Linkedin Corporation

Microsoft Technology Licensing, LLC

Viet Thuc Ha

Shakti Dhirendraji Sinha

Dhruv Arya

Mario Sergio Rodriguez

Michael Conover

Mathieu Bastian

Jessica V Zuniga

Abhishek Gupta

Deepak Agarwal

Ye Xu

Xianren Wu

Satya Kanduri

Yan Yan

Vijay K Dialani

Jia Li

Lin Guo

Asif Mansoor Ali Makhani

Senthil Sundaram

Ganesh Venkataraman

Ramesh Dommeti

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.

Query building for search by ideal candidates

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 for a combination of entities matching the tagged segments is used to rank one or more retrieved documents responsive to the query.

Entity-faceted historical click-through-rate

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.

Generation of training data for ideal candidate search ranking model

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.

Entity-aware features for personalized job search ranking

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.

Smart suggestions for query refinements

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.

Federated search page construction based on machine learning

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.

Personalized search using searcher features

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.

Personalized search based on searcher interest

In an example embodiment, 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 with members on one axis of the matrix and skills on another axis of the matrix, wherein each cell in the matrix is assigned a first value if the corresponding member explicitly lists the corresponding skill in the corresponding member profile and a second value if the corresponding member does not explicitly list the corresponding skill in the corresponding member profile. The members-skills matrix is factorized into a members matrix and a skills matrix in k-dimensional latent space, and then the dot product of the members matrix and the skills matrix is computed.

The disclosed embodiments provide a reputation system. The reputation system includes a ranking apparatus that obtains a set of reputation scores for one or more dimensions of a set of items in the reputation system and generates a ranking of the items based on the reputation scores and the one or more quantiles. The reputation system also includes a query-processing apparatus that obtains a query comprising the one or more dimensions and one or more quantiles associated with the one or more dimensions and provides the ranking in a response to the query.

Querying of reputation scores in reputation systems

The disclosed embodiments provide a reputation system. The reputation system includes a statistical model associated with a set of items and a set of dimensions of the items in the reputation system, wherein the statistical model is trained using a positive class and a negative class. The reputation system also includes a scoring apparatus that applies the statistical model to a set of features for each of the items to estimate a set of reputation scores for the items. Finally, the reputation system includes a ranking apparatus that enables use of the set of reputation scores in the reputation system.

Estimating reputation scores in reputation systems

The disclosed embodiments provide a reputation system. The reputation system includes a scoring apparatus that provides a matrix of reputation scores for a set of items and a set of dimensions of the items in the reputation system, wherein the matrix comprises unknown values for a subset of the reputation scores. The reputation system also includes an inference apparatus that calculates a factorization of the matrix and uses the factorization to update the matrix with a set of inferred values for the set of reputation scores. Finally, the reputation system includes a ranking apparatus that uses the updated matrix to obtain a ranking of the items by one or more of the dimensions.

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Milpitas, CA, United States of America

Viet Thuc Ha