US20150310392A1 - Job recommendation engine using a browsing history - Google Patents

Job recommendation engine using a browsing history Download PDF

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Publication number
US20150310392A1
US20150310392A1 US14/274,387 US201414274387A US2015310392A1 US 20150310392 A1 US20150310392 A1 US 20150310392A1 US 201414274387 A US201414274387 A US 201414274387A US 2015310392 A1 US2015310392 A1 US 2015310392A1
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Prior art keywords
job
user
postings
listing
posting
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Lili Wu
Samir M. Shah
Sean Seol Woong Choi
Vaibhav Goel
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Microsoft Technology Licensing LLC
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LinkedIn Corp
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Priority to US14/274,387 priority Critical patent/US20150310392A1/en
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Publication of US20150310392A1 publication Critical patent/US20150310392A1/en
Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC reassignment MICROSOFT TECHNOLOGY LICENSING, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: LINKEDIN CORPORATION
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/105—Human resources
    • G06Q10/1053—Employment or hiring
    • C—CHEMISTRY; METALLURGY
    • C08—ORGANIC MACROMOLECULAR COMPOUNDS; THEIR PREPARATION OR CHEMICAL WORKING-UP; COMPOSITIONS BASED THEREON
    • C08J—WORKING-UP; GENERAL PROCESSES OF COMPOUNDING; AFTER-TREATMENT NOT COVERED BY SUBCLASSES C08B, C08C, C08F, C08G or C08H
    • C08J5/00—Manufacture of articles or shaped materials containing macromolecular substances
    • C08J5/18—Manufacture of films or sheets
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10T—TECHNICAL SUBJECTS COVERED BY FORMER US CLASSIFICATION
    • Y10T428/00—Stock material or miscellaneous articles
    • Y10T428/14—Layer or component removable to expose adhesive

Definitions

  • the subject matter disclosed herein generally relates to a system and method for recommending jobs to a user of a job posting service, a social networking service, and/or business networking service, based on a browsing history of the user.
  • Job postings to advertise an available job and solicit applications for the job are well known. Job postings have been incorporated into newspapers, periodicals, and the like for many years. More recently, search engines and websites related to facilitating job searching have presented available jobs electronically. In such circumstances, entities advertising jobs conventionally pay a fee to the owner of the platform on which the advertisement is to be displayed. As a result, advertisements may be displayed generally to most if not all of the users who access the platform.
  • FIG. 1 is a block diagram of a system including user devices and a social network server.
  • FIG. 2 is a block diagram illustrating various components of a social networking server.
  • FIG. 3 is a block diagram showing some of the functional components or modules that comprise a recommendation engine.
  • FIGS. 4A , 4 B, and 4 C are example user interfaces that can be displayed by the social network on the user device.
  • FIGS. 5A , 5 B, and 5 C are a block diagram illustrating features and operations of a process and system for recommending job postings to a user.
  • FIG. 6 is a block diagram illustrating components of a machine able to read instructions from a machine-readable medium.
  • Example methods and systems are directed to recommending jobs to a user based on a browsing history of the user. 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.
  • While broad dissemination of a job posting may be desirable in certain circumstances, it may be inefficient for a job-posting entity to display jobs to users who have no interest in a particular job. This lack of interest could be because the posted job is not within the user's current area of expertise and experience, or because the job is not within the area that the user is hoping to move into. Consequently, a system has been developed that displays job postings to users and/or members of a social or business network based on a browsing history (of posted jobs) of the user, and in an embodiment, on jobs viewed by one or more other users who have also viewed a job that was viewed by the user. In this way, a user or member may receive additional pertinent job postings based on the user's job browsing history.
  • job posting entities may sponsor job postings on a social or business network or on a platform with access to the user's browsing history with the increased expectation that the expense of sponsoring a job posting may be relatively more likely to result in the job posting being presented to a user who has an interest in the job posting.
  • FIG. 1 is a block diagram of a system 100 including user devices 102 and a social network and/or business network server 104 .
  • User devices 102 can be a personal computer, netbook, electronic notebook, smartphone, or any electronic device known in the art that is configured to display web pages.
  • the user devices 102 can include a network interface 106 that is communicatively coupled to a network 108 , such as the Internet.
  • the social network server 104 can be communicatively coupled to the network 108 .
  • the server 104 can be an individual server or a cluster of servers, and can be configured to perform activities related to serving the social network, such as storing social network information, processing social network information according to scripts and software applications, transmitting information to present social network information to users of the social network, and receive information from users of the social network.
  • the server 104 can include one or more electronic data storage devices 110 , such as a hard drive, and can include a processor 112 .
  • the social network server 104 can store information in the electronic data storage device 110 related to users and/or members of the social network, such as in the form of user characteristics corresponding to individual users of the social network.
  • the user's characteristics can include one or more profile data points, including, for instance, name, age, gender, profession, prior work history or experience, educational achievement, location, citizenship status, leisure activities, likes and dislikes, and so forth.
  • the user's characteristics can further include behavior or activities within and without the social network, as well as the user's social graph.
  • the information can include name, offered products for sale, available job postings, organizational interests, forthcoming activities, and the like.
  • the job posting can include a job profile that includes one or more job characteristics, such as, for instance, area of expertise, prior experience, pay grade, residency or immigration status, and the like.
  • the recommendation engine provides a recommendation service that can be customized for use with multiple applications or services.
  • a recommendation entity can be a collection of information organized around a particular concept that is supported by the system 100 in general, such as the user browsing history, and the recommendation engine in particular.
  • the general recommendation engine may execute in real-time or as a background operation, such as offline or as part of a batch process. In some examples that incorporate relatively large amounts of data to be processed, the general recommendation engine may execute via a parallel or distributed computing platform.
  • FIG. 2 is a block diagram illustrating various components of a social networking server 104 with a recommendation engine 200 for identifying similarities between different recommendation entity types, such as job postings previously viewed by a user (as represented by a job browsing history), job postings not yet viewed by the user, and job postings viewed by one or more other users who have also viewed the job postings viewed by the user.
  • the social networking server 104 is based on a three-tiered architecture, consisting of a front-end layer, application logic layer, and data layer.
  • FIG. 2 can represent a set of executable software instructions and the corresponding hardware (e.g., memory and processor) for executing the instructions.
  • various functional modules and engines that are not germane to conveying an understanding of the inventive subject matter have been omitted from FIG. 2 .
  • additional functional modules and engines may be used with a social networking server 104 such as that illustrated in FIG. 2 , to facilitate additional functionality that is not specifically described herein.
  • the various functional modules and engines depicted in FIG. 2 may reside on a single server computer, or may be distributed across several server computers in various arrangements.
  • the front end of the social network server 104 consists of a user interface module (e.g., a web server) 202 , which receives requests from various client computing devices, and communicates appropriate responses to the requesting client devices.
  • the user interface module(s) 202 may receive requests in the form of Hypertext Transport Protocol (HTTP) requests, or other web-based, application programming interface (API) requests.
  • the application logic layer includes various application server modules 204 , which, in conjunction with the user interface module(s) 200 , generates various user interfaces (e.g., web pages) with data retrieved from various data sources in the data layer.
  • individual application server modules 204 are used to implement the functionality associated with various services and features of the system 100 . For instance, the ability to identify a particular job posting by a user may be a service implemented in an independent application server module 204 . Similarly, other applications or services that utilize the recommendation engine 200 may be embodied in their own application server modules 204 .
  • the data layer can include several databases, such as a database 208 for storing recommendation data 210 , such as the browsing history of users, data pairs of jobs viewed by users, user characteristics (and/or user preferences), and job profiles, and can further include additional social network information, such as interest groups, companies, advertisements, events, news, discussions, tweets, questions and answers, and so forth.
  • the recommendation entity data is processed in the background (e.g., offline) to generate pre-processed entity data that can be used by the recommendation engine, in real-time, and to make recommendations generally.
  • the recommendation engine 200 may retrieve a job browsing history profile of a user, compare a job in a user's job browsing history profile to an element in a job data pair, and recommend the other job posting represented by the other element in the job data pair (which represents another job posting viewed by one or more other users who also have viewed the job represented by the first element in the job data pair).
  • a person when a person initially registers to become a user (and/or member) of the system 100 , the person can be prompted to provide some personal information, such as his or her name, age (such as by birth date), gender, interests, contact information, home town, address, the names of the user's spouse and/or family users, educational background (such as schools, majors, etc.), employment history, skills, professional organizations, and so on.
  • This information can be stored, for example, in the database 208 .
  • the server 104 can include a job poster interface 205 , such as with a user interface coupled to the server 104 or via the network interface 106 .
  • the user interface can include a conventional keyboard and display configuration well known in the art.
  • the job poster interface 205 provides an interface for the posting of jobs, including a corresponding job profile, on the social network.
  • the job poster interface 205 is coupled to a data management system 206 .
  • the data management system 206 can incorporate data management technologies well known in the art or can incorporate proprietary data management structures.
  • the data management system 206 incorporates SAS, or Statistical Analysis System data management systems, to promote business analysis, statistical analysis, data storage and recovery, and the like for job information.
  • the data management system 206 can include the capacity for social network administrators to utilize the data generated by the data management system 206 , such as by inputting tasks into the data management system 206 .
  • the job poster interface 205 and the data management system 206 can both be coupled to the database 208 .
  • the job poster interface 205 can transmit job data, such as job profiles, to the database 208 for storage without respect to data management activities.
  • the data management system 206 can store job data in the database 208 upon the job data having been acted upon for data management analysis.
  • the network interface 106 can provide the input of user data, such as user browsing data or user characteristics, into the social network.
  • the user data can be stored in the database 208 or can be directly transmitted to the recommendation engine 200 for cross reference against the job data pairs stored in the database 208 . Jobs identified by the recommendation engine 200 can be transmitted via the network interface 106 to the user device 102 for presentation to the user.
  • a job analytics system 207 can track the occurrence of jobs that have been presented to or selected by a user.
  • the job analytics system 207 can track how many times a job has been presented, how many times a job has been selected or “clicked” on by a user, bill a job presenting entity accordingly, and adjust the remaining number of times the job has left to be presented or selected accordingly.
  • the job analytics system 207 can further monitor which jobs are posted to users to seek to prevent job postings from being duplicated to a single user, as well as record analytical information related to the number of times, for instance, that a presented job has been clicked on by a user and the user characteristics of users who have clicked on job presentations.
  • the job analytics system 207 can present the same job to the same user a predetermined number of times or until the user clicks on the job to learn more.
  • the job analytics system 207 can further terminate job bids that, for instance, meet a termination date or are being presented to users unsatisfactorily frequently or unsuccessfully. Additionally, the job analytics system 207 may be utilized to renew or extend job bids, such as at the direction of the job presenting entity or the social network administrator.
  • the recommendation engine 200 when a user accesses the social network, the recommendation engine 200 cross references the user's job browsing history profile against some or all of the job data pairs in the database 208 .
  • the recommendation engine 200 can generate a relevance factor for each of the cross referenced job postings.
  • the recommendation engine 200 can utilize the processor 112 and can manipulate the relevance for the job postings and/or recommendations.
  • the relevance of a job recommendation to a user may factor in previous success that the job posting has had with other users. If a large percentage of users who are presented with a job posting based on the job profile select the job posting for more information, then the job posting may be deemed more relevant. A so-called “click-through rate” that exceeds a threshold may result in the relevance of the job posting being increased, while a click-through rate less than a threshold may result in the relevance of the job posting being reduced.
  • the characteristics of other users of the social network who do select a job posting for more information can also be incorporated into determining the relevance for another particular user.
  • Characteristics of users who have selected a particular job posting in the past can be compared against user characteristics (and/or user preferences) of a prospective user.
  • user characteristics of a prospective user are or are not related to the user characteristics of users who have selected a job posting in the past
  • the relevance of the job posting may similarly be increased or decreased for a prospective user.
  • the server 104 may store characteristics of users who have selected the job posting in the past and may develop composite user characteristics. The degree to which the characteristics of a prospective user match the composite characteristics may weigh the results of the recommendation engine more heavily for a given prospective user.
  • FIG. 3 is a block diagram showing some of the functional components or modules that comprise a recommendation engine 200 , in some examples, and illustrates the flow of data that occurs when performing various operations of a method for identifying and presenting job postings based on a user's job browsing history profile (and commonly viewed job postings by other users).
  • the recommendation engine 200 can be coupled to an external data source 310 , and can consist of two primary functional modules—an extraction engine 300 and a matching engine 302 .
  • the extraction engine 300 can extract job data pairs having a particular element (that represents a particular job posting), and then the matching engine 302 can perform a particular type of matching operation, under the direction of a particular configuration file 304 , that is specific to the requesting application (such as finding other data pairs that include the particular element). Accordingly, depending upon the particular inputs to the recommendation engine 200 and the desired outputs, different configuration files 304 may be used to compare different characteristics of different recommendation entities.
  • FIG. 4A is a depiction of a user interface screen 400 that can be displayed by the social network, business network, or job posting service on the user device 102 corresponding to a user.
  • FIG. 4C is a depiction of the user interface screen on a mobile device.
  • the user interface screen 400 can occupy the entire screen of the user device 102 .
  • the user interface screen 400 can be a sub-portion of a larger user interface screen displaying additional information related to the social and/or business network.
  • the social network server 104 can transmit the jobs to the user device 102 , such as along with other social network information that is displayed on a user interface, such as a display screen, of the user device 102 .
  • the user interface screen 400 includes a list 405 of jobs.
  • the list 405 is an ordered list based on various criteria.
  • jobs 410 , 415 are displayed at the top of the list, i.e., most prominently on the list 405 .
  • the jobs 410 , 415 include a company name 411 , a job title 412 , and a job location 413 .
  • jobs 420 , 425 are displayed less prominently than the jobs 410 , 415 .
  • the jobs 420 , 425 also include a company name 411 , a job title 412 , and a job location 413 .
  • user interface 400 B When a user clicks on a job posting displayed on the user interface 400 such as job posting 415 , user interface 400 B is displayed on the screen of the user device 102 .
  • the user interface 400 B displays the company name 411 , the job title 412 , and the job location 413 .
  • the user interface further displays a detailed job description 430 , job duties 435 , and job qualifications 440 . If the user would like to apply for this job 412 after reviewing the job description 430 , job duties 435 , and job qualifications 440 , the user can click on an Apply Now button 450 which will initiate a job application process for the user.
  • sub-display 460 the system displays on the user interface other jobs that the user may be interested in.
  • these other jobs are jobs that have been viewed by one or more other users who have also viewed the job 412 that the user has clicked on and for which the details are being displayed to the user as a job description 430 , job duties 435 , and job qualifications 440 .
  • These other jobs are identified by a company name 462 and a job title 464 .
  • the user can click on one of these other jobs, and details of the other job will be displayed to the user such as the job description 430 , job duties 435 , and job qualifications 440 .
  • an additional list of others jobs 460 can be displayed to the user (based on the other job selected by the user).
  • FIGS. 5A , 5 B, and 5 C are a block diagram illustrating features of a process and system to create a job browsing history profile for a user and to use that job browsing history profile in connection with recommending job postings to the user.
  • FIGS. 5A , 5 B, and 5 C include a number of process blocks 505 - 580 . Though arranged serially in the example of FIGS. 5A , SB, and SC, other examples may reorder the blocks, omit one or more blocks, and/or execute two or more blocks in parallel using multiple processors or a single processor organized as two or more virtual machines or sub-processors. Moreover, still other examples can implement the blocks as one or more specific interconnected hardware or integrated circuit modules with related control and data signals communicated between and through the modules. Thus, any process flow is applicable to software, firmware, hardware, and hybrid implementations.
  • identifications and attributes of job postings that have been viewed by a user in a job posting service are stored in a database.
  • Job postings are listings of job positions that an employer is looking to fill. These postings are normally posted in an online or web-based environment.
  • the identifications and attributes of the job postings can relate to, for example, the company offering the job, a job title, education required for the job, work experience required for the job, skills required for the job, and salary information.
  • the user can be a user or member of a social networking service and/or a business networking service.
  • the job posting service can be an independent web-based job posting service, or the job posting service can be affiliated with the social networking service or the business networking service.
  • a job browsing history profile is created for the user using the identifications and attributes of the posted jobs that the user has viewed.
  • the job browsing history profile provides an overview of the types of jobs and the specific jobs that a user has viewed.
  • the job browsing history profile is created without regard to any personal profile of the user.
  • the job browsing history profile is compared to job postings in the job posting service. In an embodiment, these job postings in the job posting service have not been viewed by the user. After this comparison, at 520 , a listing of job postings based on the comparison is generated. As noted at 521 , the listing of job postings is generated when the attributes of the job postings in the user's job browsing history profile are similar to attributes of the job postings in the job posting service.
  • the job postings that have not been viewed by the user could be newly listed job postings in the job posting service.
  • the job postings that have not been viewed by the user could be job postings that the user's searches have not yet located, or job postings that the user has located but has not yet viewed or clicked on.
  • the listing of job postings is displayed to the user.
  • the user selects a first job posting from the listing of job postings, and at 531 , a second listing of job postings is displayed to the user.
  • the second listing of job postings includes job postings viewed by one or more other users who have also viewed the first job posting. For example, if the user views Job A in the job posting service, one or more other users who have view Job A also have viewed Jobs B, C, and D in the job posting service, and in an embodiment the user has not viewed Jobs B. C, and D, then Jobs B, C, and D can be recommended to the user. In an embodiment, other factors can be considered before Jobs B, C, and D are recommended to the user, such as when the other users viewed Jobs B. C, and D and how many other users have viewed Jobs B, C, and D.
  • the second listing of job postings is created as follows.
  • data relating to the one or more other users' viewing of job postings in the job posting service are stored in a database, and at 541 , a plurality of data pairs is created.
  • the data pairs consist of a first element including the first job posting and a second element including a second job posting.
  • the display of the second listing of job postings to the user is based on data pairs that have as the first element the first job posting selected by the user, and another job that has not been viewed by the user (but which has been viewed by other users who also viewed the first job posting).
  • job pairs For example, if another user views Jobs A, B, and C, the following job pairs will be created—(A, B), (A, C), (B, A), (B, C), (C, A), and (C, B).
  • “duplicate” pairs are removed, such as (B, A) in light of (A, B).
  • the system checks both the first and second elements of the set of data pairs, so that the pair (A,B) will indicate that Job A should also be recommended to the user (if the user has not already viewed Job A).
  • both the first job posting and the second job posting have been viewed by at least one other user.
  • a date is recorded when job postings have been viewed by one or more other users.
  • jobs in the second listing of job postings that have been viewed by the one or more other users after a threshold date are displayed to the user. This feature assures that only job postings that have recently been viewed by others are displayed to the user, thereby imparting an indication of a “fresh” interest in the job postings that are displayed to the user.
  • a new job posting is added to the second listing of job postings based on a comparison of attributes of a new job posting to attributes of the job postings in the second listing of job postings.
  • This feature permits a new job posting to be recommended to the user immediately upon its addition by the job posting service.
  • a data pair including the first job posting and the new job posting is generated when the attributes of the new job posting are similar to attributes of the first job posting. For example, if a new job C is added by the job posting service, and the new job C is similar to a current job posting B, then if job posting B is recommended to the user, then job posting C may be recommended to the user also.
  • a new job pair (A, C) would be formed based on the similarities of job postings B and C.
  • a plurality of job postings from a business organization are aggregated into a single listing among the second listing of job postings displayed to the user.
  • This feature reduces screen clutter and/or permits the display of job listings from more business organizations. This feature can be particularly useful when the job listings from the business organization are of the same or similar job title or job function. A user can click on this aggregated listing to view the job postings within the listing.
  • the aggregation of the job listings for a business organization are aggregated or categorized into different titles, functions, sub-titles, and sub-functions.
  • the job posting for a business organization may be aggregated into marketing, finance, human resource, and software development categories, and the software development category can further be divided into communications, web development, and mobile areas.
  • a user can modify the job browsing history profile, and at 556 , the modified job browsing history profile is used in connection with displaying to the user the second or recommended listing of job postings.
  • This feature permits a user to actively affect the job postings that are recommended to the user. For example, if the user has been searching for a particular type of job over several weeks, and has not found anything to his or her liking, then the user can decide that a different type of job should be considered.
  • the attributes of that different type of job can be input into the job browsing history profile, and in a very short time, job postings relating to that different type of job will be recommended to the user based on the modified job browsing profile history.
  • a user can delete his or her browsing history or a portion of the browsing history. For example, if a user was browsing job profiles just out of curiosity and not in relation to a serious job search, the user can remove these browsed jobs from the browsing history.
  • the system implements a term frequency-inverse document frequency (tf-idf) protocol.
  • tf-idf term frequency-inverse document frequency
  • This causes the system to operate in a manner such that a frequently viewed job posting by other users does not appear in the second listing of job postings displayed to the user.
  • One reason for implementing this feature may be that the competition for a highly-viewed job posting may be intense, and other less sought after jobs may be more attainable by the user.
  • the user is permitted to turn this feature on and off.
  • the second listing of job postings is regenerated on a periodic basis. For example, a daily process may be executed in the system that determines additional jobs that have been viewed by one or more other users who have also viewed the job posting viewed by the user. These additional job postings can then be recommended to the user. Each of the new job postings would be included in a new data pair with the other element of the data pair being the job posting viewed by the user.
  • the system uses a click through rate or a view rate when determining the job postings to include in the second listing of job postings. For example, only job postings that one or more other users have actually clicked on to examine in more detail may be recommended to the user (as compared to job postings that have only been presented to and viewed by the other users and not clicked on). In another embodiment, both viewed and clicked on jobs can be recommended to the user, but the clicked on job postings can be ranked or weighted in a different fashion than job postings that have just been viewed. Similarly, as indicated at 575 , the system can generate a preference value for the user for one or more job postings in the second listing of job postings.
  • This feature permits a user to identify particular preferences for the jobs recommended to him or her, such as a geographical preference.
  • the system creates the job browsing history profile after a number of job postings viewed by the user exceeds a threshold. This feature prevents the system from generating a job browsing history profile when there is too little data to generate a reliable profile. For example, a user may initially view job postings related to a particular type of job, but that may have been out of a passing interest rather than a true desire to find a particular type of job.
  • the system may be configured not to create a job browsing history profile based on a passing interest.
  • FIG. 6 is a block diagram illustrating components of a machine 600 , according to some example examples, 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.
  • a machine-readable medium e.g., a machine-readable storage medium
  • FIG. 6 shows a diagrammatic representation of the machine 600 in the example form of a computer system and within which instructions 624 (e.g., software) for causing the machine 600 to perform any one or more of the methodologies discussed herein may be executed.
  • the machine 600 operates as a standalone device or may be connected (e.g., networked) to other machines.
  • the machine 600 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 600 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 624 , sequentially or otherwise, that specify actions to be taken by that machine.
  • the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions 624 to perform any one or more of the methodologies discussed herein.
  • the machine 600 includes a processor 602 (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 604 , and a static memory 606 , which are configured to communicate with each other via a bus 608 .
  • the machine 600 may further include a graphics display 610 (e.g., a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)).
  • a graphics display 610 e.g., a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)
  • the machine 600 may also include an alphanumeric input device 612 (e.g., a keyboard), a cursor control device 614 (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument), a storage unit 616 , a signal generation device 618 (e.g., a speaker), and a network interface device 620 .
  • an alphanumeric input device 612 e.g., a keyboard
  • a cursor control device 614 e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instrument
  • a storage unit 616 e.g., a keyboard
  • a signal generation device 618 e.g., a speaker
  • the storage unit 616 includes a machine-readable medium 622 on which is stored the instructions 624 (e.g., software) embodying any one or more of the methodologies or functions described herein.
  • the instructions 624 may also reside, completely or at least partially, within the main memory 604 , within the processor 602 (e.g., within the processor's cache memory), or both, during execution thereof by the machine 600 . Accordingly, the main memory 604 and the processor 602 may be considered as machine-readable media.
  • the instructions 624 may be transmitted or received over a network 626 via the network interface device 620 .
  • 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 622 is shown in an example 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.
  • machine-readable medium shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., software) for execution by a machine (e.g., machine 600 ), such that the instructions, when executed by one or more processors of the machine (e.g., processor 602 ), cause the machine to perform any one or more of the methodologies described herein.
  • a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices.
  • the term “machine-readable medium” shall accordingly be taken to include, but not be limited to, one or more data repositories in the form of a solid-state memory, an optical medium, a magnetic medium, or any suitable combination thereof.
  • 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.
  • one or more computer systems e.g., a standalone computer system, a client computer system, or a server computer system
  • one or more hardware modules of a computer system e.g., a processor or a group of processors
  • software e.g., an application or application portion
  • a hardware module may be implemented mechanically, electronically, or any suitable combination thereof.
  • a hardware module may include dedicated circuitry or logic that is permanently configured to perform certain operations.
  • 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.
  • 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.
  • 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.
  • “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 a hardware module comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising 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) between or among two or more of 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).
  • a resource e.g., a collection of information
  • processors may be 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.
  • processor-implemented module refers to a hardware module implemented using one or more processors.
  • the methods described herein may be at least partially processor-implemented, a processor being an example of hardware.
  • a processor being an example of hardware.
  • the operations of a method may be performed by one or more processors or processor-implemented modules.
  • 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).
  • SaaS software as a service
  • 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)).
  • API application program interface
  • 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.
  • 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.

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