Abstract: An indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. A plurality of user profiles in the social networking service is accessed. A first machine-learned model is used to learn embeddings for the plurality of different entities in a d-dimensional space. A second machine-learned model is used to learn an embedding for each of one or more query terms that are not contained in the indication of the plurality of different entities in the social networking service, using the embeddings for the plurality of different entities learned using the first machine-learned model, the second-machine learned model being a deep structured semantic model (DSSM). A similarity score between a query term and an entity is calculated by computing distance between the embedding for the query term and the embedding for the entity in the d-dimensional space.

Abstract: In an example embodiment, gradient boosted decision trees are used to generate tree interaction features, which encode a set of decision rules for features of search results and hence allow feature interactions. These tree interaction features may then be used as features of a GLMix model, essentially injecting non-linearity into the GLMix model.

Abstract: In an example, an indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. Then a plurality of user profiles in the social networking service are accessed. A machine-learned model is then used to calculate, based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred, a similarity score between a first node and second node by computing distance between the first node and the second node in a d-dimensional space on which a plurality of entities are mapped, the similarity score generated using a generalized linear mixed model having a global coefficient vector applied to global function pertaining to the co-occurrence counts and a first random effects coefficient vector applied to a random effects per-country function.

Abstract: Techniques for dynamically altering weights to re-weight attributes of an ordering model based on feedback in a streaming environment are described. In an embodiment, a system accesses, based on a candidate stream definition comprising a role including a title, one or more stream-related information sources, and extracts attributes from the sources. Additionally, the system inputs the attributes to a combined ordering model that is trained by a machine learning algorithm to output ordering scores for member profiles of an online system (e.g., hosting a social networking service). The combined ordering model includes weights assigned to the attributes. Moreover, the system orders, based on the ordering scores, the profiles. Furthermore, the system presents, on a display, of top-ordered profiles. Then, the system accesses feedback regarding the top-ordered profiles and dynamically trains the weights assigned to each of the attributes to alter the weights assigned to the attributes based on the feedback.

Abstract: Systems and methods for query term weighting are disclosed. A server receives a search query for employment candidates, the search query comprising a set of parameters, each parameter having a weight. The server generates, from a data repository storing records associated with professionals, a first set of search results based on the set of parameters and the weights of the parameters in the set. The server transmits the first set of search results. The server receives a response to search result(s) from the first set of search results, the search result(s) being associated with a set of factors, the response indicating a level of interest in the search result(s). The server adjusts the parameters in the set of parameters or adjusts the weights of the parameters based on the response to the search result(s). The server provides an output based on the adjusted parameters or the adjusted weights.

Abstract: In an example, a plurality of user profiles in a social networking service are accessed. A heterogeneous graph structure having a plurality of nodes connected by edges is generated, each node corresponding to a different entity in the social networking service, each edge representing a co-occurrence of entities represented by nodes on each side of the edge in at least one of the user profiles. Weights are calculated for each edge of the heterogeneous graph structure, the weights being based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred. The heterogeneous graph structure is embedded into a d-dimensional space. A machine-learned model is then used to calculate a similarity score between a first node and second node by computing distance between the first node and the second node in the d¬-dimensional space.

Abstract: In an example, a deep learning network is used to calculate a similarity score between a first query in a social networking service and each of one or more suggestable entities in the social networking service. The suggestable entities are determined via a first machine learned model. The deep learning network takes as input the suggestable entities as well as a history of interactions with a graphical user interface of a social networking service by a first member of the social networking service, a history of queries performed via the graphical user interface by the first member, and the first query itself.

Abstract: Systems and methods for applicant tracking system (ATS) integration with a deduplicator are disclosed. A recruiting company computer system accesses a first entity record external to an ATS. The recruiting company computer system determines that the first entity record corresponds to a second entity record within the ATS based on at least first information of the first entity record and second information of the second entity record. The first information is different from the second information. The recruiting company computer system imports, into the second entity record within the ATS, information from the first entity record external to the ATS in response to the first entity record corresponding to the second entity record. The recruiting company computer system provides, in response a request to access information about an entity associated with the second entity record, the information from the first entity record.

Abstract: Systems and methods for messaging service integration with a deduplicator are disclosed. In example embodiments, a server determines that a first user from among a set of users is composing a message to an entity using a first contact information of the entity. The server determines that a second user from among the set of users previously contacted the entity using a second contact information of the entity, the second contact information being different from the first contact information. The server provides for display, within a graphical user interface, an indication that the second user has previously contacted the entity.

Abstract: In an example embodiment, gradient boosted decision trees are used to generate tree interaction features, which encode a set of decision rules for features of search results and hence allow feature interactions. These tree interaction features may then be used as features of a GLMix model, essentially injecting non-linearity into the GLMix model.

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable media for utilizing search facets based on project context. In the system, a process is used for receiving candidate attributes from candidate devices of a plurality of candidates. The processor operates for receiving, via the network interface, user-entered attributes from a user device of a user that are a first part of project attributes of a project and storing the project attributes. The candidates are searched by comparing project attributes with candidate attributes and producing a resultant matching candidate set comprising matching candidates, wherein each matching candidate is assigned an overall matching score.

Abstract: Systems and methods for identifying user information from a set of pages are disclosed. In example embodiments, a server determines that a first set of pages is associated with a specific user based on addresses of the first set of pages having a common portion of a uniform resource locator (URL). The server determines that at least a threshold number of pages from the first set of pages include common information, the common information comprising contact information or social networking information. The server associates the contact information or the social networking information with a user profile of the specific user. The server provides, as a digital transmission, the contact information or the social networking information.

Abstract: Systems and methods for analyzing pipelined data are disclosed. In some examples, a server receives a transaction description requesting candidates for a given transaction. The server accesses first records representing parties that fully completed the given transaction and second records representing parties that were in a pipeline for completing the given transaction but did not fully complete the given transaction. The first records and the second records are stored at a data repository. The server generates a model for predicting whether an identified record represents a party likely to complete the given transaction. The model is generated based on at least the first records and the second records. The server orders a list of third records representing parties likely to complete the given transaction. The server provides an output representing the third records.

Abstract: Systems and methods for identifying user information from a set of pages are disclosed. In example embodiments, a server determines that a first set of pages is associated with a specific user based on addresses of the first set of pages having a common portion of a uniform resource locator (URL). The server determines that at least a threshold number of pages from the first set of pages include common information, the common information comprising contact information or social networking information. The server associates the contact information or the social networking information with a user profile of the specific user. The server provides, as a digital transmission, the contact information or the social networking information.

Abstract: Systems and methods for query expansion are disclosed. In some examples, a server receives, from a client device, a search query for employment candidates, the search query comprising a first set of parameters. The server determines a second set of parameters related to the first set of parameters in response to identifying a second parameter for the second set of parameters that corresponds with a first parameter from the first set of parameters, the professional records being stored in a professional data repository. The server generates, from the professional data repository, a first set of search results based on the first set of parameters and the second set of parameters. The server provides, to the client device, an output representing the first set of search results.

Abstract: In an example, an indication of a plurality of different entities in a social networking service is received, including al least two entities having a different entity type. Then a plurality of user profiles in the social networking service are accessed A machine-learned model is then used to calculate, based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred, a similarity score between a first node and second node by computing distance between the first node and the second node in a d-dimensional space on which a plurality of entities are mapped, the similarity score generated using a generalized linear mixed model having a global coefficient vector applied to global function pertaining to the co-occurrence counts and a first random effects coefficient vector applied to a random effects per-country function.

Abstract: An indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. A plurality of user profiles in the social networking service is accessed. A first machine-learned model is used to learn embeddings for the plurality of different entities in a d-dimensional space. A second machine-learned model is used to learn an embedding for each of one or more query terms that are not contained in the indication of the plurality of different entities in the social networking service, using the embeddings for the plurality of different entities learned using the first machine-learned model, the second-machine learned model being a deep structured semantic model (DSSM). A similarity score between a query term and an entity is calculated by computing distance between the embedding for the query term and the embedding for the entity in the d-dimensional space.

Abstract: In an example, a deep learning network is used to calculate a similarity score between a first query in a social networking service and each of one or more suggestable entities in the social networking service. The suggestable entities are determined via a first machine learned model. The deep learning network takes as input the suggestable entities as well as a history of interactions with a graphical user interface of a social networking service by a first member of the social networking service, a history of queries performed via the graphical user interface by the first member, and the first query itself.

Abstract: In an example, a plurality of user profiles in a social networking service are accessed. A heterogeneous graph structure comprising a plurality of nodes connected by edges is generated, each node corresponding to a different entity in the social networking service, each edge representing a co-occurrence of entities represented by nodes on each side of the edge in at least one of the user profiles. Weights are calculated for each edge of the heterogeneous graph structure, the weights being based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred. The heterogeneous graph structure is embedded into a d-dimensional space. A machine-learned model is then used to calculate a similarity score between a first node and second node by computing distance between the first node and the second node in the d-dimensional space.

Abstract: Techniques for suggesting roles for hiring searches are described An example system comprises a processor, a storage device, and a memory device holding an instruction set executable on the processor to cause the system to perform operations. The system determines an organization employing a user, and identifies, based on the organization, suggested roles to hire for. Bach suggested role includes a tide. Additionally, the system causes presentation of at least some of the suggested roles, and accesses, based on a selection of one of the presented suggested roles, member profiles of members of an online system. Moreover, the system extracts attributes from the member profiles, and generates a search query based on the extracted attributes. Furthermore, the system executes the query on the member profiles in the online system to return search results. Then, the system causes presentation, on a display device, of at least some of the search results.