Systems and methods for providing service directory predictive search recommendations

Systems, methods, and non-transitory computer-readable media can train a machine learning model to determine predictive search recommendation based on search prediction information. Search prediction information associated with a user is provided to the machine learning model. A predictive search recommendation is presented to the user based on the machine learning model and the search prediction information. A search is performed based on the predictive search recommendation for one or more search results associated with entity pages on a social networking system.

FIELD OF THE INVENTION

The present technology relates to the field of social networking systems. More particularly, the present technology relates to systems and methods for providing predictive search recommendations in a service directory.

BACKGROUND

Today, people often utilize computing devices (or systems) for a wide variety of purposes. Users can use their computing devices, for example, to interact with one another, create content, share content, and view content. In some cases, a user can utilize his or her computing device to access a social networking system (or service). The user can provide, post, share, and access various content items, such as status updates, images, videos, articles, and links, via the social networking system.

A social networking system can include one or more pages associated with entities on the social networking system. For example, the social networking system can include one or more pages associated with merchants and/or service providers looking for exposure with consumers. In certain contexts, individual users can be distinguished from entity users on the social networking system. For example, an entity user or an entity account can be associated with a particular entity, and can be controlled by one or more administrators of the entity account, whereas an individual user can be associated with a particular individual. Both individual users and entity users can interact with one another on the social networking system by, for example, sending messages to one another, posting content to other users' accounts, sharing content publicly for publication to other users' newsfeeds, and the like. Interactions between users on a social networking system can be mutually beneficial. Participation in the social networking system by users can be encouraged and promoted by improving tools and features to facilitate interaction on the social networking system.

SUMMARY

Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to train a machine learning model to determine predictive search recommendations based on search prediction information. Search prediction information associated with a user is provided to the machine learning model. A predictive search recommendation is presented to the user based on the machine learning model and the search prediction information. A search is performed based on the predictive search recommendation for one or more search results associated with entity pages on a social networking system.

In an embodiment, the search prediction information includes at least one of: user identification information, user social network information, user social network engagement information, or past search history information.

In an embodiment, the one or more search results are presented to the user, each search result being associated with an entity page on the social networking system.

In an embodiment, it is determined that the user is logged into the social networking system.

In an embodiment, search prediction information is provided to the machine learning model and the predictive search recommendation is presented to the user in response to the determination that the user is logged into the social networking system.

In an embodiment, the one or more search results are ranked based on ranking criteria. The search results are presented to the user based on the ranking, and the ranking criteria are based on a popularity of each entity page associated with each search result.

In an embodiment, the ranking criteria are further based on interactions between connections of the user on the social networking system with one or more entity pages associated with one or more search results.

In an embodiment, the predictive search recommendation is associated with a service category defined by the social networking system.

In an embodiment, one or more related service categories associated with the service category are presented to the user.

In an embodiment, a selection of a first related service category of the one or more related service categories is received. A second search is performed based on the first related service category.

DETAILED DESCRIPTION

Service Directory Predictive Search Recommendations

People often utilize computing devices (or systems) for a wide variety of purposes. Users can use their computing devices to, for example, interact with one another, access content, share content, and create content. In some cases, content items can include postings from users of a social networking system. The postings may include text and media content items, such as images, videos, and audio. The postings may be published to the social network for consumption by others.

A social networking system can include one or more pages associated with entities on the social networking system. For example, the social networking system can include one or more pages associated with merchants and/or service providers looking for exposure with consumers. In certain contexts, individual users can be distinguished from entity users on the social networking system. For example, an entity user or an entity account can be associated with a particular entity, and can be controlled by one or more administrators of the entity account, whereas an individual user can be associated with a particular individual. Both individual users and entity users can interact with one another on the social networking system by, for example, sending messages to one another, posting content to other users' accounts, sharing content publicly for publication to other users' newsfeeds, and the like. Interactions between users on a social networking system can be mutually beneficial. Participation in the social networking system by users can be encouraged and promoted by improving tools and features to facilitate interaction on the social networking system.

Under conventional approaches, entity pages on a social networking system can help merchants and/or service providers provide information about their businesses or services to other users on the social networking system. Entity pages can also allow entities to interact with individual users and potential customers on the social networking system. However, without knowing a particular entity's name, or searching for a specific entity, it can be difficult for individual users to discover new merchants and/or service providers under conventional approaches. Furthermore, it may be even more difficult, or impossible, for consumers that are not users (or members) of the social networking system to discover the entities' pages on the social networking system. Accordingly, such conventional approaches can engender these and other problems arising in computer technology.

An improved approach rooted in computer technology overcomes the foregoing and other disadvantages associated with conventional approaches specifically arising in the realm of computer technology. In general, a service directory can be provided on a social networking system that allows users to search for entity pages on the social networking system. In various embodiments, users may utilize the service directory regardless of whether or not the user is a user or member of the social networking system. The service directory can provide a search function for users to search for entities (e.g., service providers) on the social networking system. In certain embodiments, a computing system can be configured to train a model for predicting a service category that may be of interest to a particular user. The service category predicted by the model can be provided to the user as a predictive search recommendation. For example, when the particular user enters a landing page for the service directory, a search bar may be pre-filled with a search recommendation for that particular user based on the model. The model can be trained using, for example, demographic information, user social network engagement information, previous search information, and the like. Once the user runs a search, the user can be presented with a list of search results that include one or more entity pages on the social networking system that match the search.

While the examples used herein may describe a service directory in which a user can search for service categories and/or service providers on a social networking system, the approaches described herein can be adapted to searches for any entity having an entity page on the social networking system. As such, the approaches described herein need not be limited to searches for service providers on a social networking system.

FIG. 1illustrates an example system100including an example service directory module102, according to an embodiment of the present disclosure. The service directory module102can be configured to provide a service directory for assisting a user in finding entity pages on a social networking system. For example, the service directory can include a search function for a user to search for a particular entity page or to search for a service category. A model can be trained to recommend a service category for a user to search. For example, the user may be presented with a search recommendation for a service category that the model has predicted may be relevant to the user. The search recommendation can be tailored to a particular user. The user can choose to run a search based on the search recommendation, or the user can enter their own search terms. When the user runs a search, the user can be presented with search results including a list of entity pages that are relevant to the search. For example, if the user runs a search for a particular service category, the user can be presented with search results including entity pages that are relevant to the service category. For instance, if a user searches for “plumbing,” the user can be presented with a list of plumbers' entity pages on the social networking system. In certain embodiments, the search may also include a location field to specify a particular location. In this way, search results can be limited to entities that are located near the specified location and/or service the specified location.

As shown in the example ofFIG. 1, the service directory module102can include a landing page module104and a search results module106. In some instances, the example system100can include at least one data store110. The components (e.g., modules, elements, etc.) shown in this figure and all figures herein are exemplary only, and other implementations may include additional, fewer, integrated, or different components. Some components may not be shown so as not to obscure relevant details. In various embodiments, one or more of the functionalities described in connection with the service directory module102can be implemented in any suitable combinations.

In some embodiments, the service directory module102can be implemented, in part or in whole, as software, hardware, or any combination thereof. In general, a module, as discussed herein, can be associated with software, hardware, or any combination thereof. In some implementations, one or more functions, tasks, and/or operations of modules can be carried out or performed by software routines, software processes, hardware, and/or any combination thereof. In some cases, the service directory module102can be implemented, in part or in whole, as software running on one or more computing devices or systems, such as on a user or client computing device. For example, the service directory module102, or at least a portion thereof, can be implemented as or within an application (e.g., app), a program, or an applet, etc., running on a user computing device or a client computing system, such as the user device610ofFIG. 6. In another example, the service directory module102, or at least a portion thereof, can be implemented using one or more computing devices or systems that include one or more servers, such as network servers or cloud servers. In some instances, the service directory module102can, in part or in whole, be implemented within or configured to operate in conjunction with a social networking system (or service), such as the social networking system630ofFIG. 6. It should be understood that there can be many variations or other possibilities.

The service directory module102can be configured to communicate and/or operate with the at least one data store110, as shown in the example system100. The data store110can be configured to store and maintain various types of data. In some implementations, the data store110can store information associated with the social networking system (e.g., the social networking system630ofFIG. 6). The information associated with the social networking system can include data about users, user identifiers, social connections, social interactions, profile information, demographic information, locations, geo-fenced areas, maps, places, events, pages, groups, posts, communications, content, feeds, account settings, privacy settings, a social graph, and various other types of data. In some embodiments, the data store110can store information that is utilized by the service directory module102. For example, the data store110can store entity page information, service category groupings, historical service directory search information, and the like. It is contemplated that there can be many variations or other possibilities.

The landing page module104can be configured to provide a service directory landing page. The landing page can be configured to allow a user to search for and/or select a service category from various service category options so that the user can find an entity associated with a particular service category. In certain embodiments, the landing page module104can provide a search function for a user to search for entity pages on a social networking system. For example, the user can search for a particular entity by name, and a list of one or more entity pages matching the search can be presented to the user. If the user is not looking for a particular entity, but is looking for any entities associated with a particular service category, the user can search for a service category. For example, if the user needs a plumber, but does not have a particular plumber in mind or is not aware of one, the user can enter the search term “plumber” or “plumbing.” In this case, the user can be presented with a list of one or more entities that match the service category. In certain embodiments, the search may also include a location field to specify a particular location. In this way, search results can be limited to entities that are located near the specified location and/or service the specified location. In various embodiments, the landing page module104can be configured to provide a predictive search recommendation for a user. A model can be trained to predict a service category that may be relevant to a particular user based on training data. The model can then be provided with search prediction information that allows the model to make a prediction of a service category that may be of interest to the user, and the predictive service category can be presented to the user as a search recommendation.

In addition to providing a search function, the landing page module104can also be configured to provide one or more service category options for selection by a user. For example, the user can be provided with a list of service categories. Service categories can be presented as, for example, selectable icons or selectable text. When a user selects a particular service category, a search can be run based on the selected service category so as to provide the user with a list of entity pages associated with the selected service category.

In certain embodiments, the landing page can be available publicly, such that users can access the landing page regardless of whether or not they are signed into or members of a social networking system. In some embodiments, the landing page experience is identical for logged-in or logged-out users. Alternatively, in certain embodiments, user experience may be different for logged-in or logged-out users. For example, if a user is logged-in to a social networking system, the landing page may be tailored to the particular user. For example, various fields may be pre-filled (e.g., the location field in the search function), or other aspects may be customized to the particular user (e.g., the listing of service categories may be tailored to the particular user). In certain embodiments, a predictive search recommendation may be provided if the user is logged into a social networking system. If a user is not logged-in to a social networking system, certain user-specific information may not be available to support certain types of individual tailoring to the particular user. As such, in various embodiments, a predictive search recommendation or a location prediction may be provided to the user in response to a determination that the user is logged into the social networking system. However, in various embodiments, even if a user is not logged into a social networking system, other available user information may be used to provide user-specific tailoring, such as a predictive search recommendation or a location prediction. More details regarding the landing page module104will be provided with reference toFIG. 2.

The search results module106can be configured to run a search and provide search results to a user. As mentioned, a search may be run based on various search criteria, such as user entered search terms (e.g., a service category, location information, an entity name, etc.), a search recommendation provided to the user, or selection of a service category option by a user.

Search results can include a search results list having entries corresponding to entity pages on a social networking system that match the search criteria. Each search result can lead users to an entity page associated with the search result. For example, if a user clicks on a search result, the user may be directed to the entity page associated with the search result. Each search result can include information about each entity, such as the entity's name, a description of the entity, address, phone number, service categories associated with the entity, etc. Each entry can also include social context information for each entity. For example, social context information can include a rating and/or reviews for the entity (e.g., reviews posted to the social networking system by other users of the social networking system, or reviews posted to other sites), the number of users on the social networking system that have liked or followed the entity page, the number of users that have visited the entity page, and the like. Each entry in the search results list can also include one or more call to action buttons or icons. A call to action button can be selected by a user to take a particular action with respect to the entity and/or the entity page. For example, the user can select a “Like” call to action button to like a particular entity's page, or can select a “Message” call to action button to send a message to an entity's page. Certain call to action buttons can be presented by default for each entity page (e.g., a “Like” or “Message” button), while other call to action buttons can be specifically selected by an entity page. For example, certain entity pages may offer online appointment scheduling services, and can offer a “Book Now” call to action button that directs the user to the online appointment scheduling service.

The search results module106can be configured to rank the search results based on various ranking criteria. Ranking criteria can include, for example, the relevance of a particular entity page to the search terms (e.g., a service category and/or a location), social context information (e.g., the number of users who have liked, followed, and/or visited an entity page), the quality of an entity page's reviews, etc. In certain embodiments, ranking criteria may include user-specific characteristics, such that rankings may be tailored and/or unique to individual users. For example, if a particular user's connections on a social networking system have positively reviewed a particular entity, or if the user's connections have liked or followed the entity's page, that entity may be ranked higher than other entities that have not been reviewed, liked, and/or followed by the user's connections on the social networking system. It should be appreciated that such user-specific information may only be available if the user is logged into a social networking system. As such, ranking criteria may differ based on whether or not the user is logged into the social networking system. If a user is not logged into a social networking system, a first set of ranking criteria may be used, and if the user is logged into the social networking system, a second set of ranking criteria may be used, and the second set of ranking criteria may include user social network information. For example, if a user runs a search and the user is not logged into a social networking system, search results may be ranked based on entity-specific information, such as each entity page's relevance to the search criteria, the popularity of the entity page associated with each search result (e.g., the number of followers of the entity page), and/or the quality of reviews and/or ratings received by each entity page. If the user is logged into a social networking system, search results can be ranked using the same entity-specific information, or search results can also include user-specific information, such as whether or not the user's connections on the social networking system have followed or liked an entity page, or positively or negatively reviewed an entity page.

The search results module106can be configured to provide a map identifying locations associated with the one or more entity pages provided in the search results. For example, the map can include one or more pins, each pin associated with a particular entity page listed in the search results. The map may be interactive. For example, if the user hovers over a particular search result, a pin associated with the particular search result may be highlighted on the map. In another example, if the user hovers over a pin on the map, or selects the pin on the map, entity information can be presented for the search result or entity page associated with the pin.

The search results module106can also be configured to provide one or more related search criteria. For example, the search results module106can provide a list of service categories that are related to or similar to the service category searched by the user. If the user selects one of the related service categories, another search can be run based on the related service category selected by the user. In certain embodiments, related service categories can be determined based on groupings of categories. For example, the service directory can include tiered levels of categories, each level defining categories with greater specificity. For example, a first or highest tier of categories can be somewhat broad, e.g., automotive, home improvement, spa/beauty/personal care, etc. Each category in this highest level of categories can then include one or more sub-categories. For example, the “automotive” category can include the categories “repair,” “car dealership,” “car wash,” etc.; the “home improvement” category can include the categories “cleaning,” “electrician,” “gardening,” “mover,” “painter,” “plumbing,” etc. Each of these narrower, lower level categories can further include their own lower level categories, e.g., the automotive repair category can include “glass repair,” “engine repair,” “body shop,” etc. Based on these tiered groupings of categories, relevant categories can be recommended to the user. For example, if the user has searched for “plumber,” it can be determined that “plumber” is a sub-category of the higher level category “home improvement,” and all other categories that fall under “home improvement” can be recommended to the user. In certain embodiments, other related categories that are not in the same grouping, but still related to the current service category can be recommended. For example, if a user searches for restaurants, the user may be provided with recommendations for after-dinner activities, even if the after-dinner activities do not fall into the same categorical grouping as the category “restaurants.”

In certain embodiments, when a user runs a search for a particular service category, the search results can display category “breadcrumbs” indicative of any higher level categories to which the service category belongs. For example, if a user searches for “plumbing,” the service category “plumbing” may belong to the higher level category “home improvement,” which itself may belong to the higher level category “services.” In this example, the breadcrumbs might be displayed as follows: “Services>Home Improvement>Plumbing.”

The search results module106can also be configured to provide a list of alternative locations that are nearby (e.g., within a threshold distance or threshold number of geolocations) the location specified in the current search criteria. For example, if a user searches for plumbers in Palo Alto, Calif., the user may be provided with alternative locations nearby Palo Alto, Calif., such as Mountain View, Calif.; San Jose, Calif.; Fremont, Calif., etc. By selecting one of these alternative locations, the user can re-run the search based on the selected alternative location, e.g., search for plumbers in Mountain View, Calif. Certain service categories may be more amenable to a broader geographic search area, whereas other service categories may require a more localized search. For example, if a user is searching for a coffee shop in a particular neighborhood, it may not be particularly useful to recommend a coffee shop in a neighboring city; whereas if a user is searching for a plumber in a particular city, the user may still be interested in plumbers in nearby cities so long as those plumbers also service the user's city. As such, the alternative locations recommended to a user may vary based on the service category specified in the current search, and a location-sensitivity associated with the service category.

In certain embodiments, if a user selects and/or interacts with one of the entity pages shown in the search results, the user may be presented with other entity pages similar to the entity page selected and/or interacted with by the user. For example, if the user interacts with an entity page for a particular car dealership associated with a particular car brand, the user may be presented with entity pages for other car dealerships associated with the same car brand. In certain embodiments, the similar entity page recommendations may be restricted to other entity pages in the same category as the entity page originally selected and/or interacted with.

FIG. 2illustrates an example landing page module202configured to provide a user with a landing page for navigating and/or searching a service directory, according to an embodiment of the present disclosure. In some embodiments, the landing page module104ofFIG. 1can be implemented as the landing page module202. As shown in the example ofFIG. 2, the landing page module202can include a search recommendation module204, a location prediction module206, and a category selection module208.

The search recommendation module204can be configured to predict search terms (e.g., a service category) that may be relevant for a particular user. A model can be trained to predict search terms that may be relevant for a particular user. More details regarding the search recommendation module204will be provided with reference toFIG. 3.

The location prediction module206can be configured to predict a particular user's location. For example, when a user is presented with a landing page, the landing page can include a search function to search for entity pages. The search function can allow a user to specify a particular location by filing in a location criteria to limit the search to entity pages that are associated with and/or relevant to the location specified by the user. The location prediction module206can be configured to predict the user's location, and automatically fill in the location criteria with the predicted location. The user's location can be predicted based on any available information. This can include, for example, a city of residence or hometown specified by the user on a social networking system, a location associated with the user's current IP address, a recent check-in posted to the social networking system by the user, a recent status update posted to the social networking system indicative of a particular location, previous searches performed by the user containing location information, etc. In certain embodiments, the search function can include an image associated with the search function. The image can be selected based on the location predicted by the location prediction module206. For example, if the location prediction module206predicts that a user is located in Seattle, Wash., the image can be an image of the Seattle skyline.

The category selection module208can be configured to present a user with one or more service category options for selection by the user. As discussed above, in addition to a search function, the landing page can include one or more service category options that a user can select. Selection of a particular service category option can result in a search being run for the selected service category. A set of service categories can be pre-defined by, for example, a social networking system. In certain embodiments, a subset of service categories can be featured and/or selected for presentation. For example, a set of featured service categories may be presented in a more easily visible way, such as larger icons that have images associated with each featured service category. A subset of featured service categories can be selected based on various featured category selection criteria. For example, service categories can be ranked based on category ranking criteria. The category ranking criteria can include the popularity of the service category (e.g., the frequency with which the service category is selected and/or searched), to name one example. In certain embodiments, ranking of service categories can vary based on a predicted location determined by the location prediction module206. For example, service categories can be ranked based on popularity in the predicted location. If the location prediction module206predicts that a user is located in Seattle, Wash., the eight most popular service categories for Seattle can be displayed as featured service categories. There may be various groupings of service category options displayed. For example, a subset of ten featured category options can be selected and displayed with large image icons in a first portion of a user interface, and another subset of fifty categories can be selected and displayed textually in another portion of the user interface to give the user additional service category options to select. Alternatively, if it is feasible to display all service category options to a user, then the user may be presented with all service category options defined by the social networking system. For example, featured category options can be presented in a first portion of a user interface, and then a listing of all service category options can be listed in another portion of the user interface.

FIG. 3illustrates an example search recommendation module302configured to provide a search recommendation to a user, according to an embodiment of the present disclosure. In some embodiments, the search recommendation module204ofFIG. 2can be implemented as the search recommendation module302. As shown in the example ofFIG. 3, the search recommendation module302can include a model training module304and a model application module306.

The model training module304can be configured to train a machine learning model based on training data. The training data can include historical service directory search information. For example, the training data can include a set of service directory search events, in which users previously ran one or more searches in the service directory. Each search event can include the search terms that were entered by a user, along with additional search prediction information that may be useful in predicting a useful search for a user and/or providing a search recommendation to a user. Search prediction information can include, for example, user identification information (e.g., the user's age, gender, occupation); user social network information (e.g., age, gender, and occupational information of the user's connections on a social networking system); user social networking system engagement information (e.g., entity pages visited by the user over a particular period of time, content posted to the social networking system by the user over a particular period of time); and/or search history information (e.g., previous searches run by the user or the user's connections in the service directory or on other search platforms).

Search prediction information, some examples of which were discussed above, can be provided to the machine learning model so that the machine learning model can be trained to determine which search prediction information is most indicative of and/or associated with particular searches in the service directory. For example, it could be determined that if a user has recently visited the entity page of a plumber, the user is likely to conduct a search on the service directory to look for additional plumbers. Or if a user has recently posted a photograph or status update regarding a pest, the user is likely to search for exterminators in the service directory. Of course, these are simplified examples, and the machine learning model can utilize multiple types or categories of search prediction information in determining the likelihood that a user will search for a particular service category in the service directory.

The model application module306can be configured to utilize the machine learning model to predict a service category that may be useful for a particular user in an application or evaluation phase. The predicted service category can then be provided to a user as a search recommendation. For example, when a user opens a service directory (e.g., opens a service directory landing page), search prediction information associated with that user can be received, and provided to the machine learning model. The machine learning model, having been trained with training data, can determine a predictive search recommendation based on the current search prediction information. The predictive search recommendation can be provided to the user. For example, when the user opens the landing page of the service directory, a search box can be pre-filled with a recommended search.

FIG. 4Aillustrates an example scenario400associated with providing a service directory landing page, according to an embodiment of the present disclosure. The example scenario400includes a landing page interface402. The landing page interface402includes a search portion404, which has a search box406for a user to enter search terms, a location box408for a user to enter location information, and a search button410for a user to run a search. In some cases, the user can be a non-member of a social networking system that provides the service directory landing page, or a member that is not logged into the social networking system. In other cases, the user can be logged into the social networking system. As discussed above, in certain embodiments, the search box406can be pre-filled with a search recommendation that has been determined by a machine learning model. The location box408can also be pre-filled with location information based on a prediction or determination of a current user's location. In certain embodiments, a background portion of the search portion404can include an image associated with the location information stated in the location box408.

The landing page interface402also includes a featured categories portion412. The featured categories portion412includes a subset of categories that have been selected from a larger set of categories based on featured category selection criteria. As discussed above, featured category selection criteria can vary. For example, in the example scenario400, the featured categories portion412includes eight featured categories414a-414h. These eight featured categories may have been selected based on a ranking of categories according to popularity for the user's location. In this example, the eight most popular categories for users in Palo Alto, Calif. would be displayed. Popularity may be determined based on the number of users who have searched for each category in a predetermined period of time, e.g., the last day, the last week, the last 30 days, or the last year. If a user selects a featured category414a-414h, a search can be run for entities associated with the selected category. For example, if a user selects the “arts and marketing” category414f, a search can be run for entities associated with the category “arts and marketing” in Palo Alto, Calif.

Below the featured categories portion412is an additional categories portion416, in which additional categories are listed. The additional categories can include the featured categories414a-414h, or can exclude them. The additional categories portion416may include all service category options available for selection (e.g., all service categories defined by a social networking system), or may include a different subset of service categories. For example, if the featured categories portion412represents the top eight service categories based on ranking criteria, the additional categories portion416may include the top fifty service categories based on the same or different ranking criteria. The user can select any category within the additional categories portion416to run a search for entities associated with that service category.

FIG. 4Billustrates an example scenario450associated with providing search results to a user, according to an embodiment of the present disclosure. The example scenario450includes a search results interface452. The search results interface452includes a search portion454, which includes a search box456and a location box458. The search portion can display the search parameters used for the current search. For example, in the example scenario452, the user has run a search for plumbers in New York, N.Y. The search portion454also allows a user to run a new search by entering search parameters into the search box456and/or the location box458and then selecting a search button460. In some cases, the user can be a non-member of a social networking system that provides the service directory search results, or a member that is not logged into the social networking system. In other cases, the user can be logged into the social networking system.

The search results interface452displays search results for the current search in a search results portion462. The search results portion462includes category breadcrumbs472to provide a user with the higher level category groups to which the searched category belongs. In the example scenario450, the user has searched for the service category “plumbers,” which falls under the higher level category “home improvement,” which falls under the higher level category “services.”

The search results portion462also includes search results464,466. Each search result464,466is associated with a particular entity page on a social networking system. The first search result464is associated with the entity page for the entity “Green Boot Plumbing.” The search result464includes information about Green Boot Plumbing, such as a rating (4.7 stars), a phone number, an address, business hours, a review of the entity, and a description of the entity. The search result also includes two call to action buttons468,470. The first call to action button468allows a user to “like” the entity's page on the social networking system, while the second call to action button470allows a user to message the social networking system. The second search result466also includes a like button, and has a “book now” call to action button480that allows a user to schedule an appointment with the entity.

The search results portion462includes a map474. The map474includes various pins that identify the locations of the various entities listed in the search results. In the example scenario450, there are five pins on the map, each representing one entity in the search results list (some of which may not be shown in the figure).

The search results portion462also includes additional location recommendations476. The additional location recommendations476can include locations nearby the location used for the current search. This may include, for example, neighboring cities or neighborhoods, or cities in the same metropolitan area or state. The additional location recommendations476may be selectable, such that if a user selects one of the additional location recommendations, a new search can be run using the selected location. For example, if the user selects “Newark,” a new search can be run for plumbers in Newark, and new search results presented to the user.

The search results portion462also includes additional service category recommendations478. The additional service category recommendations can include additional service categories that are in some way related to the service category in the current search. For example, the additional service category recommendations can include one or more service categories that are grouped into the same higher level category as the currently searched service category. In the example scenario450, the currently searched service category, “plumbing,” falls in the higher level category “home improvement.” As such, the additional service category recommendations can include other service categories that fall within the higher level category “home improvement.” If the currently searched service category has lower level categories associated with it, the additional service category recommendation can include these lower level categories. For example, if the service category plumber had associated with it lower level categories such as “copper plumbing,” “toilet repair,” “sink repair,” “rain shower installation,” these lower level categories could be listed in the additional service category recommendations478. The additional service category recommendations can be selectable by the user to run a new search using the newly selected service category.

FIG. 5illustrates an example method500associated with providing a service directory predictive search recommendation, according to an embodiment of the present disclosure. It should be appreciated that there can be additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel, within the scope of the various embodiments discussed herein unless otherwise stated.

At block502, the example method500can train a machine learning model to determine predictive search recommendations based on search prediction information, wherein the search prediction information includes at least one of user identification information, user social network information, user social network engagement information, or past search history information. At block504, the example method500can provide search prediction information associated with a user to the machine learning model. At block506, the example method500can present a predictive search recommendation to the user based on the machine learning model and the search prediction information. At block508, the example method500can perform a search based on the predictive search recommendation for one or more search results associated with entity pages on a social networking system. At block510, the example method500can present the one or more search results to the user based on the search, each search result being associated with an entity page on the social networking system.

It is contemplated that there can be many other uses, applications, and/or variations associated with the various embodiments of the present disclosure. For example, in some cases, user can choose whether or not to opt-in to utilize the disclosed technology. The disclosed technology can also ensure that various privacy settings and preferences are maintained and can prevent private information from being divulged. In another example, various embodiments of the present disclosure can learn, improve, and/or be refined over time.

Social Networking System—Example Implementation

The user device610comprises one or more computing devices that can receive input from a user and transmit and receive data via the network650. In one embodiment, the user device610is a conventional computer system executing, for example, a Microsoft Windows compatible operating system (OS), Apple OS X, and/or a Linux distribution. In another embodiment, the user device610can be a device having computer functionality, such as a smart-phone, a tablet, a personal digital assistant (PDA), a mobile telephone, etc. The user device610is configured to communicate via the network650. The user device610can execute an application, for example, a browser application that allows a user of the user device610to interact with the social networking system630. In another embodiment, the user device610interacts with the social networking system630through an application programming interface (API) provided by the native operating system of the user device610, such as iOS and ANDROID. The user device610is configured to communicate with the external system620and the social networking system630via the network650, which may comprise any combination of local area and/or wide area networks, using wired and/or wireless communication systems.

The external system620includes one or more web servers that include one or more web pages622a,622b, which are communicated to the user device610using the network650. The external system620is separate from the social networking system630. For example, the external system620is associated with a first domain, while the social networking system630is associated with a separate social networking domain. Web pages622a,622b, included in the external system620, comprise markup language documents614identifying content and including instructions specifying formatting or presentation of the identified content.

In some embodiments, the social networking system630can include a service directory module646. The service directory module646can, for example, be implemented as the service directory module102, as discussed in more detail herein. As discussed previously, it should be appreciated that there can be many variations or other possibilities. For example, in some embodiments, one or more functionalities of the service directory module646can be implemented in the user device610.

Hardware Implementation