Patent Publication Number: US-2012041820-A1

Title: Machine to structure data as composite property

Description:
TECHNICAL FIELD 
     The subject matter disclosed herein generally relates to the processing of data. Specifically, the present disclosure addresses systems and methods of structuring data as a composite property. 
     BACKGROUND 
     Nowadays, a machine (e.g., a computing device) may be used to process information pertaining to items and products. As an example, a machine may host a database that tracks an inventory of items, which may be specimens of products. The machine may be all or part of a network-based system for processing such information. For example, a network-based commerce system may include one or more machines that maintain a database, where records in the database contain information pertinent to various items or products. The various items or products may be available for purchase, and accordingly may be merchandised or advertised as being so available. 
     As used herein, the term “product” may include a tangible product, an intangible product (e.g., downloadable electronic data), an obligation to provide a product, a service, a license to use a service, or any suitable combination thereof. An “item” herein refers to an instance of a product (e.g., a specimen of the product). While a single item may constitute a product (e.g., a unique one-of-a-kind item cataloged as a product), in many cases multiple items constitute multiple instances of a product. For example, a product may be a particular model of digital camera, while a specific digital camera of that model (e.g., having a unique serial number) may be an item constituting an instance of that product. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings in which: 
         FIG. 1-4  are conceptual diagrams illustrating relationships among an item, a characteristic of the item, a descriptor of the characteristic, and a feature of the descriptor, according to some example embodiments; 
         FIG. 5  is a conceptual diagram illustrating properties that are directly or indirectly related to a product, according to some example embodiments; 
         FIG. 6  is a network diagram illustrating a system that includes a data structure machine, according to some example embodiments; 
         FIG. 7  is a block diagram illustrating a data structure usable as a composite property, according some example embodiments; 
         FIG. 8  is a block diagram illustrating components of a data structure machine, according to some example embodiments; 
         FIG. 9-12  are flowcharts illustrating a method of structuring data as a composite property of an item, according to some example embodiments; and 
         FIG. 13  is a block diagram illustrating components of a machine, according to some example embodiments, able to read instructions from a machine-readable medium and perform any one or more of the methodologies discussed herein. 
     
    
    
     DETAILED DESCRIPTION 
     Example methods and systems described herein are directed to structuring data as a composite property. Examples merely typify possible variations. Unless explicitly stated otherwise, components and functions are optional and may be combined or subdivided, and operations may vary in sequence or be combined or subdivided. In the following description, for purposes of explanation, numerous specific details are set forth to provide a thorough understanding of example embodiments. It will be evident to one skilled in the art, however, that the present subject matter may be practiced without these specific details. 
     Information pertinent to an item or product may be organized (e.g., structured) as a property of the item or product. The property may be represented (e.g., stored) as property data in a data structure of the item or product. For example, the property may be represented in a listing that describes the item or product, or in a database record that stores information regarding the item or product. In many cases, property data takes the form of an attribute-value pair. An attribute-value pair includes an attribute of the item or product and a corresponding value of the attribute. Generally, in a valid attribute-value pair, the value is assignable to the attribute and may be one of multiple potential values that are assignable to the attribute. For example, an attribute-value pair may be expressed as “color=red,” where allowable colors include “red,” “yellow,” and “blue.” The attribute is “color,” and its corresponding value is “red.” The words “red,” “yellow,” and “blue” are values that are assignable to the attribute, and the word “red” is the value actually assigned to the attribute. As another example, an attribute-value pair may be expressed as “color=0x1f,” where “0x1f” is a reference (e.g., an identifier or a pointer) that specifies the color “red.” As a further example, an attribute-value pair may be expressed as “comment=‘I really like this digital camera because it took the best outdoor shots while I was on my vacation to New Zealand,” where the attribute is “comment” and the value is free-form text (e.g., a sentence or a paragraph). 
     Similarly, information pertinent to a property may also be organized as a “property of a property.” Stated another way, a property may have one or more properties of its own. In particular, the value of an attribute-value pair may correspond to its own property data, which may include a further attribute-value pair. For example, where an attribute-value pair for a digital camera is “color=red,” an attribute-value pair for the color “red” may be “earliest availability=available now.” As further examples, the attribute-value pair for the color “red” may be “earliest availability=available in 90 days” or “earliest availability=Oct. 1, 2010.” 
     Moreover, information may be organized as a “property of a property of an item” or a “property of a property of a property of a product.” As used herein, the terms “characteristic,” “descriptor,” and “feature” all refer to a property and are used as synonyms of the term “property.” For clarity, a “property of a property of a property of an item” may be described as a “feature of a descriptor of a characteristic of an item.” In this manner, information pertinent to items, products, properties, or any suitable combination thereof may be structured with any level of sophistication or complexity (e.g., beyond three steps removed from an item or product). Accordingly, a data structure may be generated (e.g., by a machine) to contain one or more properties of an item or product, as well as to contain one or more properties of those properties. In the discussion herein, a “composite property” refers to such a data structure. Generally, a composite property may correspond to an item, a product, another composite property, or any suitable combination thereof. 
     A composite property (e.g., of an item or a product) may be indexed based on any value contained therein, and that value may be used to identify the item or the product (e.g., in response to a search request based on that value). Continuing the above example, suppose the composite property of a digital camera contains the attribute-value pair “color=red.” A query for red digital cameras may result in identification of this digital camera. Furthermore, suppose the composite property for the digital camera includes the attribute-value pair “earliest availability=available now.” A query for presently available digital cameras may result in identification of this digital camera. 
     As a result, a composite property may be used to identify an item as an instance of a virtual product. A “virtual product,” as used herein, is a set of items that share at least one attribute-value pair within their respective composite properties. Thus, following the previous example, although an item may be an instance of a particular product (e.g., serial number 00010 of a Model ABC digital camera), the same item may also be instances of multiple virtual products (e.g., all digital cameras that are red, all digital cameras that are available now, and all digital cameras that are both red and available now). Moreover, a composite property may be used to identify a virtual product as being related to another product, virtual or otherwise. For example, where one product has a composite property with the attribute-value pair “manufacturer=Sony,” a related virtual product may have a composite property with the attribute-value pair “nationality of manufacturer=Japan.” 
       FIG. 1-4  are conceptual diagrams illustrating relationships among an item  110 , a characteristic  120  of the item  110 , a descriptor  130  of the characteristic  120 , and a feature  140  of the descriptor  130 , according to some example embodiments. 
     As shown in  FIG. 1 , the item  110  is an instance of a product and corresponds to property data  112 . The property data  112  specifies an attribute  114  of the item  110 . The property data  112  also specifies a value  116  of the attribute  114 , thus specifying an attribute-value pair pertinent to the item  110 . The value  116  specifies the characteristic  120  of the item  110 . 
     The characteristic  120  is a basis of a virtual product and corresponds to property data  122 . The property data  122  specifies an attribute  124  of the characteristic  120 . The property data  122  also specifies a value  126  of the attribute  124 , thereby specifying an attribute-value pair pertinent to the characteristic  120 . The value  126  specifies the descriptor  130  of the characteristic  120 . 
     The descriptor  130  is also a basis of a virtual product and corresponds to property data  132 . The property data  132  specifies an attribute  134  of the descriptor  130 . The property data  132  also specifies a value  136  of the attribute  134 , thereby specifying an attribute-value pair pertinent to the descriptor  130 . The value  136  specifies the feature  140  of the descriptor  130 . 
     The feature  140  is another basis of a virtual product and corresponds to property data  142 . The property data  142  specifies an attribute  144  of the feature  140 . The property data  142  also specifies a value  146  of the attribute  144 , thereby specifying an attribute-value pair pertinent to the feature  140 . The value  146  may specify a downstream property (not shown) of the feature  140 . This chain of relationships may extend to any length and, in some example embodiments, may include one or more circular relationships (e.g., direct or indirect loopback relationships). As noted above, any level of sophistication or complexity may be supported by a composite property. 
     In an example embodiment shown in  FIG. 2 , the item  110  is an instance (e.g., a DVD copy) of a movie titled “Jurassic Park.” The attribute  114  of the item  110  is “writer,” and the corresponding value  116  is “Michael Crichton.”Accordingly, a query for “writer: Michael Crichton” may result in identification (e.g., as instances of a virtual product) of items of which Michael Crichton is a writer (e.g., the movie “Jurassic Park” and the book “Congo”). Hence, a virtual product based on “Michael Crichton” may be a product that is related to “Jurassic Park.” 
     The characteristic  120  of the item  110 , as specified by the value  116 , is “Michael Crichton.” The attribute  124  of the characteristic  120  is “producer of,” and the corresponding value  126  is “ER” (the title of a television show). Accordingly, a query for “producer of: ER” may result in identification (e.g., as instances of a virtual product) of items for which Michael Crichton is a producer (e.g., the show “ER” and the movie “Twister”). Hence, a virtual product based on “ER” may be a product that is related to “Michael Crichton,” “Jurassic Park,” or both. 
     The descriptor  130  of the characteristic  120 , as specified by the value  126 , is “ER.” The attribute  134  of the descriptor  130  is “co-producer,” and the corresponding value  136  is “Steven Spielberg.” Accordingly, a query for “co-producer: Steven Spielberg” may result in identification (e.g., as instances of a virtual product) of items for which Steven Spielberg is a co-producer (e.g., the show “ER” and the movie “The Goonies”). Hence, a virtual product based on “Steven Spielberg” may be a product that is related to “ER,” “Michael Crichton,” “Jurassic Park,” or any suitable combination thereof. 
     The feature  140  of the descriptor  130 , as specified by the value  136 , is “Steven Spielberg.” The attribute  144  of the feature  140  is “actor in,” and the corresponding value  146  is “The Blues Brothers.” Accordingly, a query for “actor in: The Blues Brothers” may result in identification (e.g., as instances of a virtual product) of items in which Steven Spielberg is an actor (e.g., the movie “The Blues Brothers” and the movie “Vanilla Sky”). Hence, a virtual product based on “The Blues Brothers” may be a product that is related to “Steven Spielberg,” “ER,” “Michael Crichton,” “Jurassic Park,” or any suitable combination thereof. 
     In an example embodiment shown in  FIG. 3 , the item  110  is an instance (e.g., a printed copy) of a book titled “The Stand.” The attribute  114  of the item  110  is “author,” and the corresponding value  116  is “Stephen King.” Accordingly, a query for “author: Stephen King” may result in identification of items of which Stephen King is an author (e.g., the book “The Stand” and the book “Pet Sematary”). Hence, a virtual product based on “Stephen King” may be a product that is related to “The Stand.” 
     The characteristic  120  of the item  110 , as specified by the value  116 , is “Stephen King.” The attribute  124  of the characteristic  120  is “birth year,” and the corresponding value  126  is “1947.” Accordingly, a query for “birth year: 1947” may result in identification of items that are related to a person born in 1947 (e.g., a book by Stephen King and a song by David Bowie). Hence, a virtual product based on “1947” may be a product that is related to “Stephen King,” “The Stand,” or both. 
     The descriptor  130  of the characteristic  120 , as specified by the value  126 , is “1947.” The attribute  134  of the descriptor  130  is “era,” and the corresponding value  136  is “post-WWII.” Accordingly, a query for “era: post-WWII” may result in identification of items that are related to a period of time between 1946 and 1960 (e.g., a book by Richard Matheson and a film by Alfred Hitchcock). Hence, a virtual product based on “post-WWII” may be a product that is related to “1947,” “Stephen King,” “The Stand,” or any suitable combination thereof. 
     The feature  140  of the descriptor  130 , as specified by the value  136 , is “post-WWII.” The attribute  144  of the feature  140  is “literature style(s),” and the corresponding value  146  is “pulp fiction.” Accordingly, a query for “literature style(s): pulp fiction” may result in identification of items for which the literature style is pulp fiction (e.g., books by Frank Herbert and books by H. P. Lovecraft). Hence, a virtual product based on “pulp fiction” may be a product that is related to “post-WWII,” “1947,” “Stephen King,” “The Stand,” or any suitable combination thereof. 
     In an example embodiment shown in  FIG. 4 , the item  110  is an instance (e.g., a representative instance) of a car, specifically, a “2006 Honda Civic 2 Door Coupe LX.” The attribute  114  of the item  110  is “compatible muffler,” and the corresponding value  116  is “X1 Universal Muffler.” Accordingly, a query for “compatible muffler: X1 Universal Muffler” may result in identification of items with which the X1 Universal Muffler is compatible. Hence, a virtual product based on “X1 Universal Muffler” may be a product that is related to “2006 Honda Civic 2 Door Coupe LX.” 
     The characteristic  120  of the item  110 , as specified by the value  116 , is “X1 Universal Muffler.” The attribute  124  of the characteristic  120  is “in stock at,” and the corresponding value  126  is “Bob&#39;s Car Parts.” Accordingly, a query for “in stock at: Bob&#39;s Car Parts” may result in identification of items that are in stock at Bob&#39;s Car Parts (e.g., an X1 Universal Muffler and a dashboard cover). 
     The descriptor  130  of the characteristic  120 , as specified by the value  126 , is “Bob&#39;s Car Parts.” The attribute  134  of the descriptor  130  is “shipping policy,” and the corresponding value  136  is “free shipping.” Accordingly, a query for “shipping policy: free shipping” may result in identification of items for which shipping is free (e.g., a X1 Universal Muffler and a set of snow tires). Hence, a virtual product based on “free shipping” may be a product that is related to “Bob&#39;s Car Parts,” “X1 Universal Muffler,” “2006 Honda Civic 2 Door Coupe LX,” or any suitable combination thereof. 
     The feature  140  of the descriptor  130 , as specified by the value  136 , is “free shipping.” The attribute  144  of the feature  140  is “offered by,” and the corresponding value  146  is “Tires By Mail.” Accordingly, a query for “offered by: Tires By Mail” may result in identification of items that are offered by Tires By Mail (e.g., a set of snow tires and a set of racing tires). Hence, a virtual product based on “Tires By Mail” may be a product that is related to “free shipping,” “Bob&#39;s Car Parts,” “X1 Universal Muffler,” “2006 Honda Civic 2 Door Coupe LX,” or any suitable combination thereof. 
       FIG. 5  is a conceptual diagram illustrating properties  510 - 573  that are directly or indirectly related to a product  500 , according to some example embodiments. Each of the properties  510 - 573  is named after an attribute specified by property data corresponding to that property. As an example, the product  500  may be a media item (e.g., a video, a book, or audio data). As shown in  FIG. 5 , the product  500  has four properties  510  (“Title”),  520  (“ISBN”),  530  (“Author”), and  550  (“Reviews”). These directly related properties  510 ,  520 ,  530 , and  550  may be designated as “characteristics” of the product  500 , using the nomenclature of  FIG. 1-4 . 
     The property  530  (“Author”) has its own properties  531  (“Birth Name”),  533  (“Birthdate”),  535  (“Biography”),  537  (“Books”), and  539  (“Films”). Likewise, the property  550  (“Reviews”) has its own properties  552  (“Title”),  554  (“Date”),  556  (“Author”),  558  (“Text”),  562  (“Title”),  564  (“Date”),  566  (“Publication”), and  568  (“Author”). The properties  531 ,  533 ,  535 ,  537 ,  539 ,  552 ,  554 ,  556 ,  558 ,  562 ,  564 ,  566 , and  568  constitute “properties of properties” and may be designated as “descriptors” of “characteristics” of the product  500 , using the nomenclature of  FIG. 1-4 . Note that these “properties of properties” have an indirect relationship to the product  500  and need not refer to the product  500 . 
     The property  531  (“Name”) has its own property  532  (“Aliases”). Similarly, the property  539  (“Films”) has its own properties  541  (“Title”),  542  (“Release Date”), and  543  (“Cast”). The property  558  (“Text”) has its own property  559  (“Keywords”), and the property  568  (“Author”) has its own properties  571  (“Other Works”),  572  (“Style”), and  573  (“Biography”). The properties  532 ,  541 ,  542 ,  543 ,  559 ,  571 ,  572 , and  573  constitute “properties of properties of properties” and may be designated as “features” of “descriptors” of “characteristics” of the product  500 , using the nomenclature of  FIG. 1-4 . Note that these “properties of properties of properties” have an indirect relationship to the product  500  and need not refer to the product  500 . 
     The relationships among these properties  510 - 573  may be represented in a data structure as a composite property, which may be stored as a composite property of the product  500 . This may have the effect of organizing (e.g., structuring) data that otherwise would be unstructured with respect to the product. For example, suppose that the author of the product  500  used a fictitious name (e.g., a nom de plume) for the product  500 , but has a legal birth name specified in the property  531  and a list of known aliases (e.g., nicknames) specified in the property  532 . While a vendor of the product  500  may neglect to provide the legal birth name or the aliases (e.g., due to limited space on packaging for the product  500 ), the legal birth name and the aliases may be available from an alternative source of information (e.g., another vendor of the product, the manufacturer of the product, or an information service). 
     A machine (e.g., a data structure machine) may generate the composite property to include all this information. The machine may then index the composite property based on any one or more of the properties  510 - 573 , thereby enabling identification of the product  500  using any one or more of the properties  510 - 573  (e.g., in response to a query submitted by a user). Furthermore, the machine may identify multiple products (e.g., multiple items from multiple products) as a single virtual product, based on any one or more of the properties  510 - 573  being respectively contained in composite properties of the multiple products. In other words, meaningful relationships (e.g., commonalities) among products may be identified through “properties of properties” of those products. 
       FIG. 6  is a network diagram illustrating a system  600  that includes a data structure machine  610 , according to some example embodiments. The system  600  includes the data structure machine  610 , vendor machines  620  and  630 , a manufacturer machine  640 , an information service machine  650 , a client machine  660 , and a database  670 , all coupled to each other via a network  690 . 
     The data structure machine  610  and the database  670  may be associated with a network-based commerce system and accordingly may form all or part of such a network-based commerce system. The data structure machine  610  is configured to generate a data structure as a composite property for the item  110 , as discussed in greater detail below with respect to  FIG. 8-12 . The vendor machines  620  and  630  correspond to different vendors (e.g., sellers) of the item  110 . Each of the vendor machines  620  and  630  may provide information usable by the data structure machine  610  to generate one or more properties (e.g., property data). Thus, generation of the data structure may include generating one or more attribute-value pairs based on information received from the different vendor machines  620  and  630 . Similarly, information usable by the data structure machine  610  to generate one or more properties may be received from the manufacturer machine  640 , the information service machine  650 , or any suitable combination thereof. In various example embodiments, the client machine  660  may submit information usable by the data structure machine  610  to generate one or more properties. For instance, following the example shown in  FIG. 2 , a user of the client machine  660  may upload an interesting fact about Michael Crichton for inclusion in the property data  122  of the characteristic  120  (“Michael Crichton”). 
     Any of the machines shown in  FIG. 6  may be implemented in a general-purpose computer modified (e.g., configured or programmed) by software to be a special-purpose computer to perform the functions described herein for that machine. For example, a computer system able to implement any one or more of the methodologies described herein is discussed below with respect to  FIG. 13 . Moreover, unless specifically stated otherwise, any two or more of the machines illustrated in  FIG. 6  may be combined into a single machine, and the functions described herein for any single machine may be subdivided among multiple machines. 
     The database  670  may be any kind of database that stores information (e.g., a data structure stored as one or more data records). For example, the database  670  may be a single file (e.g., a tab-delimited text file), a spreadsheet, a relational database, a triple-store, or any suitable combination thereof. Moreover, the database  670  may be implemented by one or more machines, which may be co-located together (e.g., a database server “farm”) or separated in location (e.g., a cloud computing environment). 
     The network  690  may be any network that enables communication between machines (e.g., data structure machine  610  and client machine  660 ). For example, the network  690  may be a wired network, a wireless network, or any suitable combination thereof. The network  690  may include one or more portions that constitute a private network, a public network (e.g., the Internet), or any suitable combination thereof. 
       FIG. 7  is a block diagram illustrating a data structure  700  usable as a composite property of the item  110 , according to some example embodiments. The data structure  700  may be generated by the data structure machine  610  and stored in the database  670 . Included in the data structure  700  are the property data  112  (of the item  110 ), the property data  122  (of the characteristic  120 ), the property data  132  (of the descriptor  130 ), and the property data  142  (of the feature  140 ). 
     The property data  112  (of the item  110 ) includes an attribute-value pair that specifies the attribute  114  and its corresponding value  116 . The data structure  700  includes information correlating the value  116  with the property data  122  (of the characteristic  120 ). For example, the information may be a reference (e.g., a pointer) to the property data  122 . Additional attribute-value pairs are also shown. 
     The property data  122  (of the characteristic  120 ) includes an attribute-value pair that specifies the attribute  124  and its corresponding value  126 . The data structure  700  includes information correlating the value  126  with the property data  132  (of the descriptor  130 ). For example, the information may be a reference (e.g., a pointer) to the property data  132 . Additional attribute-value pairs are also shown. 
     The property data  132  (of the descriptor  130 ) includes an attribute-value pair that specifies the attribute  134  and its corresponding value  136 . The data structure  700  includes information correlating the value  136  with the property data  142  (of the feature  140 ). For example, the information may be a reference (e.g., a pointer) to the property data  142 . Additional attribute-value pairs are also shown. 
     The property data  142  (of the feature  140 ) includes an attribute-value pair that specifies the attribute  144  and its corresponding value  146 . The data structure  700  may include information correlating the value  146  with other property data contained in the data structure  700 , in another data structure elsewhere, or both. Additional attribute-value pairs are also shown. 
       FIG. 8  is a block diagram illustrating components of the data structure machine  610 , according to some example embodiments. The data structure machine  610  includes an access module  810 , a generator module  820 , a search module  830 , and a recommendation module  840 , all configured to communicate with each other (e.g., via a bus, a shared memory, or a switch). Any one or more of these modules may be implemented using hardware or a combination of hardware and software. Moreover, any two or more of these modules may be combined into a single module, and the functions described herein for a single module may be subdivided among multiple modules. 
     The access module  810  is configured to access various property data (e.g., property data  112 ,  122 ,  132 , or  142 ). In various example embodiments, the access module  810  is further configured to receive information usable to generate (e.g., create or modify) one or more properties (e.g., property data  112 ,  122 ,  132 , or  142 ). The information may be received, in whole or in part, from different machines (e.g., partially from the vendor machine  620 , partially from the vendor machine  630 , partially from the manufacturer machine  640 , partially from the information service machine  650 , and partially from the client machine  660 ). Accordingly, the access module  810  may receive full or partial updates to the data structure  700 , and these updates may be received from one or more sources (e.g., the different machines shown in  FIG. 6 ). 
     The generator module  820  is configured to generate the data structure  700  based on property data (e.g., property data  112 ,  122 ,  132 , or  142 ) accessed by the access module  810 . Specifically, the generator module  820  may generate the data structure  700  based on one or more attributes (e.g., attributes  114 ,  124 ,  134 , or  144 ) specified by the property data, one or more values (e.g., values  116 ,  126 ,  136 , or  146 ) specified by the property data, or any suitable combination thereof. The generator module  820  is further configured to store the data structure  700  in the database  670  as a composite property of the item  110 . 
     Where the access module  810  receives information usable to generate one or more properties (e.g., property data  112 ,  122 ,  132 , or  142 ), the generator module  820  is configured to generate one or more properties (e.g., property data  112 ,  122 ,  132 , or  142 ) based on the received information. Specifically, the generator module  820  may generate one or more attributes, one or more values, or any suitable combination thereof, based on the received information. Generation of an attribute or a value, as discussed herein, includes modification (e.g., updating) of an existing attribute or value (e.g., already stored in the database  670 ), as well as creation of a new attribute or value. Accordingly, the generator module  820  may update the data structure  700 , in whole or in part, in response to the access module  810  receiving information from one or more sources (e.g., the different machines shown in  FIG. 6 ). 
     The search module  830  is configured to index the data structure  700  generated by the generator module  820 . The search module  830  may index the data structure  700  based on one or more values (e.g., values  116 ,  126 ,  136 , or  146 ) specified (e.g., contained) therein. The search module  830  may receive a search request (e.g., in the form of one or more search terms submitted by a user of the client machine  660 ) and, in response to the search request, perform a query of the database  670  to identify one or more items. The search module  830  may determine that one or more of the values matches (e.g., identically or non-identically) the search request (e.g., matches one or more search terms of the search request) and accordingly identify the item  110  based on the matching value (e.g., value  126 , value  136 , or value  146 ). 
     The recommendation module  840  is configured to identify one or more further items based on the matching value determined by the search module  830 . The one or more further items identified by the recommendation module  840  may constitute a virtual product, and the one or more further items may be presented as one or more instances of the virtual product. For example, the recommendation module  840  may transmit a description (e.g., in a listing or in an advertisement) of one of the further items to the client machine  660  for presentation to a user of the client machine  660 . 
       FIG. 9-12  are flowcharts illustrating a method  900  of structuring data as a composite property of the item  110 , according to some example embodiments. Operations in the method  900  may be performed by the data structure machine  610 , using modules described above with respect to  FIG. 8 , as appropriate. 
     As shown in  FIG. 9 , the method  900  includes operations  910 ,  920 ,  930 , and  940 . In operation  910 , the access module  810  accesses the property data  112  of the item  110 . The access module  810 , in operation  920 , accesses the property data  122  of the characteristic  120 . In one or both of the operations  910  and  920 , accessing property data may include reading information from memory (e.g., a memory of the data structure machine  610 ), reading information from a database (e.g., database  670 ), receiving information (e.g., from the vendor machine  620 ), or any suitable combination thereof. 
     In operation  930 , the generator module  820  generates the data structure  700  based on the property data  122  accessed by the access module  810  in operation  920 . For example, the data structure  700  may be generated based on the value  126  specified by the property data  122 . The generator module  820  may reference the item  110  in the data structure  700 , such that the data structure  700  is a data structure of the item  110 . In operation  940 , the generator module  820  stores the data structure  700  in the database  670  as a composite property of the item  110 . 
     In an example of method  900  shown in  FIG. 10 , the property data  112  and  122  may already be generated (e.g., stored in the database  670 ), although the data structure  700  is not necessarily generated yet (e.g., as a result of a previous execution of the method  900 ). Specifically, the values  116  and  126  may be subject to potential modification (e.g., updating). 
     Operation  902  involves receiving an update of the value  116  and an update of the value  126 , and the operation  902  may be performed by the access module  810 . As an example, the access module  810  may receive the update of the value  116  from the vendor machine  620 , and may receive the update of the value  126  from the vendor machine  630 . In operation  904 , the generator module  820  modifies the value  116  and modifies the value  126 , based on the updates received in operation  902  by the access module  810 . 
     As shown in  FIG. 10 , operations  910 - 940  are to be executed subsequent to operation  904 . The operations  910 - 940  may be performed as described above with respect to  FIG. 9 . 
     In operation  932 , the generator module  820  generates further property data of the item  110 . The further property data may specify an identifier of the data structure  700  (e.g., an item number or a product number), and this identifier may be communicated (e.g., by the generator module  820 ) to any one or more of the machines shown in  FIG. 6 . As a result, information received by the access module  810  in operation  902  (e.g., in a subsequent execution of the method  900 ) may be identified by the access module  810  as being directed to the data structure  700 , which corresponds to the item  110 . This may have the effect of enabling different entities (e.g., using the vendor machines  620  and  630 , the manufacturer machine  640 , the information service machine  650 , or the client machine  660 ) to individually and separately submit an update for the data structure  700 . 
     In operation  950 , the search module  830  indexes the data structure  700  based on the value  126 . The search module  830 , in operation  960 , performs a query of the database  670  based on the value  126  (e.g., in response to a search request), and in operation  970  identifies the item  110  based on some or all of the property data  122  (e.g., based on the value  126 ). 
     In operation  980 , the recommendation module  840  identifies a further item based on some or all of the property data  122  (e.g., based on the value  126 ). The recommendation module  840 , in operation  990 , presents the further item as an instance of a virtual product. In some example embodiments, the recommendation module  840  also presents the item  110  as an instance of the same virtual product. In certain example embodiments, the recommendation module  840  presents the item  110  as an instance of a product that is related to the virtual product. Alternatively, the recommendation module  840  may present the item  110  as an instance of a project that is unrelated to the virtual product (e.g., as a serendipitous recommendation). 
     In an example of method  900  shown in  FIG. 11 , the property data  122  and  132  is not necessarily generated yet, and the data structure  700  is not necessarily generated yet (e.g., as a result of a previous execution of the method  900 ). Specifically, the values  126  and  136  may be subject to potential generation (e.g., creation or modification). 
     Operation  901  may be performed by the access module  810  and involves receiving information pertinent to the item  110  from a vendor of the item  110  (e.g., from the vendor machine  620 ). In operation  903 , the generator module  820  generates the value  126  based on the information received in operation  901 . Similarly, in operation  905 , the generator module  820  generates the value  136  based on the received information. 
     In operation  909 , the generator module  820  accesses the property data  132  of the descriptor  130 . The property data  132  specifies the value  136  generated in operation  905 . 
     As shown in  FIG. 11 , operations  910 - 940  are to be executed subsequent to operation  909 . The operations  910 - 940  may be performed as described above with respect to  FIG. 9  or  FIG. 10 . 
     In operation  952 , the search module  830  indexes the data structure  700  based on the value  136 . The search module  830 , in operation  962 , performs a query of the database  670  based on the value  136  (e.g., in response to a search request), and in operation  972  identifies the item  110  based on some or all of the property data  132  (e.g., based on the value  136 ). 
     In operation  982 , the recommendation module  840  identifies a further item based on some or all of the property data  132  (e.g., based on the value  136 ). The recommendation module  840 , in operation  992 , presents the further item as an instance of a virtual product. In some example embodiments, the recommendation module  840  also presents the item  110  as an instance of the same virtual product. In certain example embodiments, the recommendation module  840  presents the item  110  as an instance of a product that is related to the virtual product. Alternatively, the recommendation module  840  may present the item  110  as an instance of a product that is unrelated to the virtual product (e.g., as a serendipitous recommendation). 
     In an example of method  900  shown in  FIG. 12 , the property data  122  and  132  may be already generated (e.g., stored in the database  670 ), although the data structure  700  is not necessarily generated yet. Specifically, the values  126  and  136  may be subject to potential modification (e.g., updating). 
     Operation  907  involves receiving an update of the value  126  and an update of the value  136 , and the operation  907  may be performed by the access module  810 . As an example, the access module  810  may receive the update of the value  126  from the vendor machine  630 , and may receive the update of the value  136  from the manufacturer machine  640 . In operation  908 , the generator module  820  modifies the value  126  and modifies the value  136 , based on the updates received in operation  907  by the access module  810 . 
     As shown in  FIG. 12 , operations  910 - 940  are to be executed subsequent to operation  908 . The operations  910 - 940  may be performed as described above with respect to  FIG. 9 ,  10 , or  11 . 
     Moreover, as shown in  FIG. 12 , operations  932 ,  950 ,  960 ,  970 ,  980 , and  990  may be performed as described above with respect to  FIG. 10 , and operations  952 ,  962 ,  972 ,  982 , and  992  may be performed as described above with respect to  FIG. 11 . 
     According to various example embodiments, one or more of the methodologies described herein may facilitate the provision of recommendations for products, items, or both, to a user (e.g., of the client machine  660 ). This may have the effect of providing recommendations that are perceived by the user as being enhanced (e.g., more interesting, more unexpected, or more instructive) compared to existing recommendation technology. Provision of such enhanced recommendations may therefore result in a reduction in search time spent by the user in identifying a desirable item or product. Accordingly, one or more of the methodologies discussed herein may have the technical effect of reducing demand for one or more computing resources used by one or more devices within the system  100  (e.g., the client machine  660 ). Examples of such computing resources include processor cycles, network traffic, memory usage, storage space, power consumption, and cooling capacity. 
       FIG. 13  illustrates components of a machine  1300 , according to some example embodiments, that is able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,  FIG. 13  shows a diagrammatic representation of the machine  1300  in the example form of a computer system and within which instructions  1324  (e.g., software) for causing the machine  1300  to perform any one or more of the methodologies discussed herein may be executed. In alternative embodiments, the machine  1300  operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine  1300  may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine  1300  may be a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, a smartphone, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions  1324  (sequentially or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions  1324  to perform any one or more of the methodologies discussed herein. 
     The machine  1300  includes a processor  1302  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a radio-frequency integrated circuit (RFIC), or any suitable combination thereof), a main memory  1304 , and a static memory  1306 , which are configured to communicate with each other via a bus  1308 . The machine  1300  may further include a graphics display  1310  (e.g., a plasma display panel (PDP), a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)). The machine  1300  may also include an alphanumeric input device  1312  (e.g., a keyboard), a cursor control device  1314  (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument), a storage unit  1316 , a signal generation device  1318  (e.g., a speaker), and a network interface device  1320 . 
     The storage unit  1316  includes a machine-readable medium  1322  on which is stored the instructions  1324  (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions  1324  may also reside, completely or at least partially, within the main memory  1304 , within the processor  1302  (e.g., within the processor&#39;s cache memory), or both, during execution thereof by the machine  1300 . Accordingly, the main memory  1304  and the processor  1302  may be considered as machine-readable media. The instructions  1324  may be transmitted or received over a network  1326  (e.g., network  190 ) via the network interface device  1320 . 
     As used herein, the term “memory” refers to a machine-readable medium able to store data temporarily or permanently and may be taken to include, but not be limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, and cache memory. While the machine-readable medium  1322  is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions (e.g., instructions  1324 ). The term “machine-readable medium” shall also be taken to include any medium that is capable of storing instructions (e.g., software) for execution by the machine, such that the instructions, when executed by one or more processors of the machine (e.g., processor  1302 ), cause the machine to perform any one or more of the methodologies described herein. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, a data repository in the form of a solid-state memory, an optical medium, a magnetic medium, or any suitable combination thereof. 
     Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. 
     Certain embodiments are described herein as including logic or a number of components, modules, or mechanisms. Modules may constitute either software modules (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware modules. A “hardware module” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various example embodiments, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein. 
     In some embodiments, a hardware module may be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations. For example, a hardware module may be a special-purpose processor, such as a field programmable gate array (FPGA) or an ASIC. A hardware module may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware module may include software encompassed within a general-purpose processor or other programmable processor. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations. 
     Accordingly, the term “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. As used herein, “hardware-implemented module” refers to a hardware module. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different hardware modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time. 
     Hardware modules can provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). 
     The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented modules that operate to perform one or more operations or functions described herein. As used herein, “processor-implemented module” refers to a hardware module implemented using one or more processors. 
     Similarly, the methods described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other embodiments the processors may be distributed across a number of locations. 
     The one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an application program interface (API)). 
     The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations. 
     Some portions of this specification are presented in terms of algorithms or symbolic representations of operations on data stored as bits or binary digital signals within a machine memory (e.g., a computer memory). These algorithms or symbolic representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. As used herein, an “algorithm” is a self-consistent sequence of operations or similar processing leading to a desired result. In this context, algorithms and operations involve physical manipulation of physical quantities. Typically, but not necessarily, such quantities may take the form of electrical, magnetic, or optical signals capable of being stored, accessed, transferred, combined, compared, or otherwise manipulated by a machine. It is convenient at times, principally for reasons of common usage, to refer to such signals using words such as “data,” “content,” “bits,” “values,” “elements,” “symbols,” “characters,” “terms,” “numbers,” “numerals,” or the like. These words, however, are merely convenient labels and are to be associated with appropriate physical quantities. 
     Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or any suitable combination thereof), registers, or other machine components that receive, store, transmit, or display information. Furthermore, unless specifically stated otherwise, the terms “a” or “an” are herein used, as is common in patent documents, to include one or more than one instance. Finally, as used herein, the conjunction “or” refers to a non-exclusive “or,” unless specifically stated otherwise.