Patent Application
20180060433
This application relates to the technical fields of software and/or hardware technology and, in one example embodiment, to system and method to prioritize keywords for use in the context of an on-line social network system.
An on-line social network may be viewed as a platform to connect people in virtual space. An on-line social network may be a web-based platform, such as, e.g., a social networking web site, and may be accessed by a use via a web browser or via a mobile application provided on a mobile phone, a tablet, etc. An on-line social network may be a business-focused social network that is designed specifically for the business community, where registered members establish and document networks of people they know and trust professionally. Each registered member may be represented by a member profile. A member profile may be represented by one or more web pages, or a structured representation of the member's information in XML (Extensible Markup Language), JSON (JavaScript Object Notation) or similar format. A member's profile web page of a social networking web site may emphasize employment history and education of the associated member.
Embodiments of the present invention are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like reference numbers indicate similar elements and in which:
A method and system to prioritize keywords in an on-line social network system is described. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of an embodiment 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.
As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Similarly, the term “exemplary” is merely to mean an example of something or an exemplar and not necessarily a preferred or ideal means of accomplishing a goal. Additionally, although various exemplary embodiments discussed below may utilize Java-based servers and related environments, the embodiments are given merely for clarity in disclosure. Thus, any type of server environment, including various system architectures, may employ various embodiments of the application-centric resources system and method described herein and is considered as being within a scope of the present invention.
For the purposes of this description the phrases “an on-line social networking application” and “an on-line social network system” may be referred to as and used interchangeably with the phrase “an on-line social network” or merely “a social network.” It will also be noted that an on-line social network may be any type of an on-line social network, such as, e.g., a professional network, an interest-based network, or any on-line networking system that permits users to join as registered members. For the purposes of this description, registered members of an on-line social network may be referred to as simply members.
Each member of an on-line social network is represented by a member profile (also referred to as a profile of a member or simply a profile). A member profile may be associated with social links that indicate the member's connection to other members of the social network. A member profile may also include or be associated with comments or recommendations from other members of the on-line social network, with links to other network resources, such as, e.g., publications, etc. As mentioned above, an on-line social networking system may be designed to allow registered members to establish and document networks of people they know and trust professionally. Any two members of a social network may indicate their mutual willingness to be “connected” in the context of the social network, in that they can view each other's profiles, profile recommendations and endorsements for each other and otherwise be in touch via the social network. Members that are connected in this way to a particular member may be referred to as that particular member's connections or as that particular member's network.
The profile information of a social network member may include various information such as, e.g., the name of a member, current and previous geographic location of a member, current and previous employment information of a member, information related to education of a member, information about professional accomplishments of a member, publications, patents, etc. The profile information of a social network member may also include information about the member's professional skills. A particular type of information that may be present in a profile, such as, e.g., company, industry, job position, etc., is referred to as a profile attribute. A profile attribute for a particular member profile may have one or more values. For example, a profile attribute may represent a company and be termed the company attribute. The company attribute in a particular profile may have values representing respective identifications of companies, at which the associated member has been employed. Other examples of profile attributes are the industry attribute and the region attribute. Respective values of the industry attribute and the region attribute in a member profile may indicate that the associated member is employed in the banking industry in San Francisco Bay Area.
Members may access other members' profiles by entering the name of a member represented by a member profile in the on-line social network system into the search box and examining the returned search results or by entering into a search box a phrase intended to represent a member's skill, geographic location, place of employment, etc. For example, a user may designate a search as a people search (e.g., by accessing a web page designated for people search or including a predetermined phrase, such as “working in” or “employed as,” into the search box) and enter one or more keywords, e.g., “software engineer” and “San Francisco.” A web page containing search results produced by the on-line social network system in response to a people search is referred to as a People SERP (people search results page, hereafter denoted PSERP).
Another way to access members' profiles is via a people directory web page provided by the on-line social network system. A people directory web page (also referred to as a people directory) may be organized, e.g., alphabetically by keywords. The keywords may represent members' professional skills, members' geographic locations, members' places of employment (e.g., companies), etc. An example representation of a user interface 400 for navigating a people directory is shown in
While it is possible to search for people using the web pages provided by the on-line social network system, third party search engines are often used as entry points for guests to learn about the on-line social network system. It is beneficial to provide a rich people search experience for guests (users that are not members of the on-line social network system) so that they understand the value of the on-line social network system ecosystem and become members, thereby potentially driving growth and eventual monetization. Since guests often use web search as the starting point in searching in general and for people having specific professional characteristics specifically, it may be desirable that the people search results pages (PSERPs) provided by the on-line social network system are ranked such that they appear at the top of the search results list displayed to the originator of the people-related search request.
In the on-line social network system each PSERP is associated with one or more keywords that represent members' professional skills, members' geographic locations, members' places of employment (e.g., companies), etc. For example, a PSERP that includes links to member profiles that indicate that the respective members are software engineers in San Francisco may be associated in the on-line social network system with the keywords “software engineer” and “San Francisco.” A keyword that may represent a PSERP in this manner is referred to as a people-related keyword. Given hundreds of thousands of potential people-related keywords, it is beneficial to understand respective values of PSERPs relative to one another based on the respective priority values of the associated people-related keywords.
In one example embodiment, the on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to calculate respective priority scores for people-related keywords and use these priority scores for enhancing the users' on-line people search experience. A set of keywords to be scored may be selected automatically, e.g., based on the information stored in the member profiles, and stored in a database as a bank of keywords. The SEO system may be configured to generate priority scores for different keywords, using a probabilistic model that takes into account a value expressing how likely the keyword is to be included in a search query as a search term and a value expressing how likely is a search that includes the keyword as a search term is to produce relevant results. A value expressing how likely the keyword is to be included in a search query as a search term may be referred to as a popularity score. A value expressing how likely a search that includes the keyword as a search term is to produce relevant results may be referred to as a relevance score. In some embodiments, the priority score for a keyword may be generated by multiplying the relevance score for a keyword by the popularity score for that same keyword, e.g. using Equation 1 shown below.
PrioirtyScore(w)=Pr(RELEVANT&w)=Pr(w)*Pr(RELEVANT/w), Equation (1):
where w is a keyword, Pr(w) is probability expressing the popularity score for the keyword w, and Pr(RELEVANT/w) is probability expressing the relevance score for the keyword w.
The keywords that have higher priority scores are considered to be more valuable, and, as such, can be included into the people directory and/or can be used to determine which PSERP pages to be included into a sitemap submitted to one or more third party search engines (such as, e.g., Google® or Bing®). The priority scores generated using the methodologies described herein may be also used beneficially in selecting terms for inclusion into a people directory, as explained above.
Priority scores generated for keywords may be used to determine relative importance of terms within a query.
As mentioned above, a value expressing how likely the keyword is to be included in a search query as a search term is referred to as a popularity score. Popularity of a keyword provides an indication of how frequently the keyword is used in people-related searches. In order to generate popularity score Pr(w) for a particular keyword w (a subject keyword), the SEO system monitors people-related searches that include the subject keyword. In one embodiment, the SEO system monitors, for a period of time, all people-related searches performed by one or more certain target third party search engines (e.g., Google®, Yahoo!®), as well as people-related searches performed within the on-line social network system. The results of monitoring of each of these sources with respect to a particular keyword w are used to generate respective intermittent popularity values Pj(w), where j is the j-th data source from k data sources. For example, Pj(w) for Google® data source may be determined based on the percentage of people-related searches that include the keyword w. The intermittent popularity value Pj(w) that corresponds to a third-party search volume may be designated as G(w). The intermittent popularity value Pj(w) that corresponds to people search volume obtained by monitoring search requests in the on-line social network system may be designated as I(w).
When the on-line social network system is used as a data source for determining Pj(w), the SEO system considers every search request to be a people-related search. When a third party search engine is used as a data source for determining Pj(w), the SEO system may first determine whether the intent of the search is related to people search and take into account only those searches that have been identified as people-related, while ignoring those searches that have not been identified as people-related. Identifying a people search directed to a third party search engine as being people-related could be accomplished by detecting the presence, in a search request, of additional terms that have been identified as intent indicators, such as, e.g., the word “people” or “member,” as well as phrases such as “work as/at/in” or “who are.”
Because the popularity values generated based on data obtained from different may be in different scales, the SEO system may be configured to first normalize the intermittent popularity values Pj(w) for a given keyword w, and then aggregate the normalized popularity values to arrive at the popularity score Pr(w). This approach may be expressed by Equation (2) shown below.
Pr(w)=popularityAggregateFunction(normFunction1(P1(w)),normFunction2(P2(w)), . . . ,normFunctionk(Pk(w))) Equation (2)
In one embodiment, a different normalization function is used for each of the intermittent popularity value (normFunction1 for P1(w), normFunction2 for P2(w), etc.). The aggregation function, denoted as popularityAggregateFunction in Equation (2) above, can be chosen to be one of max, median, mean, mean of the set of normalized popularity values selected from a certain percentile range, e.g., from 20th to 80th percentile. In some embodiments, the aggregation function can be the output of a machine learning model (such as logistic regression) that is learned over ground truth data. The normalization function normFunctionj(Pj(w)) is to map each of the intermittent popularity value Pj(w) to the same interval.
For example, the normalization function scale(Pj(w)) may map each of the intermittent popularity value Pj(w) to the interval [0, 1] and utilize three percentile values−the lower threshold (α-percentile value), the median (50-percentile value), and the upper threshold (β-percentile value). The normalization function performs piecewise linear mapping from the intermittent popularity values to [0, 1]. An intermittent popularity value is mapped to 0 if it is less than the lower threshold. Linear scaling to [0, 0.5] is performed for intermittent popularity values that are greater than or equal to the lower threshold and less than or equal to the median. Linear scaling to [0.5, 1] is performed for intermittent popularity values that are greater than or equal to the median and less than or equal to the upper threshold. An intermittent popularity value is mapped to 1 if it is greater than the upper threshold. The max value from the set of normalized popularity values may then be used as the aggregation function: max(scale(P1(w)), scale(P2(w)), . . . , scale(Pk(w))). The scaling applied to each of the intermittent popularity value may be different since the percentile values could be different for each intermittent popularity type.
In some embodiments, the SEO system may be configured to use the popularity score of a keyword as the priority score for that keyword. Yet in other embodiments, as stated above, respective popularity scores generated for the keywords may be used to derive the respective corresponding priority scores, e.g., by multiplying the value expressing the popularity score by the value expressing the relevance score, as expressed by Equation (1) above.
As mentioned above, a value expressing how likely a search that includes the keyword as a search term is to produce relevant results may be referred to as a relevance score. In one embodiment, the SEO system may be configured to determine the relevance score Pr(RELEVANT/w) for a keyword w using one or multiple indicators of relevance.
One example of an indicator of relevance of a keyword is the number of people search results returned in response to a query that includes a keyword as a search term and that originates from the on-line social network system. Another indicator of relevance of a keyword may be related to respective quality scores assigned to the returned results by a third party search engine. For example, a third party search engine returns search results in response to a query that includes a keyword as a search term. The returned results each have a quality score assigned to it by the search engine. The sum of quality scores of those returned search results that originate from the on-line social network system may be used by the SEO system as one of the indicators of relevance of that keyword. Yet another indicator of relevance of a keyword may be obtained based on monitoring user engagement signals with respect to the search results returned in response to a query that includes a keyword as a search term and that originate from the on-line social network system. For example, with respect to the search results returned in response to a query that includes a keyword as a search term and that originate from the on-line social network system, the SEO system may monitor and record signals such as click through rate (CTR) and bounce rate. These signals can be aggregated over individual people results (PSERPs) to obtain a combined user engagement score for that PSERP. This user engagement score may be then utilized in deriving the relevance score for the keyword.
Another indicator of relevance of a keyword may be obtained by examining member profiles in the on-line social network system. For example, the SEO system may determine how frequently a keyword is used in a member profile to designate a skill or a job title. The intuition is that if there is a large number of professionals with a given skill/title, people are more likely to use such keywords as search terms, and are more likely to find relevant people results for such keywords.
Different indicators of relevance with respect to a particular keyword w are used to generate respective intermittent relevance values Pj(RELEVANT/w), where j is the j-th data source from k data sources. Because the relevance values generated based on data obtained from different may be in different scales, the SEO system may be configured to first normalize the intermittent relevance values Pj(RELEVANT/w) for a given keyword w, and then aggregate the normalized relevance values to arrive at the relevance score Pr(RELEVANT/w). This approach may be expressed by Equation (3) shown below.
Pr(RELEVANT/w)=relevanceAggregateFunction(normFunction1(P1(RELEVANT/w)), normFunction2(P2(RELEVANT/w)), . . . , normFunction1(P1(RELEVANT/w))) Equation (3)
A different normalization function may be used for each of the intermittent relevance value (normFunction1 for P1(RELEVANT/w), normFunction2 for P2(RELEVANT/w), etc.). Furthermore, in some embodiments, these normalization functions are also different from those used for relevance score computation. The aggregation function, denoted as relevanceAggregateFunction in Equation (3) above, can be chosen to be one of max, median, mean, mean of the set of normalized relevance values selected from a certain percentile range, e.g., from 20th to 80th percentile. In some embodiments, the aggregation function can be the output of a machine learning model (such as logistic regression) that is learned over ground truth data. In some embodiments, the normalization function normFunctioni(Pj(RELEVANT/w)) is to map each of the intermittent relevance value Pj(RELEVANT/w) to the same interval and utilize two threshold values−the lower threshold (ε1), and the upper threshold (ε2).
For example, with respect to the intermittent Pj(RELEVANT/w) is the number of search results returned in response to a query that includes a keyword as a search term that originate from the on-line social network system, the normalization function scale(Pj(RELEVANT/w)) maps the people result count to [0, 1] using a step function: 0 if the people result count is fewer than the lower threshold, 1 if the people result count is greater than the upper threshold. If the people result count is greater than the lower threshold and less than the upper threshold, its normalized value is calculated as shown in Equation (4) below.
scale(Pj(RELEVANT/w))=(Pj(RELEVANT/w))−ε1)/(ε2−ε1) Equation (4)
In another example, where the intermittent Pj(RELEVANT/w) is the sum of quality scores of those returned search results that originate from the on-line social network system, a combined quality score for the page and the keyword w is derived using an aggregation function such as max, median, mean, mean of the values between certain percentiles (e.g., from 20th to 80th percentile), etc. The aggregation function can also take into account position discounting, that is, provide greater weight to jobs search results at top positions.
Another example of the intermittent Pj(RELEVANT/w) is the user feedback/engagement signals, such as, e.g., overall click through rate, bounce rate, etc. These signals can also be aggregated over individual people results to obtain combined score for the associated PSERP. Yet another example of the intermittent Pj(RELEVANT/w) is the value derived from examining the member profiles and determining the frequency of appearance of the keyword w in those profiles.
As explained above, in some embodiments, respective relevance scores generated for people-related keywords may be used to derive respective priority scores, e.g., by multiplying the value expressing the popularity score for a keyword by the value expressing the relevance score for that same keyword, as expressed by Equation (1) above. An example keyword prioritization system may be implemented in the context of a network environment 100 illustrated in
As shown in
The client systems 110 and 120 may be capable of accessing the server system 140 via a communications network 130, utilizing, e.g., a browser application 112 executing on the client system 110, or a mobile application executing on the client system 120. The communications network 130 may be a public network (e.g., the Internet, a mobile communication network, or any other network capable of communicating digital data). As shown in
The PSERP generator 210 is configured to generate a PSERP, which is a web page that comprises references to one or more member profiles representing respective members in the on-line social network system, and selects one or more terms as representing the PSERP. A term representing the PSERP may represent a professional skill of a member (e.g., “project manager”), a geographic location of a member, a place of employment of the member (e.g., “ABC company”), etc. The PSERP generator 210 selects a term to represent the PSERP by examining member profiles referenced in the PSERP.
The search request monitor 220 is configured to monitor people-related search requests. For example, the search request monitor 220 may monitor search requests that include a particular keyword or term, a subject keyword, that represents a people search results page (PSERP) provided by the on-line social network system 142 of
The popularity score generator 230 is configured to generate respective popularity scores for keywords, using the methodologies described above. As explained above, the popularity score of a keyword indicates how likely the subject keyword is to be included in a people-related search query as a search term. For example, the popularity score generator 230 may monitor people-related search requests that include a subject keyword, and determine a popularity score for the subject keyword based on frequency of appearance of the subject keyword in the monitored search requests. In one embodiment, the popularity score generator 230 monitors search requests directed to a search engine provided by an on-line social network system and also search requests directed to a third party search engine and, based on the results of the monitoring generate respective intermittent popularity values for the subject keyword. The popularity score generator 230 may then apply a normalization function to the intermittent popularity values and aggregate the resulting scaled values to generate the popularity score for the subject keyword. The normalization function may be, e.g., max, median, or mean of the intermittent popularity values.
As explained above, when the on-line social network system is used as a data source while monitoring search requests, the popularity score generator 230 considers every search request to be a people-related search. When a third party search engine is used as a data source, the popularity score generator 230 may first determine whether the intent of the search is related to people search and take into account only those searches that have been identified as people-related, while ignoring those searches that have not been identified as people-related. Identifying a people search directed to a third party search engine as being people-related could be accomplished by detecting the presence, in a search request, of additional terms that have been identified as intent indicators, such as, e.g., the word “people” or “member.”
The set of search results generated by a third party search engine (a third party search engine provided by an entity that is distinct from an entity that provides the on-line social network system) may include one or more entries that originate from the on-line social network system. The entries that originate from the on-line social network system are referred to as relevant entries for the purposes of this description. Relevant entries may be, e.g., references to member profiles maintained by the on-line social network system.
The search results produced by people-related searches monitored by the search request monitor 220 are used by the relevance score generator 240 to generate the relevance score for the subject keyword using information associated with the relevant entries in the set of search results. As stated above, the relevance score expresses how likely a search that includes the subject keyword as a search term is to produce relevant results. A relevant search result is a result that originates from the on-line social network system 142. The relevance score generator 240 generates the relevance score using one or more methodologies described above.
For example, where the third party search engine assigns a respective quality score to each entry in the set of search results, the relevance score generator 240 generates the relevance score for the subject keyword using a combination (e.g., the sum) of respective quality scores assigned to the relevant entries in the set of search results. In some embodiments, the relevance score generator 240 may also use a count of the relevant entries in the set of search results. Other signals that can be used by the relevance score generator 240 to generate a relevance score may be data that reflects user engagement with respect to relevant entries in the set of search results. For example, the relevance score generator 240 may be configured to monitor user engagement signals with respect to any of the relevant entries (e.g., clicks the duration of viewing, etc.) and adjust the relevance score based on the results of the monitoring. Another signal that can be used by the relevance score generator 240 to generate a relevance score is based on the frequency of appearance of the subject keyword in certain fields (e.g., skills or title fields) of member profiles maintained by the on-line social network
The priority score generator 250 may be configured to generate a priority score for the subject keyword utilizing its popularity score, its relevance score, or both. For example, a priority score for the subject keyword may be generated by calculating a product of the popularity score and the relevance score.
Also shown in
As shown in
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 example computer system 500 includes a processor 502 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 504 and a static memory 506, which communicate with each other via a bus 505. The computer system 500 may further include a video display unit 510 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 500 also includes an alpha-numeric input device 512 (e.g., a keyboard), a user interface (UI) navigation device 514 (e.g., a cursor control device), a disk drive unit 516, a signal generation device 518 (e.g., a speaker) and a network interface device 520.
The disk drive unit 516 includes a machine-readable medium 522 on which is stored one or more sets of instructions and data structures (e.g., software 524) embodying or utilized by any one or more of the methodologies or functions described herein. The software 524 may also reside, completely or at least partially, within the main memory 504 and/or within the processor 502 during execution thereof by the computer system 500, with the main memory 504 and the processor 502 also constituting machine-readable media.
The software 524 may further be transmitted or received over a network 526 via the network interface device 520 utilizing any one of a number of well-known transfer protocols (e.g., Hyper Text Transfer Protocol (HTTP)).
While the machine-readable medium 522 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, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing and encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of embodiments of the present invention, or that is capable of storing and encoding data structures utilized by or associated with such a set of instructions. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media. Such media may also include, without limitation, hard disks, floppy disks, flash memory cards, digital video disks, random access memory (RAMs), read only memory (ROMs), and the like.
The embodiments described herein may be implemented in an operating environment comprising software installed on a computer, in hardware, or in a combination of software and hardware. Such embodiments of the inventive subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is, in fact, disclosed.
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 (1) on a non-transitory machine-readable medium or (2) in a transmission signal) or hardware-implemented modules. A hardware-implemented module is tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more processors may be configured by software (e.g., an application or application portion) as a hardware-implemented module that operates to perform certain operations as described herein.
In various embodiments, a hardware-implemented module may be implemented mechanically or electronically. For example, a hardware-implemented module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware-implemented module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware-implemented 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-implemented module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired) or temporarily or transitorily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein. Considering embodiments in which hardware-implemented modules are temporarily configured (e.g., programmed), each of the hardware-implemented modules need not be configured or instantiated at any one instance in time. For example, where the hardware-implemented modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware-implemented modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware-implemented module at one instance of time and to constitute a different hardware-implemented module at a different instance of time.
Hardware-implemented modules can provide information to, and receive information from, other hardware-implemented modules. Accordingly, the described hardware-implemented modules may be regarded as being communicatively coupled. Where multiple of such hardware-implemented modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware-implemented modules. In embodiments in which multiple hardware-implemented modules are configured or instantiated at different times, communications between such hardware-implemented modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware-implemented modules have access. For example, one hardware-implemented 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-implemented module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware-implemented 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. 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 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).)
Thus, a method and system to prioritize keywords in an on-line social network system has been described. Although embodiments have 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 scope of the inventive subject matter. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.
