Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums which provide for a personalized expertise searching. When a user of the social networking service enters a search query, the system determines if the user is searching for members who possess a particular skill. If the user is searching for members who possess a particular skill, the search results are post-processed by personalizing the search results using one or more machine-learning models which utilize one or more observed features about the user that enters the query, the skills of the members of the social networking service, and the query itself. In some examples, the system may utilize multiple machine-learning models in multiple passes to fine tune the relevance of the search results and to ensure that the post-processing returns search results in a timely manner.

Abstract: A user submits a job search query in an online social networking system. The online social networking system calculates a score based on the similarity between the job search query and the profile of the user. When the score transgresses a threshold, the job search query is enhanced by adding data from the profile of the user to the job search query. The job search query is then used to search for, identify, and display jobs in the online social networking system.

Abstract: Apparatuses, computer readable medium, and methods are disclosed for federated search page construction based on machine learning. The method may include receiving a search query submitted by a searcher and submitting the search query to a plurality of sources of information to generate a plurality of search results. The method may further include ranking the plurality of search results based on historical search data and selecting a primary search result and a secondary search result of the plurality of search results based on the ranking. The method may further include constructing a federated search results page with a first portion of the first search results positioned first, a cluster of the second search result positioned second, and a second portion of the first search results positioned third. The method may include causing the federated search results page to be displayed to the searcher.

Abstract: A system and method for generating modifiers for updated search queries are provided. In example embodiments, metadata is accessed, the metadata corresponds to search results of an input query and comprising a plurality of candidate modifiers. A score is calculated for each candidate based on a relevance value that indicates the correlation between a candidate modifier and the input query. A list of top number of candidate modifiers is generated based on the score of the candidate modifier transgressing a first threshold. A uniqueness score is calculated for combination pairs of candidate modifiers within the list, the uniqueness score being used to eliminate candidate modifiers. The list of top number of candidate modifiers is presented, at a user interface, according to a ranked order based on the score.

Abstract: A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize people search results based on respective priority scores of the associated keywords used as search terms. The associated keywords represent respective people search results pages (PSERPs). The SEO system generates priority scores for different keyword, 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/or a value expressing how likely is a search that includes the keyword as a search term is to produce relevant results.

Abstract: In an example embodiment, one or more query terms are obtained. For each of the one or more query terms, a standardized entity taxonomy is searched to locate a standardized entity that most closely matches the query term. A confidence score is calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term. In response to a determination that the confidence score transgresses a threshold, the query term is associated with an entity identification corresponding to the standardized entity that most closely matches the query term. One or more query rewriting rules corresponding to an entity type of the standardized entity having the entity identification are obtained. The one or more query rewriting rules are executed to rewrite the first query such that the rewritten query, when performed on a data source, returns fewer search results than the first query would have.

Abstract: A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize keywords (potential search terms) that represent respective people search results pages (PSERPs). The value of a people-related keyword is expressed as a priority score assigned to that keyword. The SEO system generates 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/or a value expressing how likely is a search that includes the keyword as a search term is to produce relevant results.

Abstract: A system and method for automatic topic tagging are provided. In example embodiments, input content is received, the content includes a plurality of terms. Term vectors are generated from the plurality of terms. Candidate topics are identified to assigned to the plurality of terms. Topics are assigned to the received content from the identified candidate topics.

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 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: 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 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 query terms are obtained. Then, for each of the one or more query terms, a standardized entity taxonomy is searched to locate a standardized entity that most closely matches the query term, with the standardized entity taxonomy comprising an entity identification for each of a plurality of different standardized entities. A confidence score is then calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term, and the query term is tagged with the entity identification corresponding to the standardized entity that most closely matches the query term and the calculated confidence score.

Abstract: Techniques for searching for future candidates are disclosed herein. In some example embodiments, a future candidate system determines one or more target candidate attributes based on user input, and identifies one or more precedent candidate attributes based on a sequential relationship between the one or more precedent candidate attributes and the one or more target candidate attributes, with the one or more precedent candidate attributes being different from and preceding the one or more target candidate attributes. In some example embodiments, the future candidate system identifies one or more candidates from among a plurality of candidates based on a determination that the one or more candidates comprise the one or more precedent candidate attributes, and causes the identified one or more candidates to be displayed on a client device.

Abstract: A system and method for personalized search user searcher features may include obtaining a search term from a member of a social network at a user device via the network interface. An initial result may be generated based on the search term, including a first group of content items from a social network and stored in a content database, the content items including member profiles of members of the social network. Each of the content items of the first group may be ranked based on information indicative of interactions from an activity database with the content items of the first group, the interactions being by at least a second user of the social network different than the first user. A second group of the content items may be displayed, including at least some of the first group of the content items, based on the rank of the first group of the content items.

Abstract: A system and method for personalized search based on similarity may include obtaining a search term. An initial result based on the search term and including a first group of content items as stored in a content database may be generated. Each of the content items of the first group may be ranked based, at least in part, on similarity scores, each of the similarity scores individually based on a first member profile relative to individual ones of second member profiles to which an activity related to a content item of the first group corresponds. The user device may display a second group of the content items, including at least some of the first group of the content items, according to the rank of the first group of the content items.

Abstract: A system and method for personalized search based on searcher interest may include obtaining a search term from a member of a social network at a user device via the network interface. An initial result may be generated based on the search term, including a first group of content items from a social network and stored in a content database, the content items including member profiles of members of the social network. Each of the content items of the first group may be ranked based on information from an activity database, the activity database storing the information related to the social network, the activities including interactions with search results that include ones of the member profiles. A second group of the content items may be displayed, including at least some of the first group of the content items, based on the rank of the first group of the content items.

Abstract: In an example, a plurality of member profiles in a social networking service are obtained, each member profile identifying a member and listing one or more skills the corresponding member has explicitly added to the member profile, the one or more skills indicating a proficiency by the member in the corresponding skill. A members-skills matrix is formed, wherein each cell in the matrix is assigned a value based on whether the corresponding member has the corresponding skill. The dot product of the members matrix and the skills matrix is then computed and used to identify one or more latent skills of a first member of the social networking service. Then a first digitally stored member profile is augmented with the one or more latent skills by combining the one or more latent skills with explicit skills for purposes of one or more searches that utilize member skills as an input variable.

Abstract: In an example embodiment, usage information is used to calculate one or more individual document historical information-deficient metrics (IDHIDMs) by combining values for the one or more metrics from multiple documents within the document corpus that share an identical combination of entities of the one or more entity types. A search query is segmented into a plurality of segments, wherein at least one of the plurality of segments is tagged as a first entity type and at least one of the plurality of segments is tagged as a second entity type. At least one IDHIDM for a combination of entities matching the tagged segments is used to rank one or more retrieved documents responsive to the query.