Abstract:
Techniques are disclosed herein for learning latent interests based on metadata of one or more images. An analysis tool associates one or more attributes with each of the objects based on a time and a location described in the metadata of that object. Each of the attributes describes one of a plurality of locations or an event scheduled to occur at one or more of the plurality of locations. The analysis tool identifies one or more concepts from a distribution of the one or more attributes to each of the objects. Each of the one or more concepts includes at least a first attribute in the distribution that co-occurs with a second attribute in the distribution.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims priority to U.S. Provisional Application No. 62/093,372, filed Dec. 19, 2014. The content of the aforementioned application is incorporated by reference in its entirety. 
     
    
     BACKGROUND 
       [0002]    1. Field 
         [0003]    Embodiments of the present disclosure generally relate to data analytics. More specifically, embodiments presented herein relate to learning latent interests based on image or video metadata. 
         [0004]    2. Description of the Related Art 
         [0005]    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. 
         [0006]    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. 
         [0007]    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 
       [0008]    One embodiment presented herein describes a method for identifying latent relationships between interests based on metadata of a plurality of digital multimedia objects. The method generally includes associating one or more attributes with each of the objects based on a time and a location described in the metadata of that object. Each of the attributes describes one of a plurality of locations or an event scheduled to occur at one or more of the plurality of locations. The method also includes identifying one or more concepts from a distribution of the one or more attributes to each of the objects. Each of the one or more concepts includes at least a first attribute in the distribution that co-occurs with a second attribute in the distribution. 
         [0009]    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 
         [0010]    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. 
           [0011]      FIG. 1  illustrates an example computing environment, according to one embodiment. 
           [0012]      FIG. 2  further illustrates the mobile application described relative to  FIG. 1 , according to one embodiment. 
           [0013]      FIG. 3  further illustrates the analysis tool described relative to  FIG. 1 , according to one embodiment. 
           [0014]      FIG. 4  illustrates an example user interest taxonomy, according to one embodiment. 
           [0015]      FIG. 5  illustrates a method for determining a set of concepts based on image metadata, according to one embodiment. 
           [0016]      FIG. 6  illustrates a method for generating a user interest taxonomy based on image metadata, according to one embodiment. 
           [0017]      FIG. 7  illustrates an example application server computing system, according to one embodiment. 
           [0018]      FIG. 8  illustrates an example concept-attribute matrix, according to one embodiment. 
       
    
    
       [0019]    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 
       [0020]    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 (e.g., advertisers, marketers, and the like). 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. 
         [0021]    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 provide techniques to infer interests over a userbase based on metadata of digital multimedia. Specifically, embodiments describe techniques for learning latent concepts from metadata attributes of digital multimedia (e.g., images and video). In one embodiment, a multimedia service platform provides a software development kit (SDK) that may 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 mobile application can use APIs included in the SDK to upload images and videos to the platform from a mobile device. Further, the multimedia service platform may identify patterns from metadata extracted from images and videos of many users. The metadata may describe where and when a given image was taken. Further, in many cases, 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., take pictures of 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. 
         [0022]    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. 
         [0023]    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. 
         [0024]    In one embodiment, the multimedia service platform learns concepts from the attribute distribution. A concept is a collection of one or more identified attributes. The multimedia service platform may perform machine learning techniques to learn concepts from the attributes of the user-attribute matrix. Such techniques may group attributes by 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-occuring 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. 
         [0025]    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. 
         [0026]    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. 
         [0027]      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). 
         [0028]    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. 
         [0029]    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. 
         [0030]    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. 
         [0031]    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 infrastructure such as Hive) for access by the application server  115 . 
         [0032]    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, via a Hadoop-based storage infrastructure, etc.). A clustered architecture allows the application servers  115  to process large amounts of images and image metadata sent from mobile applications  106 . 
         [0033]    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. 
         [0034]    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. 
         [0035]    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. 
         [0036]      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 . 
         [0037]    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. 
         [0038]    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 . 
         [0039]      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 . 
         [0040]    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). 
         [0041]    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 . 
         [0042]    In one embodiment, to impute attributes from the knowledge graph  118  to a given image, the correlation component  315  evaluates time and location metadata of the image against the knowledge graph  118 . The correlation component  315  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  315  may further match the information based on a similarity of metadata of other user photos that have been matched to that event. 
         [0043]    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, 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. 
         [0044]    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. An example of the concept-attribute matrix is discussed relative to  FIG. 8 . 
         [0045]    In one embodiment, the interest inference component  325  builds a learning model based on the identified concepts and the users. To do so, the interest inference component  325  may train classifiers for predicting interest scores, e.g., through logistic regression models, boosting, or support vector machine (SVM) classifiers for each concept to determine user association in one or more concepts. Doing so results in each user in the platform being assigned an interest score per concept. 
         [0046]    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 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. 
         [0047]      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). 
         [0048]    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). 
         [0049]    The analysis tool  117  may associate each user in the multimedia service platform with one or more concepts in the interest taxonomy  400 . For a given user, the inference component  325  may determine a distribution of membership scores to each identified concept. The membership score may correlate 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. Such information may be useful to third party advertisers for targeted recommendations. 
         [0050]      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. 
         [0051]    At step  510 , the knowledge graph component  310  imputes attributes from the knowledge graph  118  onto the images based on the image metadata. That is, the knowledge graph component  310  compares time and location metadata to information in the knowledge graph  118 . 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). The graph component  310  may then identify attributes based on the correlated information (e.g., “ballpark,” “United Center,” “Bulls,” “Cavaliers,” “Chicago,” “winter,” etc.). As a result, each image is associated with a set of attributes. 
         [0052]    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. 
         [0053]    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. 
         [0054]      FIG. 6  illustrates a method  600  for generating a user interest taxonomy based on image metadata, according to one embodiment. Assume that the interest taxonomy generation component  320  has identified a set of concepts from the attributes. At step  605 , the interest taxonomy generation component  320  determines a distribution of membership scores of attributes to each identified concept. Each score indicates the likelihood that an attribute is associated with the concept. 
         [0055]    At step  610 , the interest taxonomy generation component  320  maps each attribute to one or more concepts based on the distribution. To do so, the interest taxonomy generation component  320  may, for each concept, rank each attribute by membership score and determine that a specified top amount of attributes are associated with a given concept (e.g., top three, top five, etc.). Alternatively, the interest taxonomy generation component  320  may determine that attributes exceeding a threshold score are associated with the concept. 
         [0056]    At step  615 , the interest taxonomy generation component  320  optionally identifies hierarchical relationships between the concepts. The interest taxonomy generation component  320  may identify the relationships through machine learning, such as LDA. In addition, the relationships may be manually assigned. 
         [0057]      FIG. 7  illustrates an application server computing system  700 , according to one embodiment. As shown, the computing system  700  includes, without limitation, a central processing unit (CPU)  705 , a network interface  715 , a memory  720 , and storage  730 , each connected to a bus  717 . The computing system  700  may also include an I/O device interface  710  connecting I/O devices  712  (e.g., keyboard, mouse, and display devices) to the computing system  700 . Further, in context of this disclosure, the computing elements shown in computing system  700  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. 
         [0058]    The CPU  705  retrieves and executes programming instructions stored in the memory  720  as well as stores and retrieves application data residing in the memory  720 . The interconnect  717  is used to transmit programming instructions and application data between the CPU  705 , I/O devices interface  710 , storage  730 , network interface  715 , and memory  720 . Note, CPU  705  is included to be representative of a single CPU, multiple CPUs, a single CPU having multiple processing cores, and the like. And the memory  720  is generally included to be representative of a random access memory. The storage  730  may be a disk drive storage device. Although shown as a single unit, the storage  730  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). 
         [0059]    Illustratively, the memory  720  includes an application service  722  and an analysis tool  724 . The storage  730  includes a knowledge graph  734 , and one or more user interest taxonomies  736 . The application service  722  provides access to various services of an multimedia service platform to mobile devices. The analysis tool  724  generates a user interest taxonomy  736  based on metadata of images taken by users. 
         [0060]    Further, the analysis tool  724  builds the knowledge graph  734  from external data sources. To do so, the analysis tool  724  performs NLP techniques on the raw text obtained from the data sources to identify relevant terms related to events, moments, weather, etc. Further, the analysis tool  724  may impute information from the knowledge graph  734  images submitted to the multimedia service platform. In addition, the analysis tool  724  generates a user interest taxonomy  736  of concepts inferred from the attributes. To do so, the analysis tool  724  may perform machine learning techniques to identify concepts based on co-occurring attributes. In addition, the analysis tool  724  may determine a membership score for each attribute to each identified concept. The analysis tool  724  may associate attributes to a given concept based on the membership score. Further, the analysis tool  724  may identify hierarchical relationships between the concepts through machine learning. 
         [0061]      FIG. 8  illustrates an example attribute-concept matrix, according to one embodiment. Illustratively, rows  805  of the matrix represent concepts (i.e., c 1 , c 2 , c 3 , and so on) and columns  810  of the matrix represent attributes (i.e., a 1 , a 2 , a 3 , and so on). Values in each cell  815  represents a membership score of a given attribute to a concept. As stated, a membership score indicates a measure of strength of an attribute to a given concept. The analysis tool  117  may cluster attributes having scores exceeding a specified threshold to a given concept. For example, assume that the threshold is 0.65. In such a case, the analysis tool  117  would associate a 2  (having a score of 0.87) and a 6  (having a score of 0.74) with concept c 1 . 
         [0062]    The preceding discussion presents a variety of embodiments. However, the present disclosure is not limited to the specifically described embodiments. Instead, any combination of the following features and elements, whether related to different embodiments or not, is contemplated to implement and practice the techniques described herein. Furthermore, although embodiments of the present disclosure may achieve advantages over other possible solutions and/or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the present disclosure. Thus, the following aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s). 
         [0063]    Aspects may be embodied as a system, method or computer program product. Accordingly, embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, embodiments may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
         [0064]    Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus or device. 
         [0065]    The flowchart and block diagrams in the figures illustrate the architecture, functionality and operation of possible implementations of systems, methods and computer program products according to various embodiments presented herein. In this regard, each block in the flowchart or block diagrams may represent a module, segment or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by special-purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
         [0066]    The following discussion presents a variety of embodiments. However, the present disclosure is not limited to the specifically described embodiments. Instead, any combination of the following features and elements, whether related to different embodiments or not, is contemplated to implement and practice the techniques described herein. Furthermore, although embodiments of the present disclosure may achieve advantages over other possible solutions and/or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the present disclosure. Thus, the following aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly recited in a claim(s). 
         [0067]    Aspects may be embodied as a system, method or computer program product. Accordingly, embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, embodiments may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
         [0068]    Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus or device. 
         [0069]    The flowchart and block diagrams in the figures illustrate the architecture, functionality and operation of possible implementations of systems, methods and computer program products according to various embodiments presented herein. In this regard, each block in the flowchart or block diagrams may represent a module, segment or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by special-purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
         [0070]    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.