Systems and methods for using hierarchical ordered weighted averaging for providing personalized media content

An electronic device, for each media content item of a plurality of media content items, receives a respective score for each of a first set of objectives and a second set of objectives and generates a respective score between a user and the media content item. The generating includes applying a first ordered weighted average to the respective scores for the first set of objectives, to produce a first combined score for the first set of objectives, applying a second ordered weighted average to the respective scores for the second set of objectives, to produce a second combined score for the second set of objectives and applying a third ordered weighted average to the combined score for the first set of objectives and the second set of objectives. The electronic device provides media content to the user based on the respective scores between the user and the media content items.

RELATED APPLICATIONS

This application is related to U.S. application Ser. No. 17/075,302, filed Oct. 20, 2020, entitled “Using a Hierarchical Machine Learning Algorithm for Providing Personalized Media Content,” which is hereby incorporated in its entirety.

TECHNICAL FIELD

The disclosed embodiments relate generally to media playback, and, in particular, to selecting media content for a user based in part on objective determined using hierarchical ordered weighted averaging.

BACKGROUND

Access to electronic media, such as music and video content, has expanded dramatically over time. As a departure from physical media, media content providers stream media to electronic devices across wireless networks, improving the convenience with which users can digest and experience such content.

Media content providers provide recommendations of content to users. Media content providers collect data related to their users in order to provide better and more personalized recommendations.

SUMMARY

There is a need for systems and methods of choosing media content items to present to a user, such as an order of media content items in a playlist. The chosen media content items may be recommended to the user, such that the user is enabled to select the media content for playback, or may be automatically presented to the user without user input.

To that end, the present disclosure is directed to generating scores between a user and a plurality of media items in order to determine which media items to present to the user. For example, in some embodiments, the user is provided the media content item with the highest score.

One object of the present disclosure is to combine scores for different objectives into an overall score for the media item. For example, some objectives might correspond to a user (e.g., it's desirable that the user not skip the recommended media item) whereas other objectives might correspond to the publisher (e.g., the desirability of promoting new artists). In addition, some of these scores may be predicted (e.g., using a machine learning algorithm), whereas some of the scores may be measured (e.g., based on historical information, such as the popularity of a song over the last two weeks).

In some embodiments, the system combines scores for objectives in different ways. For example, it's possible that the media-providing service might want to recommend a media item if: (1) the user desirability of the media content item is high over (e.g., many of the user objectives have high scores), and (2) any of the publisher's objectives are met (e.g., promoting new artist or promoting diverse artists, etc.). To that end, objectives are combined in using a hierarchical ordered weighted average, whereby scores for a first set of objectives (e.g., user objectives) are combined using a first ordered weighted average and scores for a second set of objectives (producer objectives) are combined using a second ordered weighted average. The first ordered weighted average and the second ordered weighted average can be combined using different parameters (e.g., such that the first set objectives are combined to produce a high score when MANY of the individual scores are high and the second set of objectives are combined to produce a high score when ANY of the individual scores are high).

In accordance with some embodiments, a method is performed at a first electronic device. The first electronic device has one or more processors and memory storing instructions for execution by the one or more processors. The method includes, for each media content item of a plurality of media content items, receiving a respective score for each of a first set of objectives and a second set of objectives and generating a respective score between a user and the media content item. The generating comprises applying a first ordered weighted average to the respective scores for the first set of objectives, to produce a first combined score for the first set of objectives, applying a second ordered weighted average to the respective scores for the second set of objectives, to produce a second combined score for the second set of objectives, and applying a third ordered weighted average to the combined score for the first set of objectives and the combined score for the second set of objectives. The method further includes providing media content to the user based on the respective scores between the user and the media content items in the plurality of media content items.

In accordance with some embodiments, a first electronic device (e.g., a server system, a client device, etc.) includes one or more processors and memory storing one or more programs configured to be executed by the one or more processors. The one or more programs include instructions for, for each media content item of a plurality of media content items, receiving a respective score for each of a first set of objectives and a second set of objectives and generating a respective score between a user and the media content item. The generating comprises applying a first ordered weighted average to the respective scores for the first set of objectives, to produce a first combined score for the first set of objectives, applying a second ordered weighted average to the respective scores for the second set of objectives, to produce a second combined score for the second set of objectives, and applying a third ordered weighted average to the combined score for the first set of objectives and the combined score for the second set of objectives. The one or more programs further include instructions for providing media content to the user based on the respective scores between the user and the media content items in the plurality of media content items.

In accordance with some embodiments, a computer-readable storage medium has stored therein instructions that, when executed by a first electronic device, cause the first electronic device to, for each media content item of a plurality of media content items, receive a respective score for each of a first set of objectives and a second set of objectives and generate a respective score between a user and the media content item. The generating comprises applying a first ordered weighted average to the respective scores for the first set of objectives, to produce a first combined score for the first set of objectives, applying a second ordered weighted average to the respective scores for the second set of objectives, to produce a second combined score for the second set of objectives, and applying a third ordered weighted average to the combined score for the first set of objectives and the combined score for the second set of objectives. The instructions further cause the first electronic device to provide media content to the user based on the respective scores between the user and the media content items in the plurality of media content items.

Thus, systems are provided with improved methods for identifying and providing a user with a recommended list of media content items.

DETAILED DESCRIPTION

It will also be understood that, although the terms first, second, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first electronic device could be termed a second electronic device, and, similarly, a second electronic device could be termed a first electronic device, without departing from the scope of the various described embodiments. The first electronic device and the second electronic device are both electronic devices, but they are not the same electronic device.

FIG.1is a block diagram illustrating a media content delivery system100, in accordance with some embodiments. The media content delivery system100includes one or more electronic devices102(e.g., electronic device102-1to electronic device102-m, where m is an integer greater than one), one or more media content servers104, and/or one or more content distribution networks (CDNs)106. The one or more media content servers104are associated with (e.g., at least partially compose) a media-providing service. The one or more CDNs106store and/or provide one or more content items (e.g., to electronic devices102). In some embodiments, the CDNs106are included in the media content servers104. One or more networks112communicably couple the components of the media content delivery system100. In some embodiments, the one or more networks112include public communication networks, private communication networks, or a combination of both public and private communication networks. For example, the one or more networks112can be any network (or combination of networks) such as the Internet, other wide area networks (WAN), local area networks (LAN), virtual private networks (VPN), metropolitan area networks (MAN), peer-to-peer networks, and/or ad-hoc connections.

In some embodiments, an electronic device102is associated with one or more users. In some embodiments, an electronic device102is a personal computer, mobile electronic device, wearable computing device, laptop computer, tablet computer, mobile phone, feature phone, smart phone, digital media player, a speaker, television (TV), digital versatile disk (DVD) player, and/or any other electronic device capable of presenting media content (e.g., controlling playback of media items, such as music tracks, videos, etc.). Electronic devices102may connect to each other wirelessly and/or through a wired connection (e.g., directly through an interface, such as an HDMI interface). In some embodiments, an electronic device102is a headless client. In some embodiments, electronic devices102-1and102-mare the same type of device (e.g., electronic device102-1and electronic device102-mare both speakers). Alternatively, electronic device102-1and electronic device102-minclude two or more different types of devices.

In some embodiments, electronic devices102-1and102-msend and receive media-control information through network(s)112. For example, electronic devices102-1and102-msend media control requests (e.g., requests to play music, movies, videos, or other media items, or playlists thereof) to media content server104through network(s)112. Additionally, electronic devices102-1and102-m, in some embodiments, also send indications of media content items to media content server104through network(s)112. In some embodiments, the media content items are uploaded to electronic devices102-1and102-mbefore the electronic devices forward the media content items to media content server104.

In some embodiments, electronic device102-1communicates directly with electronic device102-m(e.g., as illustrated by the dotted-line arrow), or any other electronic device102. As illustrated inFIG.1, electronic device102-1is able to communicate directly (e.g., through a wired connection and/or through a short-range wireless signal, such as those associated with personal-area-network (e.g., BLUETOOTH/BLE) communication technologies, radio-frequency-based near-field communication technologies, infrared communication technologies, etc.) with electronic device102-m. In some embodiments, electronic device102-1communicates with electronic device102-mthrough network(s)112. In some embodiments, electronic device102-1uses the direct connection with electronic device102-mto stream content (e.g., data for media items) for playback on the electronic device102-m.

In some embodiments, electronic device102-1and/or electronic device102-minclude a media application222(FIG.2) that allows a respective user of the respective electronic device to upload (e.g., to media content server104), browse, request (e.g., for playback at the electronic device102), and/or present media content (e.g., control playback of music tracks, videos, etc.). In some embodiments, one or more media content items are stored locally by an electronic device102(e.g., in memory212of the electronic device102,FIG.2). In some embodiments, one or more media content items are received by an electronic device102in a data stream (e.g., from the CDN106and/or from the media content server104). The electronic device(s)102are capable of receiving media content (e.g., from the CDN106) and presenting the received media content. For example, electronic device102-1may be a component of a network-connected audio/video system (e.g., a home entertainment system, a radio/alarm clock with a digital display, or an infotainment system of a vehicle). In some embodiments, the CDN106sends media content to the electronic device(s)102.

In some embodiments, the CDN106stores and provides media content (e.g., media content requested by the media application222of electronic device102) to electronic device102via the network(s)112. Content (also referred to herein as “media items,” “media content items,” and “content items”) is received, stored, and/or served by the CDN106. In some embodiments, content includes audio (e.g., music, spoken word, podcasts, etc.), video (e.g., short-form videos, music videos, television shows, movies, clips, previews, etc.), text (e.g., articles, blog posts, emails, etc.), image data (e.g., image files, photographs, drawings, renderings, etc.), games (e.g., 2- or 3-dimensional graphics-based computer games, etc.), or any combination of content types (e.g., web pages that include any combination of the foregoing types of content or other content not explicitly listed). In some embodiments, content includes one or more audio media items (also referred to herein as “audio items,” “tracks,” and/or “audio tracks”).

In some embodiments, media content server104receives media requests (e.g., commands) from electronic devices102. In some embodiments, media content server104includes a voice API, a connect API, and/or key service (e.g., key database336,FIG.3). In some embodiments, media content server104validates (e.g., using key service) electronic devices102by exchanging one or more keys (e.g., tokens) with electronic device(s)102.

In some embodiments, media content server104and/or CDN106stores one or more playlists (e.g., information indicating a set of media content items). For example, a playlist is a set of media content items defined by a user and/or defined by an editor associated with a media-providing service. The description of the media content server104as a “server” is intended as a functional description of the devices, systems, processor cores, and/or other components that provide the functionality attributed to the media content server104. It will be understood that the media content server104may be a single server computer, or may be multiple server computers. Moreover, the media content server104may be coupled to CDN106and/or other servers and/or server systems, or other devices, such as other client devices, databases, content delivery networks (e.g., peer-to-peer networks), network caches, and the like. In some embodiments, the media content server104is implemented by multiple computing devices working together to perform the actions of a server system (e.g., cloud computing).

FIG.2is a block diagram illustrating an electronic device102(e.g., electronic device102-1and/or electronic device102-m,FIG.1), in accordance with some embodiments. The electronic device102includes one or more central processing units (CPU(s), i.e., processors or cores)202, one or more network (or other communications) interfaces210, memory212, and one or more communication buses214for interconnecting these components. The communication buses214optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components.

In some embodiments, the electronic device102includes a user interface204, including output device(s)206and/or input device(s)208. In some embodiments, the input devices208include a keyboard, mouse, or track pad. Alternatively, or in addition, in some embodiments, the user interface204includes a display device that includes a touch-sensitive surface, in which case the display device is a touch-sensitive display. In electronic devices that have a touch-sensitive display, a physical keyboard is optional (e.g., a soft keyboard may be displayed when keyboard entry is needed). In some embodiments, the output devices (e.g., output device(s)206) include a speaker252(e.g., speakerphone device) and/or an audio jack250(or other physical output connection port) for connecting to speakers, earphones, headphones, or other external listening devices. Furthermore, some electronic devices102use a microphone and voice recognition device to supplement or replace the keyboard. Optionally, the electronic device102includes an audio input device (e.g., a microphone) to capture audio (e.g., speech from a user).

Optionally, the electronic device102includes a location-detection device240, such as a global navigation satellite system (GNSS) (e.g., GPS (global positioning system), GLONASS, Galileo, BeiDou) or other geo-location receiver, and/or location-detection software for determining the location of the electronic device102(e.g., module for finding a position of the electronic device102using trilateration of measured signal strengths for nearby devices).

In some embodiments, the one or more network interfaces210include wireless and/or wired interfaces for receiving data from and/or transmitting data to other electronic devices102, a media content server104, a CDN106, and/or other devices or systems. In some embodiments, data communications are carried out using any of a variety of custom or standard wireless protocols (e.g., NFC, RFID, IEEE 802.15.4, Wi-Fi, ZigBee, 6LoWPAN, Thread, Z-Wave, Bluetooth, ISA100.11a, WirelessHART, MiWi, etc.). Furthermore, in some embodiments, data communications are carried out using any of a variety of custom or standard wired protocols (e.g., USB, Firewire, Ethernet, etc.). For example, the one or more network interfaces210include a wireless interface260for enabling wireless data communications with other electronic devices102, media presentations systems108, and/or or other wireless (e.g., Bluetooth-compatible) devices (e.g., for streaming audio data to the media presentations system108of an automobile). Furthermore, in some embodiments, the wireless interface260(or a different communications interface of the one or more network interfaces210) enables data communications with other WLAN-compatible devices (e.g., a media presentations system108) and/or the media content server104(via the one or more network(s)112,FIG.1).

In some embodiments, electronic device102includes one or more sensors including, but not limited to, accelerometers, gyroscopes, compasses, magnetometer, light sensors, near field communication transceivers, barometers, humidity sensors, temperature sensors, proximity sensors, range finders, and/or other sensors/devices for sensing and measuring various environmental conditions.

Memory212includes high-speed random-access memory, such as DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. Memory212may optionally include one or more storage devices remotely located from the CPU(s)202. Memory212, or alternately, the non-volatile memory solid-state storage devices within memory212, includes a non-transitory computer-readable storage medium. In some embodiments, memory212or the non-transitory computer-readable storage medium of memory212stores the following programs, modules, and data structures, or a subset or superset thereof:an operating system216that includes procedures for handling various basic system services and for performing hardware-dependent tasks;network communication module(s)218for connecting the client device102to other computing devices (e.g., media presentation system(s)108, media content server104, and/or other client devices) via the one or more network interface(s)210(wired or wireless) connected to one or more network(s)112;a user interface module220that receives commands and/or inputs from a user via the user interface204(e.g., from the input devices208) and provides outputs for playback and/or display on the user interface204(e.g., the output devices206);a media application222(e.g., an application for accessing a media-providing service of a media content provider associated with media content server104) for uploading, browsing, receiving, processing, presenting, and/or requesting playback of media (e.g., media items). In some embodiments, media application222includes a media player, a streaming media application, and/or any other appropriate application or component of an application. In some embodiments, media application222is used to monitor, store, and/or transmit (e.g., to media content server104) data associated with user behavior with respect to media content. In some embodiments, media application222also includes the following modules (or sets of instructions), or a subset or superset thereof:a media content selection module224for selecting one or more media content items and/or sending, to the media content server, an indication of the selected media content item(s);a media content browsing module226for providing controls and/or user interfaces enabling a user to navigate, select for playback, and otherwise control or interact with media content, whether the media content is stored or played locally or remotely;a content items module228for storing media items for playback and/or for forwarding requests for media content items to the media content server;an objectives module230for determining and/or storing objectives from one or more parties, such as objectives of the user of the electronic device102; anda context module232for determining current contextual information, such as from a playback history of a user, to be provided to a machine learning model;a machine learning model233for determining scores for a plurality of objectives (e.g., tasks) between a user and a respective media content item;a weighted average module234for determining ordered weighted averages for a plurality of objectives (e.g., tasks), and providing the values determined from the ordered weighted averages to media content selection module224for selecting media content baseda web browser application235(e.g., Internet Explorer or Edge by Microsoft, Firefox by Mozilla, Safari by Apple, or Chrome by Google) for accessing, viewing, and interacting with web sites; andother applications236, such as applications for word processing, calendaring, mapping, weather, stocks, time keeping, virtual digital assistant, presenting, number crunching (spreadsheets), drawing, instant messaging, e-mail, telephony, video conferencing, photo management, video management, a digital music player, a digital video player, 2D gaming, 3D (e.g., virtual reality) gaming, electronic book reader, and/or workout support.

FIG.3is a block diagram illustrating a media content server104, in accordance with some embodiments. The media content server104typically includes one or more central processing units/cores (CPUs)302, one or more network interfaces304, memory306, and one or more communication buses308for interconnecting these components.

Memory306includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid-state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. Memory306optionally includes one or more storage devices remotely located from one or more CPUs302. Memory306, or, alternatively, the non-volatile solid-state memory device(s) within memory306, includes a non-transitory computer-readable storage medium. In some embodiments, memory306, or the non-transitory computer-readable storage medium of memory306, stores the following programs, modules and data structures, or a subset or superset thereof:an operating system310that includes procedures for handling various basic system services and for performing hardware-dependent tasks;a network communication module312that is used for connecting the media content server104to other computing devices via one or more network interfaces304(wired or wireless) connected to one or more networks112;one or more server application modules314for performing various functions with respect to providing and managing a content service, the server application modules314including, but not limited to, one or more of:a media content module316for storing one or more media content items and/or sending (e.g., streaming), to the electronic device, one or more requested media content item(s), including but not limited to, one or more of:an objectives module318for determining and/or storing objectives from one or more parties; anda context module320for determining current contextual information, such as from a playback history of a user and/or matching current contextual information to previously recorded contextual information, to be provided to a multi-arm bandit model; anda media request processing module322for processing requests for media content and facilitating access to requested media items by client devices (e.g., the client device102) including, optionally, streaming media content to such devices and/or to one or more media presentation system(s)108;a machine learning model324for determining scores for a plurality of objectives (e.g., tasks) between a user and a respective media content item;one or more server data module(s)330for handling the storage of and/or access to media items and/or metadata relating to the media items; in some embodiments, the one or more server data module(s)330include:a media content database332for storing media items;a metadata database334for storing metadata relating to the media items; anda key database336for storing keys related to account information for user media accounts, such as user profiles, credentials (e.g., user identifiers, passwords, email addresses, etc.), and/or identifiers of any linked accounts.

In some embodiments, the media content server104includes web or Hypertext Transfer Protocol (HTTP) servers, File Transfer Protocol (FTP) servers, as well as web pages and applications implemented using Common Gateway Interface (CGI) script, PHP Hyper-text Preprocessor (PHP), Active Server Pages (ASP), Hyper Text Markup Language (HTML), Extensible Markup Language (XML), Java, JavaScript, Asynchronous JavaScript and XML (AJAX), XHP, Javelin, Wireless Universal Resource File (WURFL), and the like.

Each of the above identified modules stored in memory212and306corresponds to a set of instructions for performing a function described herein. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. In some embodiments, memory212and306optionally store a subset or superset of the respective modules and data structures identified above. Furthermore, memory212and306optionally store additional modules and data structures not described above.

AlthoughFIG.3illustrates the media content server104in accordance with some embodiments,FIG.3is intended more as a functional description of the various features that may be present in one or more media content servers than as a structural schematic of the embodiments described herein. In practice, and as recognized by those of ordinary skill in the art, items shown separately could be combined and some items could be separated. For example, some items shown separately inFIG.3could be implemented on single servers and single items could be implemented by one or more servers. In some embodiments, media content database332and/or metadata database334are stored on devices (e.g., CDN106) that are accessed by media content server104. The actual number of servers used to implement the media content server104, and how features are allocated among them, will vary from one implementation to another and, optionally, depends in part on the amount of data traffic that the server system handles during peak usage periods as well as during average usage periods.

FIG.4is a block diagram of a multi-level machine learning algorithm. The system includes a first level400and a second level401. Each level in the multi-level machine learning algorithm includes one or more neural networks. For example, first level400includes neural network408and neural network416, and second level401includes neural network423. In some embodiments, each a neural network comprises an encoder (and decoder) to encode the input sequence (e.g., referred to herein as an input) into a target sequence (e.g., referred to herein as an output).

In some embodiments, a media content provider, in providing recommendations to a user, predicts how a user is likely to engage with the media content item, by either streaming the media content item completely (e.g., consuming the entire media content item), or skipping the media content item before completing it. Further, users are provided with the option to mark the media content item as a favorite (e.g., heart) to indicate that they are satisfied with the media content item, or to ban (e.g., block) a media content item to indicate that they are not satisfied with the media content item. These types of user interactions correspond to different categories: engagement objectives and satisfaction objectives, respectively. The prediction model shown inFIG.4considers both types of these objectives for the media content provider (e.g., engagement objectives in first level400and satisfaction objectives in second level401). In some embodiments, the media content provider calculates scores for these objectives to recommend media content for the user.

In some embodiments, engagement objectives and satisfaction objectives are closely related, and thus, it is beneficial to consider information about one objective when making a prediction for another objective. For example, information about one engagement objective would help the media content provider calculate another engagement objective, and the combined information about both engagement objectives helps the model learn more about user satisfaction (e.g., as satisfaction objectives). To this end, a hierarchy is provided between the objectives (also referred to as tasks) so that engagement objectives are determined at lower levels (e.g., level400) of the architecture (e.g., in a supervised neural network) while satisfaction objectives are determined at higher layers (e.g., levels) of the machine learning algorithm (e.g., at a neural network in a higher level than the neural network(s) for the engagement objectives) (e.g., level401). In some embodiments, satisfaction objectives are considered more complex than engagement objectives, and thus are placed in a higher level than engagement objectives. Other types of objectives may be considered more or less complex than engagement and/or satisfaction objectives. In some embodiments, the more complex objectives are associated with neural networks at higher levels than the neural networks of less complex objectives (e.g., such that information from the neural networks for less complex objectives are fed into the neural networks for the more complex objectives).

The model illustrated inFIG.4is trained end-to-end, using historic media content consumption data (e.g., streaming data stored in a playback history of a user). In some embodiments, the end-to-end training comprises back-propagating between each neural network (e.g., starting with the neural network in the highest level, level401). In some embodiments, because the end-to-end training comprises back-propagating between each neural network, the multiple neural networks are not trained independently (e.g., the entire system is trained with the connections between neural networks in place, such that, during a training phase, the weights for each neural network of the multiple neural networks is updated in a respective iteration before the next feed-forward iteration). In some embodiments, this multi-task approach to predicting engagement and satisfaction that is provided performs better than developing single objective models for each of the objectives.

In some embodiments, the machine learning algorithm includes a first neural network (e.g., neural network406) that receives input(s) that describe a user and a content item (e.g., of a plurality of content items). In some embodiments, the first neural network is considered as a zeroth level (e.g., distinct from the first level or the second level) in the hierarchical machine learning algorithm. In some embodiments, the first neural network406is included in first level400(not shown), rather than in its own zeroth level.

In some embodiments, the inputs to first neural network406include a user vector402and a content vector404. User vector402describes features of the user, such as demographic information, taste profile information (e.g., the genre, artists, or types of media content the user prefers to consume, which can be based on a playback history of the user), playback history information (e.g., does the user tend to access media content at particular times of the day, use the shuffle feature, etc.), and any other information known about the user. Content vector404includes information about a respective media content item, such as tempo, genre, artist, playlist, and/or other descriptive features of the media content item.

The first neural network406uses the input vectors (e.g., user vector402and content vector404) and outputs a first output. In some embodiments, the first output from the first neural network406is used as an input for a neural network in each level of the hierarchical machine learning algorithm. For example, neural network408in the first level of the machine learning algorithm uses the first output from neural network406as an input. Also, neural network423in the second level of the machine learning algorithm uses the first output from the neural network406as an input for the neural network423.

In some embodiments, the multi-level machine learning algorithm calculates scores for the various objectives corresponding to a respective user and a respective media item. Each level calculates a score for one or more objectives (e.g., the score being for a particular user and a particular media item). The first level outputs two scores, score414and score422, each score corresponding to a distinct objective. For example, score414corresponds to a skip prediction objective calculated using task specific encoder410(using mathematical function412, which in some embodiments, sums the values of the output of task specific encoder410) and score422corresponds to a completion objective calculated using task specific encoder418(using mathematical function420which in some embodiments, sums the values of the output of task specific encoder418). In some embodiments, calculated score414does not directly affect the calculation of score422(e.g., score414is not an input to the calculation score422). Instead, in some embodiments, the output of neural network408is used as an input to neural network416. Within the first level400, neural network416takes the output of neural network408as an input. Note that the output of neural network408is not task (or objective) specific. For example, task specific encoder410and the calculation of score414is distinct from, and does not affect, the output of neural network408(or the input to neural network416). Thus, the scores414and422, are both calculated in the first level400, but are not dependent on each other. Similarly, the scores calculated in the first level are not used as inputs to the neural network423in the second level401.

The second level401outputs a score428for an objective corresponding to task specific encoder424. For example, a mathematical function426(e.g., summation) is applied to the output of task specific encoder424to generate score428. The neural network423takes, as inputs, the output from the first level (e.g., the output of neural network416) and the output from the zeroth level (e.g., the output of neural network406).

In some embodiments, the hierarchical machine learning algorithm illustrated inFIG.4is applied to each media content item in a plurality of media content items (e.g., where the inputs, content vector404and optionally user vector402, are updated for each media content item). Since each score (e.g., score414, score422, and score428) calculated inFIG.4represents a prediction of the respective objective for a respective media content item (e.g., corresponding to input vector) and the user, the scores for the respective objectives can be combined or otherwise used to select which media content item to present (e.g., recommend) to the user. For example, the media content provider can select a single objective, and compare the scores for the single objective for each media content item to select media content (e.g., the media content item with the highest score for the single objective). In some embodiments, for each media content item, the scores for each objective are used to calculate an ordered weighted average (OWA) for the plurality of objectives, as described with reference toFIGS.5A-5BandFIGS.7A-7B.

In some embodiments, the objectives are grouped into “levels” based on the type of objective. For example, predictive objectives that are used to measure a prediction of user behavior with respect to media content are grouped in the first level400. For example, prediction objectives include a prediction that a user will skip a media content item (e.g., the user will consume less than a predetermined amount of time of the media content item, such as 10 seconds). A prediction objective also includes, for example, a prediction that a user with complete a media content item (e.g., the user will consume more than a predetermined amount of the media content item, such as 90% of the media content item, as measured by the length of the media content item). In some embodiments, engagement objectives that are used to measure the satisfaction of a user with media content items are grouped in the second level401.

In some embodiments, each objective is considered its own level in the hierarchical machine learning algorithm. For example, in some embodiments, the calculation of score414is the first level, and the calculation of score422is a second level distinct from the first level. For purposes of illustration,FIG.4shows the calculation of score414and the calculation of score422within the same level (e.g., the first level400). However, it would be understood by one of ordinary skill in the art that these objectives (and scores) could each correspond to distinct levels.

FIGS.5A-5Bare block diagrams illustrating hierarchical ordered weighted averaging for a plurality of objectives (referred to below as criteria). In order to determine the appropriateness of a particular media content item (referred to below as an alternative, x), as the solution (e.g., the media content item to recommend or provide to the user), its scores for the various objectives are aggregated using an ordered weighted average to find some overall, single value to associate with the media content item. Ordered weighted average functions (OWA functions) are aggregation functions that associate weights with the value of each input (instead of associating weights with a particular input). In this way, OWA functions differ from weighted arithmetic means in that the weights for OWA functions are not a priori associated with the particular inputs, but are associated with particular inputs based on the relative magnitude of the inputs (e.g., a first weight is applied to the highest-valued input, a second weight is applied to the second highest-valued input, and so on). Thus, the importance of an input is determined by its value. For example, when selecting recommendations using several satisfaction criterion (e.g., relevance, popularity, artist affinity), the largest input (the highest satisfaction criterion) is the most important, regardless of whichever specific one it is. The procedure involves three main steps: (i) specifying a quantifier, Q, (ii) generating a set of order weights associated with Q, and (iii) computing the overall evaluation for each alternative (e.g., where, in some embodiments, each alternative is a media content item that could be recommended to a user) by means of the OWA combination function. Given a weighting vector w, the OWA function is:

OWAw⁡(x)=∑i=1n⁢wi⁢x(i)=<w,x↘>,
where w is the weighting vector, x is the input vector, andis the vector obtained from x by arranging its components in non-increasing order (e.g., from the greatest value of x to the least value of x). Note that calculation of the value of the OWA function can be done by using a sort( ) operation. If all weights are equal, OWA becomes an arithmetic mean.

OWA functions are symmetric aggregation functions that allocate weights according to the input value. Thus, OWA can emphasize the largest, the smallest or mid-range inputs (in other words, the weight applied to the highest value need not be the largest weight). Thus, in the OWA aggregation, the weights are not associated with a particular argument but with the ordered position of the arguments. The quantifier is used to generate an OWA weighting vector W of dimension n (e.g., where n is the number of inputs to be weighted). This weighting vector is then used in an OWA aggregation to determine the overall evaluation for each alternative. For each alternative, the argument of this OWA aggregation is the satisfaction of the alternative to each of the criteria (e.g., where the satisfaction of the alternative to each of the criteria is the score for each objective, as discussed elsewhere in this document).

In some embodiments, the quantifier is a Regular Increasing Monotone (RIM) quantifier, which implies that the solution improves as more criteria are satisfied:

The Regular Increasing Monotone (RIM) quantifier can provide information aggregation procedures guided by verbally expressed concepts (e.g., linguistic quantifiers, that are able to express the concept of fuzzy majority: “for all”, “there exists”, “identity”, “most”, “at least half”, “as many as possible”) and a dimension independent description of the desired aggregation. A fuzzy subset Q of the real line is called a Regular Increasing Monotone (RIM) quantifier if Q(0)=0, Q(1)=1 and Q(x)≥Q(y) if x>y.

Some parameterized RIM quantifier families Q(x,α) are discussed where parameter a determines the “or”-ness level of these quantifiers, which serves as the control parameter in the aggregation process. Although the class of proportional quantifiers known as the regular increasing monotone (RIM) quantifiers is described here, it will be understood that other types of quantifiers can also be used. To identify the quantifier some embodiments employ a method for defining a parameterized subset on the unit interval. For example,
Q(p)=pα(α>0)
where Q(p) is represented as a fuzzy set in interval [0, 1]. It can be applied for generating a whole family of the RIM quantifiers. By changing the parameter, α, one can generate different types of quantifiers and associated operators between the two extreme cases of the all and at least one quantifiers. For α=1, Q(p) is proportional to α and therefore it is referred to as the identity quantifier. As α tends to zero, the quantifier Q(p) approaches its extreme case of at least one, which corresponds to the MAX operator. As α tends to infinity, the quantifier Q(p) approaches its extreme case of all, which corresponds to the MIN operator. The OWA operator can be used in multiple levels, so as to form a hierarchical ordering across multiple objectives.

Depending on the value of α, OWA can give high weights to (i) sets of objectives that have many high objective scores (OWA_MANY), which works like an “AND” operator, or (ii) sets of objectives that have any high objective scores (OWA_ANY), which works like an “OR” operator, or anything in between (depending on the value of alpha). For example, for a low value of alpha (e.g., OWA_ANY), a high weight is given to the entire set of objectives if at least one of the objective scores is high. In another example, for a high value of alpha, the OWA calculation acts like an “AND” operator, where the set of objectives is assigned a high weight value only when many of the objective scores are high.

Hierarchical Ordered Weighted Average (HOWA) is an expansion of OWA. Objectives are grouped into different Objective Sets and the weight outputs from OWA calculations (either OWA_MANY, or OWA_ANY) are used as inputs to another OWA calculation. In some embodiments, the Objective Sets are treated as individual objectives. For example, OWA outputs are recursively used as new OWA inputs. The “hierarchical” part of HOWA is where the output of different OWA results are then used as the input to another OWA calculation. This could be many-leveled, althoughFIGS.5A-5Bshow two-leveled examples.

In this way, for a media content provider selecting content to recommend to users, HOWA combines multiple objectives that support user satisfaction (e.g., user-satisfaction, track affinity, and artist affinity) in a way that is more complex than using simple averaging or weighted sums. For example, a user is satisfied with different media content for different reasons, including, familiarity with the content item, familiarity with the artist, how the media content fits with the other media content that is recently played (e.g., in the rest of the playlist), etc. Thus, it is important to score a user's satisfaction (e.g., affinity) with a media content item that accounts for this plurality of reasons (e.g., objectives).

InFIG.5A, a first ordered weighted average is applied to a first set of objectives, resulting in a score (e.g., equal to the calculated OWA) for set 1 objectives512. For example, each objective in the first set of objectives (e.g., objective502, objective504, objective506and objective508) has a value. The objectives are ordered by their value (e.g., from largest value to smallest value). For example, objective502(e.g., user-satisfaction objective) has a value of 0.8, objective504(e.g., noise objective) has a value of 0.3, objective506(e.g., track-affinity objective) has a value of 0.9, and objective508(e.g., artist-affinity objective) has a value of 0.8. Thus, the order, from greatest value to least value, is: objective506, objective502and objective508, and objective504, thus producing an ordered value vector [0.9, 0.8, 0.8, 0.3].

Next, weights are assigned to each objective, based on their order, such that the objective with the largest value also is assigned the first weight (e.g., the largest weight, although that need not be the case), and the objective with the second-to-largest value applies the second weight (e.g., the second-to-largest weight), and so on. For example, where the weight (w), for each position (alternative) i, is calculated using:

An OWA is then calculated for the first set of objectives (e.g., to produce a score for set 1 objectives512). For example, OWA_MANY (e.g., an “AND” operator) is applied to the first set of objectives (e.g., objective502, objective504, objective506and objective508). A score for set 1 objectives512is determined by computing the weighted sum of the OWA weights multiplied by the objective values for the objectives in the first set. Thus, the OWA is calculated by taking the weighted sum of the OWA weights [w1, w2, w3, w4] applied to the value vector [0.9, 0.8, 0.8, 0.3] (e.g., by multiplying each value by its corresponding weight, and then summing the weighted values). By applying an OWA_ANY calculation (e.g., with a high value for a), the resulting OWA (e.g., score) is, for example, 0.89 for the first set of objectives.

As illustrated by this example, each objective in the set is assigned a weight based on its value (e.g., relative to the values of the other objectives), instead of assigning a weight to a particular objective. For example, if the value of an objective is updated (e.g., changed), the order of objectives is also updated, and the weights assigned to each objective is updated accordingly.

The computed score of the first set of objectives (e.g., 0.89) then becomes the value for the first set of objectives when the first set of objectives is used in the second OWA calculation. For example, set 1 objectives512(e.g., with a value of 0.89) becomes an objective within objective set 2 (e.g., which also includes additional objectives510,514, and515).

A second ordered weighted average is applied to the second set of objectives to determine combined objectives516, wherein the second set of objectives includes the result set 1 objectives512and additional objectives (e.g., single objectives), including objective510, objective514, and objective515. In some embodiments, the value of objective510is determined by the machine learning model, described with reference toFIG.4. In some embodiments, the value of objective510is a preset objective (e.g., with a set value) that is determined and provided without using the machine learning model.

The second OWA is then applied to the second set of objectives (e.g., including set 1 objectives512, objective510, objective514, and objective515). For example, an OWA_ANY (“OR”) (e.g., with a low value for a) is calculated for the second set of objectives. To calculate the OWA for the second set of objectives, the objectives in set 2 are now ordered (e.g., from greatest to least), each objective is assigned a weight, and the OWA is calculated by multiplying the respective weight by the respective objective in the second set to output a value (e.g., OWA) of the second set of objectives. For example, set 1 objectives has a value of 0.89 (e.g., as calculated above), objective510has a value of 0.1, objective514has a value of 0.4, and objective515has a value of 0.2. Thus, the ordered value vector, from greatest value to least, comprises: set 1 objectives, objective514, objective515, and objective510, with values [0.89, 0.4, 0.2, 0.1] to be assigned weights [w5, w6, w7, w8]. Because this is an “OWA_ANY” calculation, and at least one objective has a high value, the overall score for combined objectives516is also high with a value of 0.82.

Taking the overall score for the combined objectives as 0.82 for this media content item (e.g., a first media content item), this score is then used by the media content provider to determine whether or not to recommend the first media content item. For example, if the first media content item has a score of 0.82, and a second media content item has a score of 0.6, the media content provider will recommend the first media content item over the second media content item because it has the larger score.

FIG.5Billustrates another example of calculating a hierarchical ordered weighted average (HOWA). For example, a score is calculated for set 1 objectives536by calculating an ordered weighted average of objective520, objective522, objective524, and objective526. A score is calculated for set 2 objectives538by calculating an ordered weighted average of objective528, objective530, objective532, and objective534.

Next, at a second level within the hierarchy of ordered weighted averages, the scores for set 1 objectives536and set 2 objectives538are combined by calculating an OWA of set 1 objectives536and set 2 objectives538. In the second level of the hierarchy, another OWA is calculated by combining the ordered weighted average of set 1 objectives536and the ordered weighted average of set 2 objectives538(and any other objectives or ordered weighted average of other sets of objectives), to determine a score for combined objectives540(e.g., where the score for combined objectives540is the OWA that is calculated using the score of set 1 objectives536and the score of set 2 objectives538). Thus, the ordered weighted average is hierarchical because the system calculates an OWA based on the values for set 1 objectives536and set 2 objectives538, where each of those values were also determined by calculating the OWA for the set 1 objectives536and calculating the OWA for the set 2 objectives538.

It will be understood that for any of the OWA calculations described, OWA_MANY or OWA_ANY may be applied depending on the value chosen for the parameter a.

In some embodiments, the objectives include different types of objectives, such as engagement objectives (e.g., user clicks, complete listens, skips), satisfaction objectives (e.g., hears and hands, percentage streamed), content characteristic objectives (e.g., relevance, user-track genre similarity, artist and genre affinity), and platform objectives (e.g., strategic value of content to platform or artist). These objectives comprise a mix of computed and predicted estimates, with the predicted estimates (e.g. engagement criterion) being the output of sophisticated large-capacity models trained specifically for these tasks (as explained with reference toFIG.4).

In some embodiments, a hierarchical ordered weighted average is computed for each media content item in a plurality of media content items. The score that is calculated (e.g., combined objectives516or combined objectives540) for each media content item is then used to rank the media content items in the plurality of media content items. For example, the media content items with the highest scores are ranked highest.

In some embodiments, the media content items with the highest ranking(s) are selected to be presented to the user. In some embodiments, the plurality of media content items are ordered, for playback, according to the combined objective score (e.g., from the largest score to the smallest score). In some embodiments, the combined objective score for each media content item in the plurality of media content items is calculated using a same hierarchical ordered weighted average structure (e.g., a first OWA calculated using OWA_ANY and a second OWA calculated using OWA_MANY).

FIGS.6A-6Bare flow diagrams illustrating a method600for providing media content to a user, in accordance with some embodiments. Method600may be performed (602) at an electronic device (e.g., electronic device102-1), the electronic device having one or more processors and memory storing instructions for execution by the one or more processors. In some embodiments, the method600is performed by executing instructions stored in the memory (e.g., memory212,FIG.2) of the electronic device. In some embodiments, the method600is performed by a combination of the server system (e.g., including media content server104and CDN106) and an electronic device (e.g., a client device). In some embodiments, the server system provides tracks (e.g., media items) for playback to the electronic device(s)102of the media content delivery system100.

Referring now toFIG.6A, in performing the method600, the electronic device, for each respective media content item of a plurality of media content items (602) receives (604) a representation of the media content item and a user.

In some embodiments, the representation of the media content item comprises (606) a vector describing the media content item. For example, as illustrated inFIG.4, content vector404is a representation of a media content item that is fed as an input into neural network406. In some embodiments, the vector describing the media content item includes information for the media content item such as genre, artist, playlist, tempo, or other identifying or characteristic information of the media content item.

In some embodiments, the representation of the user comprises (608) a vector describing user features determined from a stored playback history of the user. For example, user vector402is a representation of the user that includes information about the user as determined from a playback history of the user and/or a profile (e.g., demographics) of the user. In some embodiments, the representation of the user is updated after each media content item the user consumes (e.g., the playback history is updated to include the media content item). In some embodiments, the representation of the user is determined at the beginning of a playback session for that playback session. For example, the representation of the user includes characteristic related to the user and the playback conditions (e.g., time of day, location, etc.) for the current playback session.

In some embodiments, the electronic device normalizes (610) the representation of the media content item and the user, wherein the normalized representation is used to generate the score for each objective in the hierarchy of objectives. In some embodiments, the normalization is performed such that each objective has a similar distribution of scores (e.g., so that within the weighted average, each objective is more likely to effect the weighted average in a same way).

The electronic device generates (612) a score for each objective in a hierarchy of objectives. Generating the score for each objective comprises using (614) the representation of the media content item and the user as inputs to a first machine learning algorithm, to generate a score for a first objective corresponding to a first level in the hierarchy of the objectives. For example, as illustrated inFIG.4, a score414is generated for a first objective (corresponding to task specific encoder410) within first level400.

In some embodiments, the first level in the hierarchy of objectives includes (616) a plurality of objectives, including the first objective and a third objective. In some embodiments, the first level includes a third objective, such that the third objective and the first objective are in the first level in the hierarchy of objectives, wherein the first objective and the third objective are a same type of objective. In some embodiments, each objective has a corresponding machine learning algorithm (e.g., neural network). In some embodiments, the first level includes a plurality of machine learning algorithms, and a corresponding plurality of objectives determined in the first level. For example, as illustrated inFIG.4, the first level400includes a neural network408, and a task specific encoder410for a first objective (to generate score414for the first objective) and the first level400includes a neural network416, and a task specific encoder418for a third objective (to generate score422for the third objective).

In some embodiments, the electronic device generates (618) a score for the third objective including using an output of a machine learning algorithm of the first objective (without using the score for the first objective). For example, inFIG.4, the score422for the third objective is generated from neural network416, where neural network416takes the output of neural network408as an input. Neural network416does not take score414as an input.

In some embodiments, the first level in the hierarchy of objectives includes (620) a plurality of objectives that comprise predicted objectives (e.g., a skip prediction objective, a track completion objective, etc.).

Generating the score for each objective comprises using (622) an output of the first machine learning algorithm, distinct from the score for the first objective, as an input to a second machine learning algorithm to generate a score for a second objective corresponding to a second level in the hierarchy of objectives. For example, score428is generated in second level401, using neural network423. Neural network423uses (i) an output of neural network416(from the first level) and (ii) an output of neural network406as inputs to the neural network423. Thus, the score428is generated in the second level in the hierarchy of objectives using an output of neural network416as an input (where score422is determined using mathematical function420, which is distinct from the output of neural network416).

In some embodiments, the second level in the hierarchy of objectives includes (624) one or more objectives that comprise satisfaction objectives. For example, a satisfaction objective comprises whether a user will favorite (e.g., heart) a media content item and/or ban (e.g., block) a media content item. In some embodiments, the score for the satisfaction objective represents the likelihood that the user will favorite and/or ban the media content item.

In some embodiments, the first level in the hierarchy of objectives comprises a first predicted objective and the second level in the hierarchy of objectives comprises (626) a second predicted objective. In some embodiments, the electronic device uses an output of the first machine learning algorithm, and an output of the second machine learning algorithm as inputs to a third machine learning algorithm to generate a score for a third objective corresponding to a third level in the hierarchy of objectives. In some embodiments, generating the respective score between the user and the media content item uses the score for the first objective, the score for the second objective and the score for the third objective. For example, each level in the hierarchy comprises a single objective.

The first machine learning algorithm and the second machine learning algorithm are trained (628) using back-propagation from the second machine learning algorithm to the first machine learning algorithm. The electronic device generates (630) a respective score between the user and the media content item using the score for the first objective and the score for the second objective.

In some embodiments, the electronic device receives (632) a score for a fourth objective, and generating the respective score between the user and the media content item uses the score for the first objective, the score for the second objective and the score for the third objective. In some embodiments, the score for the fourth objective comprises an estimated objective. In some embodiments, an estimated objective is an objective that has a score that is not determined using the machine learning algorithm (e.g., neural networks).

In some embodiments, generating the respective score between the user and the media content item comprises (634) computing an ordered weighted average using the score for the first objective and the score for the second objective. For example, as explained with reference toFIGS.5A-5B, the scores of the plurality of objectives are combined using ordered weighted averaging to generate a single value (e.g., to represent an affinity between the user and the media content item).

The electronic device provides (636) media content to the user based on the respective scores between the user and the media content items in the plurality of media content items. For example, providing the media content comprises selecting media content based on the respective scores, as described with reference to steps730-734inFIG.7B.

AlthoughFIGS.6A-6Billustrate a number of logical stages in a particular order, stages which are not order dependent may be reordered and other stages may be combined or broken out. Some reordering or other groupings not specifically mentioned will be apparent to those of ordinary skill in the art, so the ordering and groupings presented herein are not exhaustive. Moreover, it should be recognized that the stages could be implemented in hardware, firmware, software, or any combination thereof.

FIGS.7A-7Bare flow diagrams illustrating a method700for providing media content to a user, in accordance with some embodiments. Method700may be performed (502) at an electronic device (e.g., electronic device102-1), the electronic device having one or more processors and memory storing instructions for execution by the one or more processors. In some embodiments, the method700is performed by executing instructions stored in the memory (e.g., memory212,FIG.2) of the electronic device. In some embodiments, the method700is performed by a combination of the server system (e.g., including media content server104and CDN106) and an electronic device (e.g., a client device). In some embodiments, the server system provides tracks (e.g., media items) for playback to the electronic device(s)102of the media content delivery system100.

Referring now toFIG.7A, the electronic device performs the method700for each media content item of a plurality of media content items (702). The electronic device receives (704) a respective score for each of a first set of objectives and a second set of objectives.

In some embodiments, the first set of objectives includes (706) at least one predicted objective and at least one measured objective. In some embodiments, the at least one predicted objective is determined (708) using a machine learning algorithm.

The electronic device generates (710) a respective score between the user and the media content item. For example, a combined score (e.g., a score for combined objectives516or a score for combined objectives540) is generated for each media content item of the plurality of media content items.

Generating the respective score includes applying (712) a first ordered weighted average to the respective scores for the first set of objectives, to produce a first combined score for the first set of objectives. For example, as illustrated inFIG.5B, a first OWA is applied to a first set of objectives (e.g., objectives520,522,524and526) to generate a first combined score for set 1 objectives536.

Generating the respective score includes applying (714) a second ordered weighted average to the respective scores for the second set of objectives, to produce a second combined score for the second set of objectives. For example, as illustrated inFIG.5B, a second OWA is applied to a second set of objectives (e.g., objectives528,530,532, and534) to produce a second combined score for set 2 objectives538.

In some embodiments, applying the first ordered weighted average comprises (716) using a first parameter to determine weights applied to the respective scores of the first set of objectives. In some embodiments, applying the second ordered weighted averaged comprising using a second parameter, having a different value than the first parameter, to determine weights applied to the respective scores of the second set of objectives. For example, as explained above, the parameter a is updated to change the calculated quantifier, Q(p), which controls whether the OWA will be calculated using an AND operator (e.g., OWA_MANY) or an OR operator (e.g., OWA_ANY). In some embodiments, a first level in the HOWA function uses a first operator (e.g., AND operator) and a second level in the HOWA function uses a second operation (e.g., OR operator). It will be understood that different operators can be applied to different levels of the HOWA function, or a same operator can be applied to each level of the HOWA function.

In some embodiments, the weights applied to each objective in a respective set of objectives are not equal (e.g., not an arithmetic mean).

Generating the respective score includes applying (718) a third ordered weighted average to the combined score for the first set of objectives and the combined score for the second set of objectives. For example, as illustrated inFIG.5B, a third OWA is applied to combine the score for set 1 objectives536and set 2 objectives538to generate the score for combined objectives540.

In some embodiments, applying a respective ordered weighted average for a respective set of objectives comprises (720) determining, in real time, weights for each objective in the set of objectives. For example, as explained above, the weight for each objective is dynamically calculated based on the value assigned to the respective objective. For example, the assigned weight is based on the respective value for the respective objective (e.g., rather than a particular weight always being assigned to a particular objective). In this way, in some embodiments, each objective in the set of objectives that is combined using the ordered weighted average is dynamically weighted such that a same objective is assigned a different weight for each media content item (e.g., the weight for a respective objective is not fixed). For example, an affinity objective is assigned a higher weight for a first media content item and the same affinity objective is assigned a lower weight for a second media content item. In some embodiments, the weights are not manually specified, and the scores are estimated in real-time (dynamically) based on the contextual predictions of objectives (e.g., where the value of the objective is determined by contextual predictions, such as the machine learning algorithm described with reference toFIG.4).

In some embodiments, applying the third ordered weighted average comprises (724) applying the third ordered weighted average to the combined score for the first set of objectives, the combined score for the second set of objectives and a combined score for a third set of objectives. For example, a plurality of objectives are combined in a same level (e.g., 3 scores are combined in 1 level), as illustrated inFIG.5A.

In some embodiments, applying the third ordered weighted average to the combined score for the first set of objectives and the combined score for the second set of objectives produces (726) a third combined score. In some embodiments, the electronic device applies a fourth ordered weighted average to the third combined score and a fourth combined score. For example, as illustrated inFIG.5B, a second level in the hierarchy uses the output of the first level (i.e., the third combined score) and another score.

The electronic device provides (730) media content to the user based on the respective scores between the user and the media content items in the plurality of media content items. In some embodiments, the media content is provided to the user by changing an order of media content items to be presented for a radio session, a playlist, or a shuffle session. In some embodiments, providing media content to the user comprises (732) providing a first media content item having a respective score that satisfies a first threshold. In some embodiments, the first threshold is defined as a score such that a predetermined number of media content items are provided. For example, a predetermined number of media content items that have the highest score(s) are provided (e.g., the top 10 highest scoring media content items are provided).

In some embodiments, providing the media content comprises determining a predefined number of media content items with the highest score(s) (as determined by the method described inFIGS.7A-7B) and determining one or more media content items having a highest value for a particular objective (e.g., as determined by the method described inFIGS.6A-6B, or where the value for the particular objective is a preset value). For example, the electronic device identifies a respective objective and determines, for each media content item in the plurality of media content items, the score (e.g., value) for the respective objective (e.g., wherein the score for the respective objective is generated as described with reference toFIGS.6A-6B). The one or more media content items are selected based on the score for the respective objective. In this way, providing the media content comprises selecting media content items based on the respective scores between the user and the media content items in the plurality of media content items and based on the respective score for a respective objective for one or more media content items.

In some embodiments, providing the media content comprises (734) determining an order of media content items to present to the user (e.g., automatically without receiving user input). In some embodiments, providing the media content comprises, within a playlist that includes a plurality of media content items, reordering the plurality of media content items such that the media content items with the highest score(s) are played first (e.g., the media content items are presented in order from highest scoring to lowest scoring). In some embodiments, the user does not select a next media content item to be played. For example, when the media content is provided during a radio session, the user does not select the next media content item to be played (e.g., streamed). In some embodiments, the media content is provided at the end of a playlist by automatically playing additional media content (e.g., that is not in the playlist) such that the user experience is not interrupted.