Patent Application
20140129477
The present disclosure generally relates to data processing systems and techniques for processing and presenting content within an online social network environment. More specifically, the present disclosure relates to methods and systems for analyzing and aggregating information, such as specific outcomes achieved from people associated with an organization. As one example, education and post-graduate position information of individual members of a social network service is aggregated so as to present the aggregated information in an interactive manner that enables members of the social network service to explore a wide variety of university outcome information may be used to rank universities.
A social network service is a computer- or web-based application that enables its members or users to establish links or connections with persons for the purpose of sharing information with one another. In general, a social network service enables people to memorialize or acknowledge the relationships that exist in their “offline” (i.e., real-world) lives by establishing a computer-based representation of these same relationships in the “online” world. Many social network services require or request that each user, sometimes called members, provide personal information about the user, such as professional information including information regarding their educational background, employment positions that the user has held, and so forth. This information is frequently referred to as “profile” information, or “member profile” information. In many instances, social network services enable users, with the appropriate data access rights, to view the personal information (e.g., member profiles) of other users. Although such personal information about individual users can be useful in certain scenarios, it may not provide many insights into “big picture” questions about various professions, careers, and individual jobs or employment positions.
Some embodiments are illustrated by way of example and not limitation in the Figures of the accompanying drawings, in which:
Methods and systems for ranking entities are described. Ranking schools is used as an example. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various aspects of different embodiments of the present invention. It will be evident, however, to one skilled in the art, that the present invention may be practiced without these specific details.
College rankings have become a major force in U.S. higher education, influencing the matriculation decisions of prospective undergraduates. As the significance of university rankings increases, so too does the scrutiny to which the methodologies are subjected. The best-known rankings, produced by U.S. News and World Report, are derived from a weighted aggregation of peer surveys of institution quality, along with data on retention rates, student selectivity, faculty and financial resources, graduation rate performance, and alumni giving.
A new framework may be used to rank universities, by evaluating how well they produce a wide variety of desirable post-graduate outcomes, including degrees from graduate and professional schools, and positions in specific industries and roles. Using data from a professional social network service on tens of millions of professionals, the ranking system creates ten individual sub-rankings of universities, comparing schools by how likely their students are to achieve specific outcomes. More or fewer than ten may be used. The composite overall ranking may be created by an average of the sub-rankings, weighted approximately according to actual prevalence of the outcome in labor data from the U.S. Department of Labor, Bureau of Labor Statistics [2010]. This system uses a huge data set to create concrete, data-driven, outcome rankings that provide unique insights for students that have specific goals, and a generally useful composite ranking for those that do not.
The embodiments have wider application than ranking undergraduate institutions, and may be used for ranking any institutions, regardless of type, such as, without limitation, clubs, teams, social organizations, and other such institutions. When applied to ranking undergraduate institutions, the embodiments are described in the context of members who indicate a bachelor degree on their member profile. However, when ranking other types of institutions, one of ordinary skill in the art will understand that the embodiments may be described in the context of members providing any predetermined indicator on their member profile. Similarly, when ranking undergraduate institutions the sub-rankings of desired outcomes are described in terms of acceptance to graduate schools or jobs obtained in given industries. However, those of ordinary skill in the art will recognize that the desired outcomes will vary by the type of institutions being ranked, with the desired outcomes generally being those outcomes desired by members of the type of institution being ranked. For example, although an embodiment herein describes ranking post-secondary schools, one of ordinary skill in the art will readily recognize that additional embodiments could describe ranking high schools, elementary schools, and even professional certification or accreditation institutions (e.g., LSAT prep, CFA, and the like). In the latter embodiments, one of ordinary skill in the art would recognize the use of schools other than law schools, medical schools, business schools, and other post-graduate schools for the sub-rankings
The method may begin by creating sub-rankings for each desirable outcome, ordering schools by the proportion of undergraduates that go on to achieve the specified result. To do this, the algorithm first identities all of the members of a social network service who have listed a bachelor's degree in their profile, and groups them by undergraduate institution, counting the number of graduates from each school. Schools with under fewer than a threshold number of bachelor's degree holders may be filtered out due to sparsity concerns. In one embodiment, schools with fewer than two-thousand (2,000) bachelor degree holders in the social network service are filtered out, leaving a set of approximately eight hundred (800) schools. Next, for each sub-ranking, schools are ordered by the proportion of students achieving the outcome of the sub-ranking, calculated by dividing the number of degree holders achieving the result by the total number of degree holders from that school. These numbers, and the numbers associated with the sub-rankings in terms of “top” entities, as described in detail below, depend, to some degree, on the size of the social network service database.
Finally, to produce the composite ranking, the individual outcome rankings are aggregated by taking a weighted average of a school's score in each sub-ranking To make the rankings as generally useful as possible, the sub-rankings may be weighted by the prevalence of each outcome, taken from the Career Guide to Industries produced by the U.S. Department of Labor, Bureau of Labor Statistics. The method replaces subjective surveys with objective outcome data from the social network service's database to create concrete and valuable sub-rankings, and it replaces arbitrary aggregation of sub-rankings with intelligently chosen weights based on population data, to create additional value for the prospective student.
The above method is particularly useful since many social network services, and particularly those with a professional or business focus, request, or even require, users to provide various items of personal information, including information concerning a user's educational background, employment history and career. For example, a user may be prompted to provide information concerning the schools and universities attended, the dates or years of attendance, the subject matter concentration (e.g., academic concentration or major), as well as the professional certifications and/or academic degrees that the user has obtained. Similarly, a user may be prompted to provide information concerning the companies for which he or she has worked, the employment positions (e.g., job titles) held, the dates of such employment, the skills obtained, and any special recognition or awards received. The data that is requested and obtained may be structured, or unstructured. Other information may be requested and provided as well, such as a professional summary, which summarizes a user's employment skills and experiences, or an objective or mission statement, indicating the user's professional or career aspirations. For purposes of this disclosure, the above-described data or information is generally referred to as member profile data or member profile information. Furthermore, each individual item of data or information may be referred to as a member profile attribute.
Consistent with some embodiments, a social network service includes a school ranking information aggregation service, which is referred to hereinafter as the “school ranking module” or “school ranking application.” Consistent with some embodiments, the school ranking application analyzes and aggregates the member profile information of all (or some subset of) members of the social network service to provide a rich and easy to access set of tools that enables users to explore and discover a variety of ranking information, and possibly trends, concerning various schools as they relate to industries, professions, employments positions, and/or careers.
As illustrated in
The service 10 includes an external data interface 16 to receive data from one or more externally hosted sources. For instance, with some embodiments, certain information about companies and/or particular job titles or employment positions (e.g., salary ranges) may be obtained from one or more external sources. With some embodiments, such data may be accessed in real-time, while in other embodiments the data may be imported periodically and stored locally at the social network service that is hosting the school ranking application.
With some embodiments, the volume of member profile data that is available for processing is extremely large. Accordingly, as shown in
In addition to normalizing various items of information, with some embodiments, the processing module 18 obtains or otherwise derives a set of school ranking parameters from or based on profile attributes of the members for use in ranking as discussed below. At least with some embodiments, these parameters are updated periodically (e.g., daily, nightly, bi-daily, weekly, every few hours, etc.) to take into account changes members make to their profiles.
School ranking parameters are stored for use with the school ranking application 22, as shown in
As illustrated in
Certain attribute information from the member profiles of members of a social network service are retrieved and analyzed for the purpose of normalizing the information for use with the school ranking application. For instance, with some embodiments, job titles may be specified (as opposed to selected) by the members of the social network service and therefore will not be standardized across companies and industries. As such, with some embodiments, a normalizer module will analyze the profile information from which certain job titles are extracted to ascertain an industry specific job title. Accordingly, with some embodiments, the school ranking application will utilize a set of unique, industry specific job titles. Of course, other attributes may also be normalized when appropriate.
In one embodiment, the first four sub-rankings in the disclosed method judge schools by the proportion of students they produce that attend (1) top business schools, (2) top law schools, (3) top medical schools, and (4) Ph.D. programs. The Ph.D. outcome ranking counts students that achieved Ph.D. degrees at any school, whereas the professional school rankings-law, medicine, and business-only include students that received the professional degree at “top” schools. Top schools may be defined by existing professional school rankings from the US News & World Report [2011]. For law and business schools, schools in the top 25 were in one embodiment treated as “top”, and, for medical schools, schools in the top 50 were treated as “top” due to smaller enrollments. Though these professional schools rankings suffer many of the same shortcomings as the undergraduate rankings, the outcome-based ranking system tempers the small distinctions between positions by treating all schools in the “top” bucket as equal, using the existing rankings as a reasonable snapshot of strong professional schools, not a conclusive ordering.
The remaining six outcomes, which could be in the embodiment under discussion, or in a separate embodiment, come from positions in industry: (5) working for top banking companies, (6) working for top consulting companies, (7) holding a position of executive leadership, (8) working in the higher education industry, (9) working for a top tech company, or (10) working with a job function of writing or journalism. For the banking, consulting and technology industries, only employees of the 25 top companies in that industry may be counted because the desirability of jobs in these industries varies significantly based on the quality of the company. Top companies are calculated by aggregating indicators of a company's quality from the social network service's data, including company followers, company page views, average profile views of employees, and more. As an example of “top” companies, the top 5 companies in consulting, according to this metric, using the database of the largest social networking service, are: Accenture, Deloitte, Mckinsey and Company, The Boston Consulting Group, and Slalom Consulting.
For executive leadership and writing or journalism, employees at any company, in those particular job functions, are counted as achieving that outcome. Similarly, for higher education, anyone in that industry is counted, regardless of institution. These outcomes were not constrained to only “top” companies because these positions are often recognized as desirable across a much larger set of companies and institutions.
A position of executive leadership may include, without limitation, chairman of a corporation, chief executive officer of a corporation, president of a corporation, chief technical officer of a corporation, chief marketing officer of a corporation, vice president of a corporation, general counsel of a corporation, and similar positions. General counsel of a corporation and various partnership levels of a law firm may be included within a given standardized grouping.
In another embodiment, the “top” entities need not be used. That is, instead, of “top” business schools, “top” law schools, “top” medical schools, the method may rank schools by the number of graduates they produce that go on to any business school, any law school, or any medical school. Further, community colleges may be ranked on the number of students transferring to four-year colleges. In another embodiment, the rankings need not be limited to working for top consulting companies, holding a position of executive leadership, or working for a top tech company. Instead, the rankings could be based on the number of graduates working in any consulting company, or any technology company. Further, the rankings can be based on other industries, such as, for example, finance and real estate.
In yet another embodiment, the rankings could be based on the number of graduates that start their own business and, as one choice, employ a number of employees, say ten or more.
Continuing with
Examples of user interfaces are seen at
Using the databases of a professional social network service as of 2011, and using data standardization in use at that period of time,
As discussed above, a composite overall ranking may be created by an average of the sub-rankings, weighted approximately according to actual prevalence of the outcome in labor data from the U.S. Department of Labor, Bureau of Labor Statistics [2010]. This system uses a huge data set to create concrete, data-driven, outcome rankings that provide unique insights for students that have specific goals, and a generally useful composite ranking for those that do not.
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. The modules referred to herein may, in some example embodiments, comprise processor-implemented modules.
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), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., Application Program Interfaces (APIs).)
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 or objects that operate to perform one or more operations or functions. The modules and objects referred to herein may, in some example embodiments, comprise processor-implemented modules and/or objects.
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 operations may be distributed among the one or more processors, not only residing within a single machine or computer, but deployed across a number of machines or computers. 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 at 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 within the context of “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), these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., Application Program Interfaces (APIs)).
The example computer system 1600 includes a processor 1602 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 1601 and a static memory 1606, which communicate with each other via a bus 1608. The computer system 1600 may further include a display unit 1610, an alphanumeric input device 1617 (e.g., a keyboard), and a user interface (UI) navigation device 1611 (e.g., a mouse). In one embodiment, the display, input device and cursor control device are a touch screen display. The computer system 1600 may additionally include a storage device 1616 (e.g., drive unit), a signal generation device 1618 (e.g., a speaker), a network interface device 1620, and one or more sensors 1621, such as a global positioning system sensor, compass, accelerometer, or other sensor.
The drive unit 1616 includes a machine-readable medium 1622 on which is stored one or more sets of instructions and data structures (e.g., software 1623) embodying or utilized by any one or more of the methodologies or functions described herein. The software 1623 may also reside, completely or at least partially, within the main memory 1601 and/or within the processor 1602 during execution thereof by the computer system 1600, the main memory 1601 and the processor 1602 also constituting machine-readable media.
While the machine-readable medium 1622 is illustrated in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention, or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.
The software 1623 may further be transmitted or received over a communications network 1626 using a transmission medium via the network interface device 1620 utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Wi-Fi® and WiMax® networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.
Although an embodiment has been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.
| Number | Date | Country | |
|---|---|---|---|
| 61722036 | Nov 2012 | US |
