Abstract: A member profile including a vector containing a field for each of a plurality of skills and a rating of one or more of the skills in the vector for a member of a social networking service is obtained. A first distance indicating a vector distance between the vector of the member profile and a vector of a hypothetical member profile representing the perfect job candidate is obtained. A hypothetical member profile for the member is created by combining the vector of the member profile with the indication of how each of the one or more skills is improved through taking the course from course information. A second distance between the member and the hypothetical perfect candidate for the job is obtained, and the difference between the first distance and the second distance is calculated to determine an estimate of how much the course will increase the member's job chances.

Abstract: In an example embodiment, a member profile corresponding to a member of a social networking service is obtained. Usage information for the member is then obtained, and one or more member metrics are calculated based on the member profile and usage information for the corresponding member. A plurality of features are extracted from the member profile and the one or more member metrics. The plurality of features is inserted into an organization name confidence score model to obtain a confidence score for an organization name in the member profile.

Abstract: In an example embodiment, a web page is obtained using a web page address stored in a first record and is parsed to extract one or more images from the web page along with a first plurality of features for each of the one or more images from the web page. Information about each image of the web page and the extracted first plurality of features for the web page are input into a supervised machine learning classifier to calculate a logo confidence score for each image of the web page, the logo confidence score indicating the probability that the image is an organization logo. In response to a particular image in the web page having a logo confidence score transgressing a first threshold, the particular image is injected into an organization logo field of the first record.

Abstract: In an example embodiment, a fuzzy join operation is performed by, for each pair of records, evaluating a first plurality of features for both records in the pair of records by calculating term frequency-inverse term frequency (TF-IDF) for each token of each field relevant to each feature and based on the calculated TF-IDF for each token of each field relevant to each feature, computing a similarity score based on the similarity function by adding a weight assigned to the TF-IDF for any token that appears in both records. Then a graph data structure is created, having a node for each record in the plurality of records and edges between each of the nodes, except, for each record pair having a similarity score that does not transgress a first threshold, causing no edge between the nodes for the record pair to appear in the graph data structure.

Abstract: In an example embodiment, a web page is obtained using a web page address stored in a first record and is parsed to extract one or more images from the web page along with a first plurality of features for each of the one or more images from the web page. Information about each image of the web page and the extracted first plurality of features for the web page are input into a supervised machine learning classifier to calculate a logo confidence score for each image of the web page, the logo confidence score indicating the probability that the image is an organization logo. In response to a particular image in the web page having a logo confidence score transgressing a first threshold, the particular image is injected into an organization logo field of the first record.

Abstract: In an example embodiment, a web page is obtained using a web page address stored in a first record and is parsed to extract one or more images from the web page along with a second plurality of features for each of the one or more images from the web page. Information about each image of the web page and the extracted second plurality of features for the web page are input into a supervised machine learning classifier to calculate a logo confidence score for each image of the web page, the logo confidence score indicating the probability that the image is an organization logo. In response to a particular image in the web page having a logo confidence score transgressing a first threshold, the particular image is injected into an organization logo field of the first record.

Abstract: In an example embodiment, an organization name is retrieved from an organization name field of a first record. A web search is performed using the organization name, producing web search results. A second plurality of features is extracted for each web search result in the set of web search results. Each of the extracted second plurality of features for each web search result in the set of web search results is input into a supervised machine learning classifier to classify each of the web search results in the set of web search results as either containing an organization web address or not containing an organization web address. In response to a determination by the supervised machine learning classifier that a first web search result contains an organization web address, the organization web address from the first web search result is injected into an organization web address field of the first record.

Abstract: In an example embodiment, a fuzzy join operation is performed by, for each pair of records, evaluating a first plurality of features for both records in the pair of records by calculating term frequency-inverse term frequency (TF-IDF) for each token of each field relevant to each feature and based on the calculated TF-IDF for each token of each field relevant to each feature, computing a similarity score based on the similarity function by adding a weight assigned to the TF-IDF for any token that appears in both records.

Abstract: In an example embodiment, a web page is obtained using a web page address stored in a first record and is parsed to extract one or more images from the web page along with a second plurality of features for each of the one or more images from the web page. Information about each image of the web page and the extracted second plurality of features for the web page are input into a supervised machine learning classifier to calculate a logo confidence score for each image of the web page, the logo confidence score indicating the probability that the image is an organization logo. In response to a particular image in the web page having a logo confidence score transgressing a first threshold, the particular image is injected into an organization logo field of the first record.

Abstract: In an example embodiment, a member profile corresponding to a member of a social networking service is obtained. Usage information for the member is then obtained, and one or more member metrics are calculated based on the member profile and usage information for the corresponding member. A plurality of features are extracted from the member profile and the one or more member metrics. The plurality of features is inserted into an organization name confidence score model to obtain a confidence score for an organization name in the member profile.

Abstract: A member profile including a vector containing a field for each of a plurality of skills and a rating of one or more of the skills in the vector for a member of a social networking service is obtained. A first distance indicating a vector distance between the vector of the member profile and a vector of a hypothetical member profile representing the perfect job candidate is obtained. A hypothetical member profile for the member is created by combining the vector of the member profile with the indication of how each of the one or more skills is improved through taking the course from course information. A second distance between the member and the hypothetical perfect candidate for the job is obtained, and the difference between the first distance and the second distance is calculated to determine an estimate of how much the course will increase the member's job chances.

Abstract: In an example embodiment, a method is provided to calculate a distance for a first member of a social networking service and a job opening, which indicates the likelihood that the first member will obtain the job. The distance is calculated by applying one or more policies to member profiles of members who applied for similar jobs to obtain a hypothetical perfect candidate for the job. The member's profile is then automatically compared to the hypothetical perfect candidate profile to calculate the distance.

Abstract: In an example embodiment, a method is provided where information regarding a member of a social networking service and a job opening are obtained. Then information about a plurality of courses available for the member to take is obtained, the information including cost, time, and a vector including, for each of a plurality of skills identified in a member profile of the member, an effect of taking the corresponding course. A subset selection algorithm is then used to select an optimal combination of courses selected from the plurality of courses, based on minimizing cost and time while maximizing cumulative increase in odds of the member successfully obtaining the job opening from taking the selected courses.