its predictions were requested 50 times by users over the tracking period). Candidate weight combination106, may have resulted128in a score of 25 (e.g., because its predictions were requested 25 times by users over the tracking period). Candidate weight combination108may have resulted130in score of 0 (e.g., because its predictions were not requested by users over the tracking period).

At132, the content recommendation application may decide to keep or replace some of the candidate weight combinations. For example, the content recommendation application may replace candidate weight combinations that resulted in evaluation metrics that are lower than a threshold. In some embodiments, the content recommendation application may replace candidate weight combinations that resulted in the lowest evaluation metric. In some embodiments, the content recommendation application may use a differential evolutionary algorithm to select which candidate weight combinations should be replaced in order to optimize the evaluation metric. For example, weight combination104may be kept, while weight combinations106and108may be replaced. The replacement weight combination may be generated using differential evolutionary techniques. For example, non-promising candidate weight combinations may be replaced with mutated versions of promising candidate weight combinations or with crossovers of promising candidate weight combinations. The differential evolution method may refer to any method or algorithm that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Such methods are commonly known as metaheuristics as they make few or no assumptions about the problem being optimized and can search very large space of candidate solutions. Use of the differential evolution method with a measure of quality being determined empirically to optimize weights of algorithms allows for detection of optimal or near optimal selection of different algorithms to create the most relevant set of recommendations.

At134, steps116-132may be repeated until evaluation metrics stop improving (e.g., in accordance with iterative differential evolution methods). For example, when evaluation metrics improve by less than 1% over several cycles, the content recommendation application may stop the repetition of steps116-132. At136, the content recommendation application may use the candidate weight combinations that resulted in the highest evaluation to select a final set of predicted media content items. Identifiers of those content items may then be generated for display. For example, whenever a member of the demographic group that was monitored in step124opens an EPG or front screen of an OTT service, the content recommendation application may generate for display selectable identifiers of the final set of recommended media content items.

FIG. 2shows an illustrative block diagram of a system200for displaying content based on event monitoring, in accordance with some embodiments of the disclosure. In various aspects, system200includes one or more of server202, media content source204, media guidance data source206, communication network208, and one or more computing devices210, such as user television equipment210a(e.g., a set-top box), user computer equipment210b(e.g., a laptop), and/or wireless user communications device210c(e.g., a smartphone device). AlthoughFIG. 2shows one of each component, in various examples, system200may include fewer than the illustrated components and/or multiples of one or more illustrated components. Communication network208may 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 network208includes 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 network208communicatively couples various components of system200to one another. For instance, server202may be communicatively coupled to media content source204, media guidance data source206, and/or computing device210via communication network208.

In some examples, media content source204and media guidance data source206may be integrated as one device. Media content source204may 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 source204may 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 source204may include cable sources, satellite providers, on-demand providers, Internet providers, over-the-top content providers, or other providers of content. Media content source204may 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 device210. 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 source204and media guidance data source206may provide content and/or media guidance data to computing device210and/or server202using any suitable approach. In some embodiments, media guidance data source206may 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 source206may provide program schedule data and other guidance data to computing device210on 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, server202manages 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 source204to computing device210via communication network208. For instance, in some embodiments, content from media content source204and/or guidance data from media guidance data source206may be provided to computing device210using a client/server approach. In such examples, computing device210may pull content and/or media guidance data from server202and/or server202may push content and/or media guidance data to computing device210. In some embodiments, a client application residing on computing device210may initiate sessions with server202, media content source204, and/or media guidance data source206to obtain content and/or guidance data when needed, e.g., when the guidance data is out of date or when computing device210receives a request from the user to receive content or guidance data. In various aspects, server202may 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 device210. Additionally, althoughFIG. 2shows media content source204and media guidance data source706as separate from server202, in some embodiments, media content source204and/or media guidance data source206may be integrated as one device with server202.

Content and/or media guidance data delivered to computing device210may be over-the-top (OTT) content. OTT content delivery allows Internet-enabled user devices, such as computing device210, 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 device210.

FIG. 3is an illustrative block diagram showing additional details of the system300(which may be the same as system200ofFIG. 7), in accordance with some embodiments of the disclosure. In particular, server301(e.g., the same server as server202) includes control circuitry302and I/O path308, and control circuitry302includes storage304and processing circuitry306. Computing device360(e.g., one or more of devices210a,210, and210c) includes control circuitry310, I/O path316, speaker318, display320, and user input interface322. Control circuitry310includes storage312and processing circuitry314. Control circuitry302and/or310may be based on any suitable processing circuitry such as processing circuitry306and/or314. 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 storage304, storage312, and/or storages of other components of system300(e.g., storages of media content source354, media guidance data source356, and/or the like) may be an electronic storage device. In some embodiments, media content source354may be the same as media content source204. In some embodiments, media guidance data source356may be the same as media content source206. 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 storage304, storage312, and/or storages of other components of system300may 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 storages304,312or instead of storages304,312. In some embodiments, control circuitry302and/or310executes instructions for a content recommendation application stored in memory (e.g., storage304and/or312). Specifically, control circuitry302and/or310may be instructed by the content recommendation application to perform the functions discussed herein. In some implementations, any action performed by control circuitry302and/or310may 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 storage304and/or312and executed by control circuitry302and/or310. 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 device360, and a server content recommendation application resides on server301.

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 device360. In such an approach, instructions for the content recommendation application are stored locally (e.g., in storage312), 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 circuitry310may retrieve instructions for the content recommendation application from storage312and process the instructions to perform the functionality described herein. Based on the processed instructions, control circuitry310may determine what action to perform when input is received from user input interface322.

In client/server-based embodiments, control circuitry310may include communication circuitry suitable for communicating with a content recommendation application server (e.g., server301) 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 network358). In some embodiments, communication network358may be the same as network208. In another example of a client/server-based application, control circuitry310runs a web browser that interprets web pages provided by a remote server (e.g., server301). 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 circuitry302) and generate the displays discussed above and below. Computing device360may receive the displays generated by the remote server and may display the content of the displays locally via display320. This way, the processing of the instructions is performed remotely (e.g., by server301) while the resulting displays, such as the display windows described elsewhere herein, are provided locally on computing device360. For example, computing device360may include display circuitry (e.g., video card circuitry or combination motherboard and video card circuitry) configured to generate for display the display windows. Computing device360may receive inputs from the user via input interface322and transmit those inputs to the remote server for processing and generating the corresponding displays.

A user may send instructions to control circuitry302and/or310using user input interface322. User input interface322may 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 interface322may be integrated with or combined with display320, which may be a monitor, television, liquid crystal display (LCD), electronic ink display, or any other equipment suitable for displaying visual images.

Server301and computing device360may receive content and data via input/output (hereinafter “I/O”) path308and316, respectively. For instance, I/O path316may include circuitry that includes one or more of communication port configured to receive a live content stream from server301and/or media content source354via a communication network358. Storage312may be configured to buffer the received live content stream for playback, and display320may 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 paths308,316may 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 circuitry302,310. Control circuitry302,310may be used to send and receive commands, requests, and other suitable data using I/O paths308,316. I/O paths308,316may connect control circuitry302,310(and specifically processing circuitry306,314) 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 inFIG. 3to avoid overcomplicating the drawing.

Having described systems200and300, reference is now made toFIG. 4, which depicts an illustrative flowchart of process400for providing media content recommendations that may be implemented by using systems200and300, in accordance with some embodiments of the disclosure. In various embodiments, the individual steps of process400may be implemented by one or more components of systems200and300. Although the present disclosure may describe certain steps of process400(and of other processes described herein) as being implemented by certain components of systems200and300, this is for purposes of illustration only, and it should be understood that other components of systems200and300may implement those steps instead. For example, steps of process400may be executed by server302and/or by computing device360to provide content recommendations.

At step402, control circuitry310accesses a plurality of recommendation algorithms (e.g., content request prediction algorithms). In some embodiments, control circuitry310may access the content request prediction algorithms via an API from server302, or from media content guidance data source356. For example, control circuitry310may provide input for the algorithms and receive results via communications network358. In another embodiment, control circuitry310may store the algorithm locally in storage312. In some embodiments, a wide range of content request prediction algorithms may be accessed.

For example, control circuitry310may use a recommendation algorithm, e.g., 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). Control circuitry310may also use an algorithm configured to predict which tv series a user will consume at a given time of day based on that user's previous viewing behavior (e.g., series prediction algorithm). Control circuitry310may also use an algorithm configured to predict what type of an object (e.g., one of a Movie object, a Series object, an Episode object, a Program object, and a Sports Event object) a user will consume at a given time of day based on that user's previous viewing behavior. Control circuitry310may also use an algorithm configured to predicts which genere of TV series a user will consume at a given time of day based on that user's previous viewing behavior. Control circuitry310may also use an algorithm configured to predicts which content a user will consume based on popularity rankings of content with other users. Control circuitry310may also use an algorithm configured to predicts which content a user will consume based on characteristics of content that user put in a “favorites” list. Control circuitry310may also use an algorithm configured to predicts which content a user will consume based on what series the user has recently been watching a lot of (e.g., binging on). Control circuitry310may also use an algorithm configured to predicts which content a user will consume based on what content the user has finished watching. Control circuitry310may also use an algorithm configured to predicts which content a user will consume based on how old or new the content is. Control circuitry310may also use an algorithm configured to predicts which content a user will consume based on explicit feedback received from the user (e.g., thumbs up or down rating or numerical rating). Control circuitry310may also use an algorithm configured to predicts which content a user will consume based on the amount of interest expressed by other users in a sporting event. Control circuitry310may also use an algorithm configured to predicts which content a user will consume based on sematic similarity of content to previously consumed content.

Control circuitry310may send a user name as input to the algorithm. In some embodiments, control circuitry310may send user historical requests or behavior data as input to the algorithm. In yet another embodiment, control circuitry310may send demographics selection (e.g., selection of people aged 18-45, or selection of people aged 3-6). Each of the algorithms may use the input data to generate a set of predicted media content items (e.g., a list of movies and TV shows).

At step404, control circuitry310may assign a plurality of weight values to each prediction algorithm. For example, control circuitry310may assign weight values randomly, by applying heuristics (e.g., which algorithms were used most often in the past). For example, initial weight values (e.g., weights104,106,108) may be created as shown in exemplary example ofFIG. 1. In some embodiments, control circuitry310may calculate initial weight value for a prediction algorithm by dividing a constant (e.g., “3”) by an average of top 10 scores of that prediction algorithm, and adding a random number (e.g., a random number in a range between −0.5 and 0.5). Any other way of generating initial weight values may also be used.

At404, control circuitry310may generate or select a set of candidate weight combinations. For example, for each candidate weight combination, each one of the algorithms may be assigned a weight value generated in step404. In some embodiments, the initial combination may be random. However, subsequent candidate weight combinations may be generated in accordance with an iterative differential evolution algorithm, as will be explained below.

At406, control circuitry310may generate a set of recommended content items based on the plurality of the prediction algorithms and weight values of each particular candidate combination. For example, each algorithm may be used to generate its own list of preliminary recommended (e.g., predicted) content items. Then the preliminary predicted content items are weighted using the weights of the particular candidate combination, and, at step408, the final selection of set of media content items is made for each candidate combination. An exemplary embodiment of such a technique is explained in more detail inFIGS. 6 and 7.

At step410, control circuitry310monitors user requests (e.g., requests to watch or to store a media content item). For example, control circuitry310may track received requests for content items over a predetermined period of time. For example, control circuitry310may track requests by a single user, or by a group of users (e.g.,2000particularly active users in a selected demographic category). For example, control circuitry310may track requests for media content received at media content sources354, or at server301via communications network358.

Steps412-416may be performed for each candidate weight combination. For example, for a particular candidate combination, at step412, control circuitry310may generate an evaluation metric based on a match between the requests for content items and the set of recommended content items generated for the particular candidate combination. For example, if the set of recommended content items matched a larger number of requests, it may receive a high evaluation metric (e.g., a metric that is proportional to the number of matches). In another example, if the set of recommended content items matched a small number of requests, it may receive a low evaluation metric. An exemplary embodiment of such a calculation is explained in more detail in toFIGS. 6 and 7.

At414, control circuitry310may search for the candidate combination that generated the lowest evaluation metric. Such a candidate combination may be replaced at step418. At step416, candidate combinations that generated evaluation metrics that are not the lowest may be kept. In some embodiments, instead of replacing only the candidate combination which generated the lowest evaluation metric, all candidate combination that performed poorly may be replaced. For example, the bottom 50% of the candidate combinations may be replaced. In some embodiments, a pre-set or dynamic threshold may be used to sort candidate combinations. In some embodiments, candidate combinations to be replaced are determined using differential evolution techniques.

In some embodiments, a candidate combination is replaced with another combination of weights based on differential evolution techniques in order to maximize evaluation metrics. For example, control circuitry310may replace a poorly performing candidate combination with a candidate combination generated by mutating one of candidate combinations that resulted in a generation of a promising evaluation metric (e.g., a candidate combination that resulted in an evaluation metric that exceeded the threshold). In another example, control circuitry310may replace a poorly performing candidate combination with a candidate combination generated by crossing over two or more of the candidate combinations that resulted in a generation of a promising evaluation metric (e.g., a candidate combination that resulted in evaluation metrics that exceeded the threshold).

At420, control circuitry310may check if the evaluation metrics are improving. For example, while steps410-418are being performed, control circuitry310may maintain a historical high evaluation metric (e.g., the value of the highest evaluation metric is stored in a constant in storage312) for each iteration cycle. After each cycle of steps410-418, control circuitry310may compare all historical high evaluation metrics of previous cycles. For example, the last five cycles may have resulted in historical high evaluation metric of {40, 50, 55, 55, 55}. Because last three historical high evaluation metrics ({50, 50, 50}) are not increasing, control circuitry310may determine that evaluation metrics have stopped improving. In some embodiments, improvements that are lower than 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 evaluation metrics of {40, 50, 55, 56, 57}. Because the last three historical high evaluation metrics ({55, 56, 57}) are not increasing by a sufficiently high margin, control circuitry310may determine that evaluation metrics have stopped improving.

If the evaluation metrics are still improving, control circuitry310repeats424steps410-418. Otherwise, control circuitry310proceeds to step422. At step422, control circuitry310selects candidate weight combination that resulted in the highest evaluation metrics. This weight combination is then used in the production of recommendations. For example, at step426, control circuitry310may use the selected candidate weight combination to select a final set of media content items. In some embodiments, this final set may be reported to other devices (e.g., via communications network358). In some embodiments, control circuitry310may, at step428, generate for display identifiers for the final set of recommended content items. For example, the identifiers for recommended content items may be displayed on display320by server301or media content source354. 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. 5depicts an illustrative flowchart of process500for selecting candidate weight combinations that may be implemented by using systems200and300, in accordance with some embodiments of the disclosure. In various embodiments, individual steps of process500may be implemented by one or more components of systems200and300. Although the present disclosure may describe certain steps of process500(and of other processes described herein) as being implemented by certain components of systems200and300, this is for purposes of illustration only, and it should be understood that other components of systems200and300may implement those steps instead. For example, steps of process500may be executed by server202and/or by computing device260to provide content recommendations. In some embodiments, control circuitry310may use process500as part of process400ofFIG. 4(e.g., in addition to or instead of steps410-418).

At step502, control circuitry310may generate multiple sets of candidate weight combinations (e.g., weight combinations104,106,108ofFIG. 1). In some embodiments, at least some of the weight combinations may be randomized. Any number of candidate weight combinations may be generated. At step504, a set of media content items is selected based on each of the candidate combinations. For example, at506, a set of media content items is selected using weight combination104. At508, a set of media content items is selected using weight combination106. And, at510, a set of media content items is selected using weight combination108.

At step512, control circuitry310may evaluate the performance of each weight combination generated in steps506-510. For example, to test a candidate weight combination, control circuitry310may generate an evaluation metric for each weight combination based on matches with requested content items over a certain period of time (e.g., one day or one week). For example, the method described above in step412may be used to generate an evaluation metric.

Steps514-524may be performed for each weight combination (e.g., for weight combinations104,106, and108). At514, control circuitry310may check if the evaluation metric for a particular weight combination is low. For example, control circuitry310may select only the weight combination that scored the lowest for replacement. In some embodiments, control circuitry310may select all weight combinations that scored below a threshold for replacement. If the combination is not low, then, at step515, control circuitry310may keep that weight combination in storage312. At516, weight combination with low evaluation metrics may be eliminated. For example, at520, control circuitry310may completely delete the lowest scoring weight combination from storage312. Additionally, at518, control circuitry310may generate a replacement combination in any one of the ways described at step522or step524below.

For example, at522, control circuitry310may generate a new weight combination by mutating one of the other weight combinations (e.g., as shown inFIG. 9). At524, control circuitry310may generate a new weight combination by crossing over two other weight combinations (e.g., as shown inFIG. 10).

At526, control circuitry310may repeat steps502-524for newly replaced weight combinations until evaluation metrics stop improving (e.g., if the historically high evaluation metrics stagnate over certain number of cycles). At528, the highest-scoring weight combination is selected to be used (e.g., for selecting recommendations for display).

FIG. 6depicts an illustrative flowchart of process600for generating a set of content items for a candidate weight combination that may be implemented by using systems200and300, in accordance with some embodiments of the disclosure. In various embodiments, the individual steps of process600may be implemented by one or more components of systems200and300. Although the present disclosure may describe certain steps of process600(and of other processes described herein) as being implemented by certain components of systems200and300, this is for purposes of illustration only, and it should be understood that other components of systems200and300may implement those steps instead. For example, the steps of process600may be executed by server302and/or by computing device360to provide content recommendations. In some embodiments, control circuitry310may use process600as a part of process400ofFIG. 4(e.g., in addition to or instead of step408). For example, control circuitry310, when performing step408, may use process600to select content items for a particular weight combination.

At602, control circuitry310may generate a preliminary set of content items using each available prediction algorithm. For example, control circuitry310may issue an API call to server301to run each available prediction algorithm with a specified input (e.g., input specifying desired user demographics). In this example, server301executes each prediction algorithm and returns a preliminary set of predicted content items for each prediction algorithm via communications network358.

At604, control circuitry310may count how many times a particular content item has appeared in any of the preliminary sets of predicted content items. For example, if a “Game of Thrones” episode appeared in three preliminary sets of predicted content items, the count of “3” may be stored for that “Game of Thrones” episode.

At606, control circuitry310may adjust the count values based on the weight assigned to each prediction algorithm. For example, if the “Game of Thrones” episode appeared in a preliminary set of predicted content items generated by a prediction algorithm with a weight “0.3,” the count may be reduced proportionally to the weight. For example, only 0.3 points may be added to the count instead of a regular “1” point.

At608, control circuitry310may check if more content items are left to be evaluated. If so, control circuitry310returns to604and repeats the process until a count has been established for each content item. Once no more items remain, control circuitry310proceeds to step610. At610, control circuitry310selects a predetermined number (e.g., 10) content items with the highest score for inclusion in the set of items for the particular weight combination.

In some embodiments, instead of adding up the adjusted count values as described in connection with steps606-610, control circuitry310may select for inclusion into the set of items for the particular weight combination the content items that have the greatest associated weight in any of the preliminary set of predicted content items. In other embodiments, any other algorithms or techniques for inclusion of content items into the set of items for the particular weight combination may be used.

FIG. 7depicts an illustrative scenario for generating a set of content items for a candidate weight combination that may be implemented by using systems200and300, in accordance with some embodiments of the disclosure. In particular,FIG. 7depicts an exemplary way to generate a set of content items for candidate weight combination702. In some embodiments, illustrative scenario ofFIG. 7may be performed as part of step408ofFIG. 4.

As shown, weight combination702assigns a value of 0.5 to first prediction algorithm704, a value of 0 to second prediction algorithm706, and a value of 0.3 to third prediction algorithm708. Each of the algorithms may be used to generate a preliminary set of content items. For example, control circuitry310may issue an API call to server301to execute each of algorithms704,706, and708. Server301may then return over communications network358the following preliminary set of content items: set {Game of Thrones, 20/20} for algorithm704, set {Today, Spellbound} for algorithm706, and set {Game of Thrones, Today} for algorithm708.

In some embodiments, control circuitry310may then calculate content item counts 712 for each content item from the preliminary set of content items. The count may be weighted based on the weights of each of algorithms704,706,708. For example, “Game of Thrones” appears twice, once in the set produced using algorithm704and once in the set produced using algorithm708. In this scenario the “Game of Thrones” content item may receive a total score724of “0.8” (0.5 from weight of algorithm704and 0.3 from weight of algorithm708, which are summed). Scores726,728, and730for content items 20/20, Today, and Spellbound may be calculated in a similar manner.

Control circuitry310may then select the set of content items with the highest score for candidate weight combination702. For example, because “Game of Thrones” content item and “20/20” have the highest counts (0.8 and 0.5, respectively), they are selected for the set of content items associated with the candidate weight combination702.

It is to be noted, that the techniques described inFIG. 7is exemplary, and other techniques may be used to select content items for the set of content items associated with the candidate weight combination702. For example, control circuitry310may select content items “Game of Thrones,” and “20/20” because they appear in the preliminary set with the highest weight.

FIG. 8depicts an illustrative flowchart of process800for replacing a candidate weight combination that may be implemented by using systems200and300, in accordance with some embodiments of the disclosure. In various embodiments, individual steps of process800may be implemented by one or more components of systems200and300. Although the present disclosure may describe certain steps of process500(and of other processes described herein) as being implemented by certain components of systems200and300, this is for purposes of illustration only, and it should be understood that other components of systems200and300may implement those steps instead. For example, steps of process800may be executed by server302and/or by computing device360to provide content recommendations. In some embodiments, control circuitry310may use process800as a part of process400ofFIG. 4(e.g., in addition to or instead of step418). For example, control circuitry310, when performing step418, may use process800to generate a replacement weight combination.

At802, control circuitry310may determine whether a particular weight combination needs to be replaced. For example, steps410-414ofFIG. 4may be used to perform this determination. If the particular weight combination does not need to be replaced, process800ends at step804. Otherwise, process800proceeds to steps806and808. In some embodiments, steps806-810and808-812may be performed selectively (i.e., only one may be performed), simultaneously, or one after the other.

At806, control circuitry310may select one other candidate weight combination that is determined to be kept. This weight combination is mutated at step810. For example, some of the weights of the other candidate weight combination may be micro-adjusted (e.g., by using 5%-10% adjustment). In some embodiments, the value of adjustment may be selected using differential evolution algorithms. In some embodiments, the mutated weight combination may be normalized (e.g., so that all weights add up to the same constant).

At808, control circuitry310may select two other candidate weight combinations that are determined to be kept. These weight combinations are crossed over at step812. For example, some of the weights of other candidate weight combination and some weights of the second other candidate weight combination are used to create a new weight combination. In some embodiments, the crossed-over weight combination may be normalized (e.g., so that all weights add up to a constant).

At814, the new mutated or crossed-over combination is used as the replacement combination. In some embodiments, the new weight combination can be both crossed over and mutated in any order.

FIG. 9depicts an illustrative scenario for mutating a candidate weight combination that may be implemented by using systems200and300, in accordance with some embodiments of the disclosure. In particular,FIG. 9depicts an exemplary mutation of a candidate weight combination902. For example, the steps ofFIG. 9may be performed as part of step418ofFIG. 4.

As shown, a candidate weight combination assigns values of {0.5; 0; and 0.3} to three recommendation (e.g., prediction) algorithms. At904, a mutation factor is applied. For example, the first weight is modified by 0.1,” the second weight is modified by −0.2, and the third weight is modified by 0 (i.e., not modified). These adjustment values may be selected by a differential evolution algorithm attempting to maximize the evaluation metric. As a result, the mutated combination906assigns values of {0.6; −0.2; and 0.3} to three prediction algorithms. It should be noted that a negative weight may indicate that content items generated by this algorithm may be counted negatively. The mutated combination906may then be normalized (not shown).

FIG. 10depicts an illustrative scenario for crossing over a candidate weight combination that may be implemented by using systems200and300, in accordance with some embodiments of the disclosure. In particular,FIG. 10depicts an exemplary crossover of candidate weight combinations1012. For example, steps ofFIG. 10may be performed as part of step418ofFIG. 4.

As shown, candidate weight combination 1 assigns values of {0.5; 0; and 0.3} to three predictions algorithms, while candidate weight combination 2 assigns values of {0; 0.7; and 0.8} to the same three predictions algorithms. At1014, a crossover occurs. In this example, values 0.5 and 0.3 of the first weight combination are kept, and value 0.7 of the second weight combination is kept. The kept weights are combined into a new combination1016that assigns values of {0.5; 0.7; and 0.3} to three predictions algorithms. The crossed-over combination1016may then be normalized (not shown).

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.