Abstract: Techniques for query intent clustering for automated sourcing are described. In an example embodiment, disclosed is a system comprising 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 obtains one or more recent hire member profiles used as a basis for a search on member profiles in a social networking service. Additionally, the system extracts one or more attributes from the one or more recent hire member profiles and stores the attributes on the storage device. Moreover, the system identifies skills clusters based on the extracted attributes retrieved from the storage device. Furthermore, the system generates a search query based on the identified skills clusters. Then, a search can be performed on member profiles in the social networking service using the generated search query, returning one or more result member profiles as candidates.

Abstract: Techniques for providing explanations of candidate search queries are described. The queries can be created using query intent clustering in an automated sourcing tool. In an example embodiment, disclosed is a system that obtains one or more current candidate member profiles used as a basis for a search on member profiles in an online system. Additionally, the system extracts one or more attributes from the one or more current candidate member profiles. Moreover, the system identifies query intent clusters based on the extracted one or more attributes. Furthermore, the system generates a search query based on the identified query intent clusters. Next, an explanation of the search query can be displayed on a display device of the system. In some embodiments, the online system hosts a social networking service that includes the member profiles, and the identified query intent clusters include skills clusters.

Abstract: In an example embodiment, one or more specified ideal candidates are used to perform a search in a database. One or more attributes are extracted from one or more ideal candidate member profiles. A search query is then generated based on the extracted one or more attributes. Then, a search is performed on member profiles in the social networking service using the generated search query, returning one or more result member profiles.

Abstract: Techniques for using recruiter review data to create training, validation and test sets for automated sourcing are described. An example system obtains sample suggested candidate member profiles and sample search result member profiles in an online system. The system identifies unique pairs of member profiles, each pair consisting of one of the suggested candidate profiles and one of the search result profiles. Additionally, the system generates a label for each of the unique pairs of profiles. The label is generated using a score generated from log information of the online system, the log information including historical records of searcher feedback regarding members of the online system, the score being higher if the searcher accepted the sample search result member profile in a search session. Furthermore, the system inputs the labels into a machine learning algorithm to train a combined ranking model that outputs ranking scores for search result member profiles.

Abstract: In an example embodiment, one or more sample ideal candidate member profiles in a social networking service are obtained, as well as one or more sample search result member profiles in the social networking service. Then, for each unique pair of sample ideal candidate member profile and sample search result member profile, a label is generated using a score generated from log information of the social networking service, the log information including records of communications between a searcher and members of the social networking service, the score being higher if the searcher communicated with both the member corresponding sample ideal candidate member profile and the member corresponding to the sample search result member profile in a same search session. The generated labels are fed into a machine learning algorithm to train a combined ranking model used to output ranking scores for search result member profiles.

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 embodiment, a query for search results is received, the query including at least one value for one facet, a facet defining a categorical dimension for the search results. It is then determined that the facet in the query is exclusive. In response to the determination that the facet is exclusive: for each potential facet different from the facet in the query: for each potential value in the potential facet: conditional entropy gain of the value in the query and the potential value is determined. The potential value in the potential facet that has the highest conditional entropy gain is determined, as is the potential facet with the minimum maximum conditional entropy gain. Then the potential facet with the minimum maximum is input into a machine learning model, causing the machine learning model to output one or more suggested facets to add to the query.

Abstract: Techniques for using recruiter review data to create training, validation and test sets for automated sourcing are described. An example system obtains sample suggested candidate member profiles and sample search result member profiles in an online system. The system identifies unique pairs of member profiles, each pair consisting of one of the suggested candidate profiles and one of the search result profiles. Additionally, the system generates a label for each of the unique pairs of profiles. The label is generated using a score generated from log information of the online system, the log information including historical records of searcher feedback regarding members of the online system, the score being higher if the searcher accepted the sample search result member profile in a search session. Furthermore, the system inputs the labels into a machine learning algorithm to train a combined ranking model that outputs ranking scores for search result member profiles.

Abstract: Techniques for providing explanations of candidate search queries are described. The queries can be created using query intent clustering in an automated sourcing tool. In an example embodiment, disclosed is a system that obtains one or more current candidate member profiles used as a basis for a search on member profiles in an online system. Additionally, the system extracts one or more attributes from the one or more current candidate member profiles. Moreover, the system identifies query intent clusters based on the extracted one or more attributes. Furthermore, the system generates a search query based on the identified query intent clusters. Next, an explanation of the search query can be displayed on a display device of the system. In some embodiments, the online system hosts a social networking service that includes the member profiles, and the identified query intent clusters include skills clusters.

Abstract: Techniques for joint weight attribution for weights of candidate features of a candidate search are described in an example embodiment, disclosed is a system that obtains one or more suggested candidate documents based on a search query specifying one or more parameters. Additionally, the system extracts query intents from the one or more suggested candidate documents, the one or more query intents corresponding to the one or more parameters. Moreover, the system ranks the one or more suggested candidate documents based on the extracted query intents. Furthermore, the system displays top ranked documents on a display device. The system then receives feedback regarding the displayed top ranked documents. Then, weights of a hidden intent are attributed to a set of possible intents based on the received feedback. The feedback can be received retrospectively and proactively. For example, some embodiments perform joint weight attribution based on retrospective and proactive feedback ingestion.

Abstract: Techniques for dynamically altering weights to re-weight candidate features of a candidate search and ranking model in a streaming environment are described. In an embodiment, a disclosed system obtains desired hire documents using a search query specifying parameters. Additionally, the system extracts desired hire-based features from the documents, with the features corresponding to the parameters. Moreover, the system inputs the features to a combined ranking model that is trained by a machine learning algorithm to output a ranking score for each of the documents, with the combined ranking model including weights assigned to each of the features. Furthermore, the system ranks the desired hire documents based on the ranking scores and displays top ranked documents. Then, feedback is received regarding the top ranked documents, and the weights assigned to each of the features are dynamically trained to alter the weights assigned to each of the features based on the feedback.

Abstract: Techniques for query intent clustering for automated sourcing are described. In an example embodiment, disclosed is a system comprising 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 obtains one or more recent hire member profiles used as a basis for a search on member profiles in a social networking service. Additionally, the system extracts one or more attributes from the one or more recent hire member profiles and stores the attributes on the storage device. Moreover, the system identifies skills clusters based on the extracted attributes retrieved from the storage device. Furthermore, the system generates a search query based on the identified skills clusters. Then, a search can be performed on member profiles in the social networking service using the generated search query, returning one or more result member profiles as candidates.

Abstract: In an example embodiment, a query for search results is received, the query including at least one value for one facet, a facet defining a categorical dimension for the search results. It is then determined that the facet in the query is exclusive. In response to the determination that the facet is exclusive: for each potential facet different from the facet in the query: for each potential value in the potential facet: conditional entropy gain of the value in the query and the potential value is determined. The potential value in the potential facet that has the highest conditional entropy gain is determined, as is the potential facet with the minimum maximum conditional entropy gain. Then the potential facet with the minimum maximum is input into a machine learning model, causing the machine learning model to output one or more suggested facets to add to the query.

Abstract: In an example embodiment, one or more sample ideal candidate member profiles in a social networking service are obtained, as well as one or more sample search result member profiles in the social networking service. Then, for each unique pair of sample ideal candidate member profile and sample search result member profile, a label is generated using a score generated from log information of the social networking service, the log information including records of communications between a searcher and members of the social networking service, the score being higher if the searcher communicated with both the member corresponding sample ideal candidate member profile and the member corresponding to the sample search result member profile in a same search session. The generated labels are fed into a machine learning algorithm to train a combined ranking model used to output ranking scores for search result member profiles.

Abstract: In an example embodiment, one or more ideal candidate member profiles in a social networking service are obtained. Then a search is performed on member profiles in the social networking service using a search query, returning one or more result member profiles. One or more query-based features are produced from the one or more result member profiles using the search query. One or more ideal candidate-based features are produced from the one or more result member profiles using the one or more ideal candidate member profiles. The one or more query-based features and the one or more ideal candidate-based features are input to a combined ranking model trained by a machine learning algorithm to output a ranking score for each of the one or more result member profiles. The one or more result member profiles are then ranked based on the ranking scores.

Abstract: In an example embodiment, as time goes on and as refinements are received to an online search, weights assigned to each of the one or more query-based features are dynamically trained to increase as more refinements are received and weights assigned to each of the one or more ideal candidate-based features to decrease as more refinements are received.

Abstract: In an example embodiment, one or more specified ideal candidates are used to perform a search in a database. One or more attributes are extracted from one or more ideal candidate member profiles. A search query is then generated based on the extracted one or more attributes. Then, a search is performed on member profiles in the social networking service using the generated search query, returning one or more result member profiles.