Abstract:
Techniques disclosed herein describe inferring user interests based on metadata of a plurality of multimedia objects captured by a plurality of users. An analysis tool receives, for each of the users, metadata describing each multimedia object in the plurality of objects associated with that user. Each multimedia object includes one or more attributes imputed to that object based on the metadata. The analysis tool identifies one or more concepts from the one or more attributes. Each concept includes at least a first attribute that co-occurs with a second attribute imputed to a first multimedia object. The analysis tool associates a first one of the plurality of users with at least one of the concepts based on the attributes imputed to multimedia objects associated with the first one of the plurality of users.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. Provisional Application No. 62/093,372, filed Dec. 17, 2014. The content of the aforementioned application is incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Field 
     Embodiments of the present disclosure generally relate to data analytics. More specifically, embodiments presented herein relate to generating a learning model of user interests based on image or video metadata. 
     Description of the Related Art 
     Individuals take images to capture personal experiences and events. The images can represent mementos of various times and places experienced in an individual&#39;s life. 
     In addition, mobile devices (e.g., smart phones, tablets, etc.) allow individuals to easily capture both digital images as well as record video. For instance, cameras in mobile devices have steadily improved in quality and are can capture high-resolution images. Further, mobile devices now commonly have a storage capacity that can store thousands of images. And because individuals carry smart phones around with them, they can capture images and videos virtually anywhere. 
     This has resulted in an explosion of multimedia content, as virtually anyone can capture and share digital images and videos via text message, image services, social media, video services, and the like. This volume of digital multimedia, now readily available, provides a variety of information. 
     SUMMARY 
     One embodiment presented herein describes a method for inferring user interests based on metadata of a plurality of multimedia objects captured by a plurality of users. The method generally includes receiving, for each of the users, metadata describing each multimedia object in the plurality of objects associated with that user. Each multimedia object includes one or more attributes imputed to that object based on the metadata. The method also includes identifying one or more concepts from the one or more attributes. Each concept includes at least a first attribute that co-occurs with a second attribute imputed to a first multimedia object. The method also includes associating a first one of the plurality of users with at least one of the concepts based on the attributes imputed to multimedia objects associated with the first one of the plurality of users. 
     Other embodiments include, without limitation, a computer-readable medium that includes instructions that enable a processing unit to implement one or more aspects of the disclosed methods as well as a system having a processor, memory, and application programs configured to implement one or more aspects of the disclosed methods. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only exemplary embodiments and are therefore not to be considered limiting of its scope, may admit to other equally effective embodiments. 
         FIG. 1  illustrates an example computing environment, according to one embodiment. 
         FIG. 2  further illustrates the mobile application described relative to  FIG. 1 , according to one embodiment. 
         FIG. 3  further illustrates the analysis tool described relative to  FIG. 1 , according to one embodiment. 
         FIG. 4  illustrates an example user interest taxonomy, according to one embodiment. 
         FIG. 5  illustrates a method for generating a user interest taxonomy, according to one embodiment. 
         FIG. 6  illustrates a method for building a predictive model for inferring user interests, according to one embodiment. 
         FIG. 7  illustrates a method for inferring user interests based on a predictive model, according to one embodiment. 
         FIG. 8  illustrates an application server computing system, according to one embodiment. 
     
    
    
     To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation. 
     DETAILED DESCRIPTION 
     Embodiments presented herein describe techniques for inferring user interests from metadata associated with digital multimedia (e.g., images and video). Digital multimedia provides a wealth of information which can be evaluated to determine a variety of valuable insights about individuals taking images. For example, assume an individual takes pictures at a golf course using a mobile device (e.g., a smart phone, tablet, etc.). Further, assume that the pictures are the only indication the individual was at the golf course (e.g., because the individual made only cash purchases and signed no registers). Metadata associated with this image can place the individual at the golf course at a specific time. Further, event data could be used to correlate whether there was going on at that time (e.g., a specific tournament). Such information may be useful to third parties, e.g., for targeted advertising and recommendations. 
     However, an advertiser might not be able to identify an effective audience for targeting a given product or service based on such information alone. Even if image metadata places an individual at a golf course at a particular point of time, the advertiser might draw inaccurate inferences about the individual. For example, the advertiser might assume that because the metadata places the individual at a high-end golf course, the individual is interested in high-end golf equipment. The advertiser might then recommend other high-end equipment or other golf courses to that individual. If the individual rarely plays golf or does not usually spend money at high-end locations. Such recommendations may lead to low conversion rates for the advertiser. Historically, advertisers have been generally forced to accept low conversation rates, as techniques for identifying individuals likely to be receptive to or interested in a given product or service are often ineffective. 
     Embodiments presented herein describe techniques for inferring user interests based on metadata of images (e.g., digital photos). Specifically, embodiments describe building a predictive model used to infer interests for a user. In one embodiment, a multimedia service platform provides a software development kit (SDK) that third parties (e.g., retailers, marketers, etc.) may use to build mobile applications that extracts metadata from digital multimedia captured and stored on a mobile device. The metadata may describe where and when a given image was taken. The mobile application can use APIs included in the SDK to upload images and videos and metadata thereof to the platform from a mobile device. Further, the multimedia service platform may identify patterns from metadata extracted from images and videos. Further, embodiments presented herein can identify latent relationships between different categories, topics, or subjects (referred to generally as interests or user interests) from multimedia collections of multiple users. For example, if many users take pictures at golf courses also take pictures at an unrelated event (e.g., at a traveling museum exhibit) then the system can discover a relationship between these otherwise unrelated interests. Thereafter, advertising related to golfing products and services could be targeted to individuals who publish pictures of the travelling museum exhibit, regardless of any other known interest in golf. 
     In one embodiment, the multimedia service platform evaluates metadata corresponding to each image or video submitted to the platform against a knowledge graph. The knowledge graph provides a variety of information about events, places, dates, times, etc. that may be compared with the metadata of a given image. For example, the knowledge graph may include weather data, location data, event data, and online encyclopedia data. For instance, attributes associated with an event may include a name, location, start time, end time, price range, etc. The multimedia service platform correlates spatiotemporal metadata from a digital image with a specific event in the knowledge graph. That is, the knowledge graph is used to impute attributes related to events, places, dates, times, etc., to a given digital multimedia file based on the metadata provided with that file. 
     In one embodiment, the analysis tool represents attributes imputed to digital multimedia in a user-attribute matrix, where each row of the matrix represents a distinct user and each column represents an attribute from the knowledge graph that can be imputed to a digital multimedia file. The analysis tool may add columns to the user-attribute matrix as additional attributes are identified. The cells of a given row indicate how many times a given attribute has been imputed to a digital multimedia file published by a user corresponding to that row. Accordingly, when the analysis tool imputes an attribute to a digital multimedia file (based on the file metadata), a value for that attribute is incremented in the user-attribute matrix. Doing so allows the multimedia service platform to identify useful information about that user. For instance, the analysis tool may identify that a user often attends sporting events, movies, participates in a particular recreational event (e.g., skiing or golf), etc. In addition, the analysis tool may identify information about events that the user attends, such as whether the events are related to a given sports team, whether the events are related to flights from an airport, a range specifying how much the event may cost, etc. 
     In one embodiment, the multimedia service platform learns concepts based on co-occurring attributes identified in the user-attribute matrix. A concept is a collection of one or more identified attributes. The multimedia service platform may use machine learning techniques to learn concepts from the attributes of the user-attribute matrix. For example, machine learning techniques cluster or otherwise group group attributes based on co-occurrences. For instance, “travel,” “winter,” “Park City,” and “skiing” may frequently co-occur. As a result, the machine learning techniques may group these co-occurring attributes into a concept (e.g., a “skiing” concept). Further, the multimedia service platform may score an attribute to each respective concept. The multimedia service platform may associate attributes that satisfy specified criteria (e.g., the top five scores per concept, attributes exceeding a specified threshold, etc.) to a given concept. 
     Further, the analysis tool may generate an interest taxonomy based on the learned concepts. In one embodiment, an interest taxonomy is a hierarchical representation of user interests. For example, the interest taxonomy can identify general groups (e.g., sports, music, and travel) and sub-groups (e.g., basketball, rock music, and discount airlines) of interest identified from the concepts. The multimedia service platform may use the interest taxonomy to discover latent relationships between concepts. For example, the multimedia service platform may build a predictive learning model using the interest taxonomy. 
     In one embodiment, the multimedia service platform may use the interest taxonomy to infer interests of a given user. To do so, the multimedia service platform builds a learning model that determines membership scores of a user for a given concept. By training the learning model, the multimedia service platform can identify latent interests of the user based on the interest taxonomy of the user as a result. Further, the multimedia service platform may map product feeds of third parties to user interest taxonomies to identify products to recommend to a given user. Doing so allows third parties to target more meaningful recommendations to a given user. 
     Note, the following description relies on digital images captured by a user and metadata as a reference example of learning latent interests based on the metadata. However, one of skill in the art will recognize that the embodiments presented herein may be adapted to other digital multimedia that include time and location metadata, such as digital videos captured on a mobile device. Further, an analysis tool may extract metadata particular to a type of the multimedia, e.g., the length of a video, which can be used relative to the techniques described herein. 
       FIG. 1  illustrates an example computing environment  100 , according to one embodiment. As shown, the computing environment  100  includes mobile devices  105 , an extract, transform, and load (ETL) server  110 , an application server  115 , and a third party system  120 , connected to a network  125  (e.g., the Internet). 
     In one embodiment, the mobile devices  105  include a mobile application  106  which allows users to interact with a multimedia service platform (represented by the ETL server  110  and the application server  115 ). In one embodiment, the mobile application  106  is developed by a third-party enterprise (e.g., a retailer, social network provider, fitness tracker developer, etc.). The mobile application  106  may send images  108  and associated metadata to the multimedia service platform. In one embodiment, the mobile application  106  may access APIs exposed by a software development kit (SDK) distinct to the platform. 
     In another embodiment, the mobile application  106  may access a social media service (application service  116 ) provided by the service platform. The social media service allows users to capture, share, and comment on images  108  as a part of existing social networks (or in conjunction) with those social networks. For example, a user may publish images  108  captured using a camera on mobile device  105  to a specified social network. In turn, the application  106  retrieves metadata and images  108  and metadata to the multimedia service platform. The multimedia service platform uses the metadata to infer latent interests of the userbase as well as latent relationships between the interests. 
     The mobile application  106  extracts Exchangeable Image Format (EXIF) metadata from each image  108 . The mobile application  106  can also extract other metadata (e.g., PHAsset metadata in Apple iOS devices) describing additional information, such as GPS data. In addition, the mobile application  106  may perform extract, transform, and load (ETL) operations on the metadata to format the metadata for use by components of the multimedia service platform. For example, the mobile application  106  may determine additional information based on the metadata, such as whether a given image was taken during daytime or nighttime, whether the image was taken indoors or outdoors, whether the image is a “selfie,” etc. Further, the mobile application  106  also retrieves metadata describing application use. Such metadata includes activity by the user on the mobile application  106 , such as image views, tagging, etc. Further, as described below, the mobile application  106  provides functionality that allows a user to search through a collection of images by the additional metadata, e.g., searching a collection of images that are “selfies” and taken in the morning. 
     In one embodiment, the ETL server  110  includes an ETL application  112 . The ETL application  112  receives streams of image metadata  114  (e.g., the EXIF metadata, PHAsset metadata, and additional metadata) from mobile devices  105 . Further, the ETL application  112  cleans, stores, and indexes the image metadata  114  for use by the application server  115 . Once processed, the ETL application  112  may store the image metadata  114  in a data store, e.g., such as in a database or a Hadoop-based storage architecture (e.g., Hive), for access by the application server  115 . 
     In one embodiment, the application service  116  communicates with the mobile application  106 . The application server  115  may be a physical computing system or a virtual machine instance in a computing cloud. Although depicted as a single server, the application server  115  may comprise multiple servers configured as a cluster (e.g., via the Apache Spark framework, a Hadoop-based storage architecture). A clustered architecture allows the application servers  115  to process large amounts of images and image metadata sent from mobile applications  106 . 
     As shown, the application server  115  includes an analysis tool  117 , a knowledge graph  118 , and a user interest taxonomy  119 . As described below, the user interest taxonomy  119  represents interests inferred from image attributes identified from the knowledge graph  118  based on the image metadata  114  from image collections of multiple users. 
     In one embodiment, the knowledge graph  118  includes a collection of attributes which may be imputed to an image. Example attributes include time and location information, event information, genres, price ranges, weather, subject matter, and the like. The analysis tool  117  builds the knowledge graph  118  using weather data, location data, events data, encyclopedia data, and the like from a variety of data sources. 
     In one embodiment, the analysis tool  117  imputes attributes from the knowledge graph  118  to an image  108  based on the metadata  114 . That is, the analysis tool  117  may correlate time and location information in image metadata  114  to attributes in the knowledge graph  118 . For example, assume that a user captures an image  108  of a baseball game. Metadata  114  for that image  108  may include a GPS, a date, and a time when the image  108  was captured. The analysis tool  117  can correlate this information to attributes such as weather conditions at that time and location (e.g., “sunny”), an event name (e.g., “Dodgers Game”), teams playing at that game (e.g., “Dodgers” and “Cardinals”), etc. The analysis tool  117  associates the imputed attributes with the user who took the image. As noted, e.g., a row in a user attribute matrix may be updated to reflect the imputed attributes of each new image taken by that user. Further, the analysis tool  117  may perform machine learning techniques, such as latent Dirichlet analysis (LDA), to decompose the user-attribute matrix into sub-matrices. Doing so allows the analysis tool  117  to identify concepts, i.e., clusters of attributes. The analysis tool  117  may use the user interest taxonomy  119  to generate product recommendations. The analysis tool  117  may also use the interest taxonomy  119  identify one or more users that may be interested in a product or service. For example, the analysis tool  117  may extract information from a product feed  121  of a third party system  120 . In one embodiment, the product feed  121  is a listing of products or services of a third party, such as a retailer. The analysis tool  117  may identify, from the product feed  121 , one or more attributes describing each product. For example, a product of a shoe retailer may have attributes such as “shoe,” “running,” “menswear,” and so on. The analysis tool  117  can map the attributes of the product feed  121  with the interest taxonomy  119 . Doing so allows the analysis tool  117  to identify products and services from the feed  121  that align with interests in the interest taxonomy. In turn, third parties can target users who may be interested in the identified products and services. 
       FIG. 2  illustrates mobile application  106 , according to one embodiment. As shown, mobile application  106  includes a SDK component  200  with APIs configured to send image and metadata information to the multimedia service platform. The SDK component  200  further includes an extraction component  205 , a search and similarity component  210 , and a log component  215 . In one embodiment, the extraction component  205  extracts metadata (e.g., EXIF metadata, PHAsset metadata, and the like) from images captured using a mobile device  105 . The metadata may describe various aspects specific the image, such as whether the image is in color or black and white, whether the image is a “selfie,” and the like. Further, the extraction component  205  may perform ETL preprocessing operations on the metadata. For example, the extraction component  205  may format the metadata for the search and similarity component  210  and the log component  215 . 
     In one embodiment, the search and similarity component  210  infers additional metadata from an image based on the metadata (e.g., spatiotemporal metadata) retrieved by the extraction component  205 . Examples of additional metadata include whether a given image was captured at daytime or nighttime, whether the image was captured indoors or outdoors, whether the image was edited, weather conditions when the image was captured, etc. Further, the search and similarity component  210  generates a two-dimensional image feature map from a collection of images captured on a given mobile device  105 , where each row represents an image and columns represent metadata attributes. Cells of the map indicate whether an image has a particular attribute. The image feature map allows the search and similarity component  210  to provide search features to a user. For example, the mobile application  106  may search for images on a mobile device which have a given attribute, such as images taken during daytime or taken from a particular location. In turn, the search and similarity component  210  may evaluate the image map to identify images (or other multimedia) having the particular attribute. 
     In one embodiment, the log component  215  evaluates the image metadata. For example, the log component  215  records metadata sent to the ETL server  110 . Once received, the application  112  performs ETL operations, e.g., loading the metadata into a data store (such as a database). The metadata is accessible by the analysis tool  117 . 
       FIG. 3  further illustrates the analysis tool  117 , according to one embodiment. As shown, the analysis tool  117  includes an aggregation component  305 , a knowledge graph component  310 , a user interest taxonomy generation component  320 , and a user interest inference component  325 . 
     In one embodiment, the aggregation component  305  receives streams of image metadata corresponding to images captured by users of application  106  by users from the ETL server  110 . Once received, the aggregation component  305  organizes images and metadata by user. The metadata may include both raw image metadata (e.g., time and GPS information) and inferred metadata (e.g., daytime or nighttime image, indoor or outdoor image, “selfie” image, etc.). To organize metadata by user, the aggregation component  305  evaluates log data from the ETL server  110  to identify image metadata from different devices (and presumably different users) and metadata type (e.g., whether the metadata corresponds to image metadata or application usage data). 
     In one embodiment, the knowledge graph component  310  builds (and later maintains) the knowledge graph  118  using any suitable data source, such as local news and media websites, online event schedules for performance venues, calendars published by schools, government, or private enterprises, online schedules and ticket sales. The knowledge graph component  310  determines attributes related to each event to store in the knowledge graph  118 . 
     In one embodiment, to impute attributes from the knowledge graph  118  to a given image, the knowledge graph component  310  evaluates time and location metadata of the image against the knowledge graph  118 . The knowledge graph component  310  determines whether the image metadata matches a location and/or event in the knowledge graph. The information may be matched using a specified spatiotemporal range, e.g., within a time period of the event, within a set of GPS coordinate range, etc. In one embodiment, the component  310  may further match the information based on a similarity of metadata of other user photos that have been matched to that event. 
     In one embodiment, the taxonomy component  320  evaluates the user-attribute matrix to determine concepts associated with a given user. As stated, a concept is a cluster of related attributes. The interest taxonomy generation component  320  may perform machine learning techniques, such as Latent Dirichlet Analysis (LDA), Non-Negative Matrix Factorization (NNMF), Deep Learning algorithms, and the like, to decompose the user-attribute matrix into sub-matrices. The taxonomy component  320  evaluates the sub-matrices to identify latent concepts from co-occurring attributes. 
     Further, the taxonomy component  320  may determine a membership score distribution for each attribute over each concept. A membership score indicates a measure of strength that a given attribute correlates with a concept. The interest taxonomy generation component  320  may populate a concept-attribute matrix, where the rows represent concepts and columns represent attributes. Each cell value is the membership score of the respective attribute to the respective concept. The generation component  320  may perform further machine learning techniques (e.g., LDA, NNMF, Deep Learning, etc.) to identify relationships and hierarchies between each concepts. 
     In one embodiment, the interest inference component  325  builds a learning model of user interests based on the identified concepts. To do so, the interest inference component  325  may train multi-class classifiers for predicting an interest score. For example, the inference component  325  may use Logistic Regression, Boosting, or Support Vector Machine (SVM) classifiers for each concept to determine user association with one or more concepts. Doing so results in each user in the platform being assigned an interest score per concept. Further, doing so provides positive and negative membership examples used to train the learning model. 
     Once trained, the interest inference component  325  may predict user interests using the learning model. As the multimedia service platform receives image metadata from new users, the interest inference component  325  can assign the new users with membership scores for each concept based on the metadata and the learning model. A user having a high membership score in a given concept may indicate a high degree of interest for that concept. 
       FIG. 4  illustrates an example user interest taxonomy  400 , according to one embodiment. As shown, the taxonomy  400  is a hierarchical representation of user interests identified from image metadata, such as metadata describing time and location information of a given image. Each node in the taxonomy  400  represents a concept identified from one or more attributes. As stated, the interest taxonomy generation component  320  may perform machine learning (e.g., LDA) to identify hierarchies and relationships between concepts. The hierarchies and relationships may further be determined manually (e.g., by a subject matter expert). 
     The taxonomy  400  includes groups  410  and sub-groups  415 . Illustratively, the concepts depicted in groups  410  include generally broader concepts, such as sports, music, and travel. The sub-groups  415  include more specific concepts related to the groups  410 , such as basketball, rock, and airlines. Further, each sub-group  415  may have its own subgroup. For example, the baseball node may include sub-group nodes depicting team names. Note,  FIG. 4  depicts a relatively small amount of concept nodes in the taxonomy  400 . In practice, the taxonomy  400  may include a greater amount of nodes (e.g., 1,000 concept nodes). 
     Each user in the multimedia service platform may be associated with one or more concepts in the interest taxonomy  400 . For a given user, the interest inference component  325  may determine a distribution of membership scores to each identified concept. The membership score may indicate a strength of correlation to a degree of interest that the user has for a given concept. For example, a high membership score in the football concept may indicate that a user has a high interest in football. Further, the interest inference component  325  may build a predictive learning model based on the membership score distribution. The interest inference component  325  can train the model using membership and non-membership of users to a given concept as positive and negative examples of concept membership. Thus, when the multimedia service platform receives new image data and metadata from a user, the interest inference component  325  may predict the user membership scores to each concept based on the image metadata. As a result, the interest inference component  325  can infer additional concepts to which new user belongs, even with a limited amount of image metadata. Such information may be useful to third party advertisers for targeted recommendations. 
       FIG. 5  illustrates a method  500  for determining a set of concepts based on image metadata, according to one embodiment. Method  500  begins at step  505 , where the aggregation component  305  segments images by users. Doing so allows the analysis tool  107  to evaluate collections of image metadata for each user individually. 
     At step  510 , the knowledge graph component  310  imputes attributes from the knowledge graph  118  onto the images based on the image metadata. To do so, the graph component  310  correlates time and location metadata of a given image to information provided in the knowledge graph, such as events, that coincide with the time and location metadata (with a degree of allowance). As a result, each image is associated with a set of attributes. 
     At step  515 , the knowledge graph component  310  builds a user-attribute matrix based on the imputed attributes to the images. The knowledge graph component  310  further imputes attributes associated with each image to the respective user. Each cell in the user-attribute matrix is an incremental value that represents a count of images in which the corresponding attribute is present. 
     At step  520 , the interest taxonomy generation component  320  decomposes the user-attribute matrix to identify concepts from the attributes. As stated, a concept may include one or more attributes. The interest taxonomy generation component  320  may evaluate the attributes using machine learning techniques to identify the concepts. Further, the interest taxonomy generation component  320  may generate an attribute-concept matrix, where the cell values represent membership scores of each attribute to a given concept. Attributes having a qualifying score may be associated with the concept. 
       FIG. 6  illustrates a method  600  for building a predictive model for inferring user interests, according to one embodiment. Method  600  begins at step  605 , where the interest inference component  325  determines, for each user, a membership score for each concept relative to other concepts. To do so, the interest inference component  325  may generate a user-attribute-concept matrix which is a dot product of the user-attribute matrix and the attribute concept matrix. Cell values of the user-attribute-concept matrix represent membership scores of a given user to each concept. 
     At step  610 , the interest inference component  325  assigns each user to one or more concepts based on the score. The interest inference component  325  may assign the user to a concept based on the concept in which the user has the highest membership score. Alternatively, the interest inference component  325  may assign the user to a concept based on threshold scores. In particular, if the membership score for a concept exceeds a threshold, the interest inference component  325  assigns the user to the concept. 
     At step  615 , the interest inference component  325  trains multiple one-versus-all predictive models for inferring user interests. The interest inference component  325  may build Support Vector Machine (SVM) classifiers for each concept. The SVM classifiers evaluate a given concept relative to other identified concepts. To train one-versus-all predictive models, the interest inference component  325  may use user membership to concepts as positive and negative examples of membership. 
       FIG. 7  illustrates a method  700  for inferring user interests based on a predictive model, according to one embodiment. Method  700  may occur any time a new user sends images to the multimedia service platform through the mobile application  106 . As stated, the ETL server  110  formats the image and metadata for processing by the analysis tool  117 . Method  700  begins at step  705 , where the user aggregation component  305  receives one or more images captured by the new user. The user aggregation component  305  segments the images by user ID. 
     At step  710 , the knowledge graph component  310  imputes attributes from the knowledge graph  118  to each image based on image metadata. To do so, the knowledge graph component  310  correlates time and location metadata of a given image to information provided in the knowledge graph, such as events, that coincide with the time and location metadata (with a degree of allowance). As a result, each image is associated with a set of attributes. 
     At step  715 , the interest inference component  325  predicts a concept score distribution for the user based on the predictive models. The interest inference component  710  may evaluate the attributes identified in the knowledge graph imputation to determine concept scores for each attribute based on the predictive models. The interest inference component  325  may perform a dot product of the user row in the user-attribute matrix to the concept-attribute matrix to determine user membership scores to each concept. The interest inference component may then assign the new user to one or more concepts based on the scores. 
       FIG. 8  illustrates an application server computing system  800  configured to impute knowledge graph attributes onto image metadata, according to one embodiment. As shown, the computing system  800  includes, without limitation, a central processing unit (CPU)  805 , a network interface  815 , a memory  820 , and storage  830 , each connected to a bus  817 . The computing system  800  may also include an I/O device interface  810  connecting I/O devices  812  (e.g., keyboard, mouse, and display devices) to the computing system  800 . Further, in context of this disclosure, the computing elements shown in computing system  800  may correspond to a physical computing system (e.g., a system in a data center) or may be a virtual computing instance executing within a computing cloud. 
     The CPU  805  retrieves and executes programming instructions stored in the memory  820  as well as stores and retrieves application data residing in the memory  820 . The interconnect  817  is used to transmit programming instructions and application data between the CPU  805 , I/O devices interface  810 , storage  78  representative of a single CPU, multiple CPUs, a single CPU having multiple processing cores, and the like. And the memory  820  is generally included to be representative of a random access memory. The storage  830  may be a disk drive storage device. Although shown as a single unit, the storage  830  may be a combination of fixed and/or removable storage devices, such as fixed disc drives, removable memory cards, or optical storage, network attached storage (NAS), or a storage area-network (SAN). 
     Illustratively, the memory  820  includes an application service  822  and an analysis tool  824 . The storage  830  includes a knowledge graph  834 , and an interest taxonomy  836 . The application service  822  provides access to various services of the multimedia service platform to mobile devices. The analysis tool  824  generates a user interest taxonomy  836  based on metadata of images taken by users. 
     In one embodiment, the analysis tool  824  builds the knowledge graph  834  from external data sources. To do so, the analysis tool  824  performs NLP techniques on the raw text obtained from the data sources to identify relevant terms related to events, moments, weather, etc. 
     In one embodiment, the analysis tool  824  may impute information from the knowledge graph  834  images submitted to the multimedia service platform. In addition, the analysis tool  824  generates a user interest taxonomy  836  of concepts inferred from the attributes. To do so, the analysis tool  824  may perform machine learning techniques (e.g., LDA, pLSA, NNMF, etc.) to learn concepts based on co-occurring attributes. In addition, the analysis tool  824  may determine a membership score for each attribute to each identified concept. The analysis tool  824  may associate attributes to a given concept based on the membership score. Further, the analysis tool  824  may identify hierarchical relationships between the concepts through machine learning. 
     In one embodiment, the analysis tool  824  performs further machine learning techniques to assign users to each identified concept. In particular, the analysis tool  824  may determine membership scores for each concept for a given user. The user may be associated with a concept in which the membership score is the highest. Alternatively, the user may be associated with multiple concepts based on the top membership scores (e.g., top five scores, top ten scores, etc.). The analysis tool  824  may train SVM classifiers for each concept to build a predictive model that can be used to predict membership scores for new users. 
     While the foregoing is directed to embodiments of the present disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.