Patent ID: 12244898

DETAILED DESCRIPTION

The following description contains specific information pertaining to implementations in the present disclosure. One skilled in the art will recognize that the present disclosure may be implemented in a manner different from that specifically discussed herein. The drawings in the present application and their accompanying detailed description are directed to merely exemplary implementations. Unless noted otherwise, like or corresponding elements among the figures may be indicated by like or corresponding reference numerals. Moreover, the drawings and illustrations in the present application are generally not to scale, and are not intended to correspond to actual relative dimensions.

The present application discloses automated systems and methods for recommending content using a metadata based content map that address and overcome the deficiencies in the conventional art. By utilizing deep metadata describing content, and consumption history data specific to a user or a demographic associated with the user, the present application discloses an automated content recommendation solution capable of identifying content items likely to be desirable to the user. In addition, by generating a networked map of nodes corresponding respectively to the desirable content items in which distances between nodes are based on similarity of metadata amongst the content items corresponding to the nodes, the automated content recommendation solution disclosed herein advantageously surfaces content that the user may be unaware of. Moreover, by providing the networked map to the user via a user interface through which the nodes of the networked map are displayed as respective thumbnail images selectable by the user, the present content recommendation solution enables the user to navigate intuitively among the displayed content items.

It is noted that, as used in the present application, the terms “automation,” “automated”, and “automating” refer to systems and processes that do not require the participation of a human editor or curator. Although, in some implementations, a human editor or curator may review a recommendation made by the automated systems and according to the automated methods described herein, that human involvement is optional. Thus, the methods described in the present application may be performed under the control of hardware processing components of the disclosed automated systems.

FIG.1shows an exemplary system for performing automated content recommendation using a metadata based content map, according to one implementation. As shown inFIG.1, content recommendation system100includes computing platform102having hardware processor104and system memory106implemented as a non-transitory storage device. According to the present exemplary implementation, system memory106stores content surfacing software code120including genome mapping module130, user interface122provided by content surfacing software code120, content library110, and user consumption profile database112. Also shown inFIG.1are media playout window124, metadata based content map132, content recommendation window126, and character biography window128of user interface122, as well as user consumption profile114stored in user consumption profile database112.

As further shown inFIG.1, content recommendation system100is implemented within a use environment including communication network108, personal communication device150including display158, and user140of personal communication device150. In addition,FIG.1shows network communication links118of communication network108interactively connecting personal communication device150with content recommendation system100. Also shown inFIG.1are initiation signal142, content selection data144, and content rejection data146. It is noted that user consumption profile114includes a consumption profile for user140.

It is further noted that, although the present application refers to content surfacing software code120as being stored in system memory106for conceptual clarity, more generally, system memory106may take the form of any computer-readable non-transitory storage medium. The expression “computer-readable non-transitory storage medium,” as used in the present application, refers to any medium, excluding a carrier wave or other transitory signal that provides instructions to hardware processor104of computing platform102. Thus, a computer-readable non-transitory medium may correspond to various types of media, such as volatile media and non-volatile media, for example. Volatile media may include dynamic memory, such as dynamic random access memory (dynamic RAM), while non-volatile memory may include optical, magnetic, or electrostatic storage devices. Common forms of computer-readable non-transitory media include, for example, optical discs, RAM, programmable read-only memory (PROM), erasable PROM (EPROM), and FLASH memory.

It is also noted that althoughFIG.1depicts content surfacing software code120, content library110, and user consumption profile database112as being co-located in system memory106, that representation is merely provided as an aid to conceptual clarity. More generally, content recommendation system100may include one or more computing platforms102, such as computer servers for example, which may be co-located, or may form an interactively linked but distributed system, such as a cloud based system, for instance. As a result, hardware processor104and system memory106may correspond to distributed processor and memory resources within content recommendation system100.

According to the implementation shown byFIG.1, user140may utilize personal communication device150to interact with content recommendation system100over communication network108. In one such implementation, computing platform102may correspond to one or more web servers, accessible over a packet-switched network such as the Internet, for example. Alternatively, computing platform102may correspond to one or more computer servers supporting a local area network (LAN), or included in another type of limited distribution network.

Although personal communication device150is shown as a mobile computing device such as a smartphone or tablet computer inFIG.1, that representation is also provided merely as an example. More generally, personal communication device150may be any suitable mobile or stationary personal communication device or system that implements data processing capabilities sufficient to support connections to communication network108, and implement the functionality ascribed to personal communication device150herein. For example, in other implementations, personal communication device150may take the form of a desktop computer, a laptop computer, a smart TV, a digital media player, or a gaming console, for example.

User140, who may be a consumer of media content such as movies, television (TV) programming content, or video games, for example, may utilize personal communication device150to interact with content recommendation system100via user interface122. For example, user140may utilize media playout window124of user interface122to view content from content library110selected by user140via user interface122and rendered on display158of personal communication device150. Display158of personal communication device150may take the form of a liquid crystal display (LCD), a light-emitting diode (LED) display, an organic light-emitting diode (OLED) display, or another suitable display screen that performs a physical transformation of signals to light.

FIG.2shows a more detailed representation of exemplary personal communication device250in combination with computing platform202implemented as a computer server. As shown inFIG.2, personal communication device250is communicatively coupled to computing platform202over network communication link218. Computing platform202includes hardware processor204, and system memory206storing content surfacing software code220aincluding genome mapping module230aand providing user interface222a, content library210, and user consumption profile database212including user consumption profile214.

As further shown inFIG.2, personal communication device250includes hardware processor254, memory256implemented as a non-transitory storage device storing content surfacing software code220bincluding genome mapping module230b, and user interface222bprovided by content surfacing software code220b. As also shown inFIG.2, personal communication device250includes transceiver260, and display258for rendering user interface222b.

Network communication link218and computing platform202having hardware processor204and system memory206, correspond respectively in general to network communication link118and computing platform102having hardware processor104and system memory106, inFIG.1. In addition, content surfacing software code220aincluding genome mapping module230a, and user interface222a, inFIG.2, correspond respectively in general to content surfacing software code120including genome mapping module130, and user interface122, inFIG.1. In other words, content surfacing software code220aincluding genome mapping module230a, and user interface222amay share any of the characteristics attributed to respective content surfacing software code120including genome mapping module130, and user interface122by the present disclosure, and vice versa.

It is also noted that content library210and user consumption profile database212including user consumption profile214, inFIG.2, correspond respectively in general to content library110and user consumption profile database112including user consumption profile114, inFIG.1. Moreover, media playout window224a, metadata based content map232a, content recommendation window226a, and character biography window228aof user interface222acorrespond respectively to media playout window124, metadata based content map132, content recommendation window126, and character biography window128. Thus, media playout window224a, metadata based content map232a, content recommendation window226a, and character biography window228amay share any of the characteristics attributed to respective media playout window124, metadata based content map132, content recommendation window126, and character biography window128by the present disclosure, and vice versa.

Personal communication device250and display258correspond in general to personal communication device150and display158, inFIG.1, and those corresponding features may share any of the characteristics attributed to either corresponding feature by the present disclosure. Thus, like personal communication device150, personal communication device250may take the form of a smartphone, tablet computer, desktop computer, laptop computer, smart TV, digital media player, or gaming console, for example. In addition, and although not shown inFIG.1, personal communication device150may include features corresponding to hardware processor254, transceiver260, memory256storing content surfacing software code220bincluding genome mapping module230b, and user interface222bprovided by content surfacing software code220b. Moreover, like display158, display258may be implemented as an LCD, an LED display, an OLED display, or any other suitable display screen that performs a physical transformation of signals to light.

With respect to content surfacing software code220b, it is noted that in some implementations, content surfacing software code220bmay be a direct-to-consumer application providing user interface222bfor exchanging data, such as data corresponding to initiation signal142, content selection data144, and content rejection data146with computing platform102/202. In some of those implementations, for example, content surfacing software code220bmay not include genome mapping module230b.

However, in other implementations, content surfacing software code220bmay be an application including all of the features of content surfacing software code120/220a, and may be capable of executing all of the same functionality. That is to say, in some implementations, content surfacing software code220bcorresponds to content surfacing software code120/220aand may share any of the characteristics attributed to those corresponding features by the present disclosure.

Furthermore, media playout window224b, metadata based content map232b, content recommendation window226b, and character biography window228bof user interface222bcorrespond respectively to media playout window124/224a, metadata based content map132/232a, content recommendation window126/226a, and character biography window128/228a. Thus, media playout window224b, metadata based content map232b, content recommendation window226b, and character biography window228bmay share any of the characteristics attributed to respective media playout window124/224a, metadata based content map132/232a, content recommendation window126/226a, and character biography window128/228aby the present disclosure, and vice versa.

According to the exemplary implementation shown inFIG.2, content surfacing software code220bis located in memory256, having been received via network communication link118/218, either from computing platform102/202or an authorized third party source of content surfacing software code220b. In one implementation, network communication link118/218corresponds to transfer of content surfacing software code220bover a packet-switched network, for example.

Once transferred, for instance by being downloaded over network communication link118/218, content surfacing software code220bmay be persistently stored in memory256, and content surfacing software code220bmay be executed on personal communication device150/250by hardware processor254. Hardware processor254may be the central processing unit (CPU) for personal communication device150/250, for example, in which role hardware processor254runs the operating system for personal communication device150/250and executes content surfacing software code220b. Thus, in some implementations, the computing platform for performing automated content recommendation using a metadata based content map may be part of personal communication device150/250.

FIG.3shows exemplary user interface322provided by content surfacing software code120/220a/220b, according to one implementation. As shown inFIG.3exemplary user interface322is displaying metadata based content map332as a networked map of nodes displayed as thumbnail images depicting content items identified by content surfacing software code120/220a/220bas desirable to user140. Example thumbnail images corresponding to nodes of networked metadata based content map322(hereinafter “nodes/thumbnail images”) are identified by reference numbers362a,362b,362c, and362d. In addition, networked metadata based content map332displays story archetypes classifying the desirable content items and represented by exemplary “deception” story archetype364a, “crime” story archetype364b, and “romance” story archetype364c. Also shown inFIG.3are mapping network links associating different desirable content items with one another, as well as the story archetypes with which they can be classified.

It is noted that the distances between nodes/thumbnail images corresponding respectively to content items identified as desirable to user and displayed on metadata based content map332may be based on the similarity of the metadata describing each of the nodes. For example, distance366acbetween node/thumbnail image362aand node/thumbnail image362ccorresponds to the similarity between the metadata describing the desirable content items corresponding respectively to nodes/thumbnail images362aand362c.

As a specific example, nodes/thumbnail images362band362dare both associated with the genome metadata story archetype “crime”364b. In addition, nodes/thumbnail images362band362dare both associated with heroic character archetypes having similar character motivations. As a result, nodes/thumbnail images362band362dare closely located to one another on metadata based content map332, as shown by distance366bd.

Moreover, although all three of nodes/thumbnail images362a,362b, and362dare associated with the genome metadata story archetype “crime”364b, nodes/thumbnail images362aand362dare more closely associated with the genome metadata story archetype “deception”364a, as well as with female heroic characters archetypes than is node/thumbnail image362b. Consequently, distance366adseparating nodes/thumbnail images362aand362dis less than distance366abseparating nodes/thumbnail images362aand362b. That is to say, nodes/thumbnail images362aand362dare closer to one another than are nodes/thumbnail images362aand362b.

As yet another example, although nodes/thumbnail images362aand362cshare an association with the genome metadata story archetype “deception”364a, as well as with metadata describing female lead characters, their character archetypes may be different, e.g., heroic vs. villainous. In addition, the character motivations associated with nodes/thumbnail images362aand362bmay be quite different, resulting in distance366acbeing greater than either of distances366ador event366ab.

It is further noted that although the nodes/thumbnail images shown inFIG.3are depicted as having the same size, that representation is merely exemplary. In some implementations, the nodes/thumbnail images displayed on metadata based content map332may be sized according to their predicted desirability to user140. That is to say, the more desirable a particular content item is predicted to be to user140, the larger may be its thumbnail on metadata based content map332.

User interface322and networked metadata based content map332correspond respectively in general to user interface122/222a/222band metadata based content map132/232a/232b, inFIGS.1and2. That is to say, user interface322and networked metadata based content map332may share any of the characteristics attributed to respective user interface122/222a/222band metadata based content map132/232a/232bby the present disclosure, and vice versa.

Referring toFIGS.4A and4B,FIG.4Ashows exemplary content recommendation window426A of user interface422, according to one implementation, whileFIG.4Bshows exemplary content recommendation window426B of user interface422, according to another implementation. Exemplary content recommendation window426A displays metadata associated with the content item corresponding to node/thumbnail image362aof networked metadata based content map132/232a/232b/332. Examples of such metadata include content item title460, thumbnail image462aassociated with the content item, and synopsis468of the recommended content item. Similarly, exemplary content recommendation window426B displays metadata associated the content item corresponding to node/thumbnail image362bof networked metadata based content map132/232a/232b/332, such as content item title460, thumbnail image462b, and synopsis468.

In addition, each of exemplary content recommendation windows426A and426B displays exemplary genome metadata such as “story archetype” tag or tags468associated with the recommended content item. Each of exemplary content recommendation windows426A and426B also displays selection options “add to my list”444aand “see the series”444b, as well as rejection option446. Exemplary content recommendation window426B further displays related content links472to more detailed descriptions of other popular content items that may be desirable to user140.

User interface422corresponds in general to user interface122/222a/222b/322, inFIGS.1,2, and3. Consequently user interface422may share any of the characteristics attributed to user interface122/222a/222b/322by the present disclosure, and vice versa. Each of content recommendation windows426A and426bcorresponds in general to content recommendation window126/226a/226b, inFIGS.1and2. Thus, content recommendation window126/226a/226bmay share any of the features shown and described by reference toFIGS.4A and4B.

It is noted that selection options444aand444b, inFIGS.4A and4B, can correspond in general to selection data144, inFIG.1, while rejection option446can correspond in general to rejection data146. It is further noted that thumbnail images462aand462bcorrespond respectively in general to nodes/thumbnail images362aand362bincluded on metadata based content map132/232a/232b/332.

FIG.5shows user interface522displaying exemplary character biography window528, according to one implementation. As shown inFIG.5, character biography window528is dedicated to a single character574and displays metadata identifying character574, metadata identifying title560of the content item including the character, and metadata providing brief description580of the character. In addition, exemplary character biography window528displays genome metadata in the form of character archetype tag “hero”576and character motivation tags578associated with character574. Exemplary character biography window528also identifies other characters582associated with similar genome metadata tags.

User interface522corresponds in general to user interface122/222a/222b/322/422, inFIGS.1,2,3, and4. Consequently user interface522may share any of the characteristics attributed to user interface122/222a/222b/322/422by the present disclosure, and vice versa. Character biography window528corresponds in general to character biography window128/228a/228b, inFIGS.1and2. Thus, character biography window128/228a/228bmay share any of the features shown and described by reference toFIG.5.

The functionality of content surfacing software code120/220a/220bwill be further described by reference toFIG.6.FIG.6shows flowchart690presenting an exemplary method for use by a system, such as content recommendation system100, or personal communication device150/250, for performing automated content recommendation using a metadata based content map. With respect to the method outlined inFIG.6, it is noted that certain details and features have been left out of flowchart690in order not to obscure the discussion of the inventive features in the present application.

Referring toFIG.6, with further reference toFIGS.1and2, flowchart690begins with receiving initiation signal142identifying user140(action692). For example, in one implementation, initiation signal142may correspond to selection of an application icon corresponding to content surfacing software code120/220a/220b, either resident on personal communication device150/250or accessible via communication network108and network communication links118/218, that initiates execution of content surfacing software code120/220a/220b.

As shown byFIGS.1and2, in one implementation, initiation signal142may be transmitted by personal communication device150/250, using transceiver260, to computing platform102/202via network108and network communication links118/218. In those implementations, initiation signal142may be received by content surfacing software code120/220a, executed by hardware processor104/204of computing platform102/202. However, in other implementations, initiation signal142may be received by content surfacing software code220b, executed by hardware processor254of personal communication device150/250, as an input to user interface222bprovided by user140.

Flowchart690continues with, in response to receiving initiation signal142, identifying multiple content items as desirable content items to user140, based on user140and metadata describing each of the desirable content items (action694). The content items identified as desirable to user140may take a variety of forms. For instance, those content items may be audio-visual content, such as a movie, a TV series, a single episode of TV programming content, or a video game, for example.

In use cases in which user consumption profile database112/212includes a consumption history specific to user140, i.e., user consumption profile114/214, identification of content items as desirable content items to user140may be performed by reference to user consumption profile114/214. Moreover, in some implementations, content items may be identified as desirable content items to user140based on one or more of a character archetype, a character motivation, and a story archetype of each of the content items. In implementations in which initiation signal142is received by content surfacing software code120/220aon computing platform102/202, identification of desirable content items to user140may be performed by content surfacing software code120/220a, executed by hardware processor104/204, using user consumption profile114/214.

In implementations in which initiation signal142is received by content surfacing software code220bvia user interface222b, content surfacing software code220bmay access user consumption profile database112/212on computing platform102/202using transceiver260, communication network108, and network communication links118/218. In those implementations, identification of desirable content items to user140may be performed by content surfacing software code220b, executed by hardware processor254of personal communication device150/250, and using user consumption profile114/214.

In use cases in which user consumption profile database112/212does not include user consumption profile114/214specific to user140, identification of desirable content items to user140may be performed using collaborative filtering recommendation techniques. That is to say, in some implementations, identification of desirable content items to user140may be performed by reference to a consumption profile of a demographic of content consumers determined to be similar to user140. For example, even without access to user consumption profile114/214specific to user140, a preliminary identification of content items likely to be desirable to user140may be based on information such as the age and gender of user140and usage data for that portion of a known content consumer population having a similar age and the same gender.

In some implementations, a preliminary identification of desirable content to user140may be based on the geographic region in which user140resides, as well as the nature of personal communication device150/250. For example, where personal communication device150/250is a gaming console, reference to a demographic of content consumer population that utilizes gaming consoles may reveal that video game content is more likely to be desirable to user140than movie or TV content.

Referring toFIG.3in combination withFIGS.1,2, and6, flowchart690continues with generating networked metadata based content map132/232a/232b/332of nodes corresponding respectively to the desirable content items identified in action694(action696). As discussed above, the distance between nodes corresponding respectively to content items identified as desirable to user140may be based on the similarity of the metadata describing each of the nodes. For example, distance366acbetween the node/thumbnail image362aand node/thumbnail image362ccorresponds to the similarity between the metadata describing the desirable content items corresponding respectively to nodes/thumbnail images362aand362c.

As noted above, the desirable content items included on networked metadata based content map132/232a/232b/332may take a variety of forms, such as movies, TV programming content, and video games, for example. In use cases in which the desirable content items included on metadata based content map132/232a/232b/332are movies or TV programs, for example, the similarity in metadata describing the desirable content items and used to determine distances between desirable content items on networked metadata based content map132/232a/232b/332may be evaluated and determined per storyline of each desirable content item.

In implementations in which desirable content items to user140are identified in action694by content surfacing software code120/220a, generation of networked metadata based content map132/232a/232b/332may also be performed by content surfacing software code120/220a, executed by hardware processor104/204, and using genome mapping module130/230a. However, in implementations in which desirable content items to user140are identified in action694by content surfacing software code220b, generation of networked metadata based content map132/232a/232b/332may be performed by content surfacing software code220b, executed by hardware processor254, and using genome mapping module230b.

Flowchart690can conclude with outputting networked metadata based content map132/232a/232b/332for display to user140via user interface122/222a/222b/322(action698). It is noted that the nodes of networked metadata based content map132/232a/232b/332are displayed as respective thumbnail images depicting the desirable content items corresponding respectively to each node, as shown by exemplary nodes/thumbnail images362a,362b,362c, and362dinFIG.3. It is further noted that each node/thumbnail image included on networked metadata based content map132/232a/232b/332is selectable by user140and may enable user140to navigate to one or more user interface windows dedicated to the desirable content item corresponding to the selected node/thumbnail image.

As also shown by exemplary nodes/thumbnail images362a,362b,362c, and362d, in some implementations, each of the nodes/thumbnail images may depict a character from the desirable content item corresponding respectively to each node, such as a character from a movie, or a character from a TV program. Furthermore, and as discussed above, in some implementations, the thumbnail images displayed on networked metadata based content map132/232a/232b/332may be sized according to their predicted desirability to user140. That is to say, a desirable content item predicted to be more desirable to user140than other desirable content items may be represented on networked metadata based content map132/232a/232b/332by a thumbnail that is larger than thumbnail corresponding to other, less desirable content items.

Alternatively, or in addition, in some implementations, the thumbnail images displayed on networked metadata based content map132/232a/232b/332may be centered or otherwise located on metadata based content map132/232a/232b/332according to their predicted desirability to user140. For example, a desirable content item predicted to be more desirable to user140than other desirable content items may be represented by a thumbnail image located closer to the center of networked metadata based content map132/232a/232b/332than thumbnail images corresponding to other, less desirable content items.

In implementations in which generation of networked metadata based content map132/232a/232b/332is performed in action696by content surfacing software code120/220a, networked metadata based content map132/232a/232b/332may be output for display to user140via user interface122/222a/222b/322by content surfacing software code120/220a, executed by hardware processor104/204. However, in implementations in which generation of networked metadata based content map132/232a/232b/332is performed in action696by content surfacing software code220b, networked metadata based content map132/232a/232b/332may be output for display to user140via user interface122/222a/222b/322by content surfacing software code220b, executed by hardware processor254. Moreover, hardware processor254of personal communication device150/250may be further configured to render networked metadata based content map132/232a/232b/332on display158/258.

Thus, when user140opens an application corresponding to content surfacing software code120/220a/220b, user140generates initiation signal142. As a result, user140can advantageously be presented with networked metadata based content map132a/232a/232b/332displaying nodes/thumbnail images corresponding respectively to content items identified as desirable to user140. Identification of the desirable content items is based on metadata, including genome metadata such as story archetype, character archetype, and character motivations associated with each content item. Moreover, user140may select any of the thumbnail images included on networked metadata based content map132/232a/232b/332and may thereby navigate to one or more user interface windows dedicated to the desirable content item corresponding to the thumbnail image.

For example, selection of node/thumbnail image362aof networked metadata based content map132/232a/232b/332by user140may result in navigation to content recommendation window426A. As discussed above, content recommendation window426A describes the desirable content item corresponding to thumbnail image362a/462a, and enables user140to further select the desirable content item using one of selection options444aor444b, or to reject the content item as undesirable using rejection option446.

Alternatively, selection of node/thumbnail image362bof networked metadata based content map132/232a/232b/332by user140may result in navigation to content recommendation window426B. Content recommendation window426B describes the desirable content item corresponding to thumbnail image362b/462b, and enables user140to further select the desirable content item using one of selection options444aor444b, or to reject the content item as undesirable using rejection option446.

Thus, in some implementations, content surfacing software code120/220amay be executed by hardware processor104/204of computing platform102/202to receive content selection data144identifying one of the desirable content items corresponding respectively to thumbnail images362aor362bas a selected content item. In those implementations, hardware processor104/204may further execute content surfacing software code120/220ato navigate to a respective one of content recommendation windows426A or426B of user interface422enabling sampling of the selected content item by user144, for example via selection option444b.

In other implementations, content surfacing software code220bmay be executed by hardware processor254of personal communication device150/250to receive content selection data144identifying one of the desirable content items corresponding respectively to thumbnail images362aor362bas an input to personal communication device150/250by user140. In those implementations, hardware processor254may further execute content surfacing software code120/220ato navigate to a respective one of content recommendation windows426A or426B of user interface422enabling sampling of the selected content item by user144, for example via selection option444b.

As another example, selection of another thumbnail image of networked metadata based content map132/232a/232b/332by user140may result in navigation to character biography window528. As discussed above, character biography window528profiles character574by providing brief description580of character574, as well as displaying genome metadata in the form of character archetype tag576and character motivation tags578associated with character574. Exemplary character biography window528also enables identification of one or more other characters582in other content items having character traits similar to character574, i.e., other characters associated with similar genome metadata tags.

Thus, in some implementations, content surfacing software code120/220amay be executed by hardware processor104/204of computing platform102/202to receive content selection data144and navigate to character biography window528. In other implementations, content surfacing software code220bmay be executed by hardware processor254of personal communication device150/250to receive content selection data144and navigate to character biography window528.

In some implementations, receipt of content selection data144/444a/444bor content rejection data146/446by content surfacing software code120/220a/220bmay cause content surfacing software code120/220a/220bto use genome mapping module130/230a/230bto dynamically rearrange networked metadata based content map132/232a/232b/332in response. For example, where a desirable content item initially identified as less desirable than other content items is selected by user140via selection option444aor444b, networked metadata based content map132/232a/232b/332may be dynamically rearranged through enlargement and/or relocation of the thumbnail image corresponding to the selected content item. Moreover, other desirable content items having similar metadata tags may also have their corresponding thumbnail images analogously enlarged and/or relocated.

By contrast, where a content item initially identified desirable and included on networked metadata based content map132/232a/232b/332is rejected by user140via rejection option446, networked metadata based content map132/232a/232b/332may be dynamically rearranged through removal, reduction in size, and/or relocation of the thumbnail image corresponding to the rejected content item. Moreover, other content items having similar metadata tags may also have their corresponding thumbnail images analogously removed, reduced in size, and/or relocated.

As another example, where a node/thumbnail image corresponding to a desirable content item is dragged or otherwise relocated to a new location on networked metadata based content map132/232a/232b/332, or even off of networked metadata based content map132/232a/232b/332by an input to user interface122/222a/222b/322by user140, networked metadata based content map132/232a/232b/332may be dynamically rearranged in response. For instance, depending on the nature of the relocation, e.g., from the periphery towards to center, or from the center towards the periphery or off of networked metadata based content map132/232a/232b/332entirely, the node/thumbnail image may be enlarged, reduced in size, or may disappear. Moreover, other desirable content items having similar metadata tags may also have their corresponding thumbnail images analogously enlarged, reduced in size, relocated, or removed.

In some implementations, hardware processor104/204or254may further execute respective content surfacing software code120/220aor210bto improve its performance through machine learning. For example, content surfacing software code120/220a/220bmay track inputs to user interface122/222a/222b/322/422/522by user140and record which content items are selected, which are rejected, and which are ignored. That information can be used as feedback to content surfacing software code120/220a/220bincluding genome mapping module130/230a/230b, to better learn the content consumption preferences of user140.

Thus, the present application discloses automated systems and methods for recommending content using a metadata based content map. By utilizing deep metadata including genome metadata describing content, and consumption history data specific to a user or a demographic associated with the user, the present application discloses an automated content recommendation solution capable of identifying content items likely to be desirable to the user. By generating a networked metadata based content map of nodes corresponding respectively to the content items in which distances between nodes are based on similarity of metadata amongst the content items corresponding to the nodes, the automated content recommendation solution disclosed herein advantageously surfaces desirable content that the user may be unaware of. Moreover, by providing the networked metadata based content map to the user via a user interface through which the nodes of the networked map are displayed as respective thumbnail images selectable by the user, the present content recommendation solution enables the user to navigate intuitively among the displayed content items.

From the above description it is manifest that various techniques can be used for implementing the concepts described in the present application without departing from the scope of those concepts. Moreover, while the concepts have been described with specific reference to certain implementations, a person of ordinary skill in the art would recognize that changes can be made in form and detail without departing from the scope of those concepts. As such, the described implementations are to be considered in all respects as illustrative and not restrictive. It should also be understood that the present application is not limited to the particular implementations described herein, but many rearrangements, modifications, and substitutions are possible without departing from the scope of the present disclosure.