Abstract: A system for rating job transitions includes a probability determiner for determining a set of probabilities, a grouper for determining a group of job transition histories, a filter for determining a subset of job transition histories from the group of job transition histories by filtering based at least in part on a transition characteristic, a normalizer for determining a model set of job transition histories by normalizing the subset of job transition histories, a feature vector extractor for determining a set of feature vectors using the model set of job transition histories, a model builder for determining a model based at least in part on the set of feature vectors, and a rater for rating potential job transitions of a selected employee based on the model using a set of test feature vectors.

Abstract: A system for determining retention risk comprises a grouper, a filter, a normalizer, a feature vector extractor, a model builder, and a predictor. The grouper is for determining a set of time series of transactions where each is associated with one employee. The filter is for filtering the set of time series of transactions based on an employee transition characteristic to determine a subset of time series. The normalizer is for determining a model set of time series by normalizing the subset of time series. The feature vector extractor is for determining a set of feature vectors determined from a time series of the model set of time series. The model builder is for determining one or more models based at least in part on the set of feature vectors. The predictor is for predicting retention risk for a given employee using the one or more models.

Abstract: Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.

Abstract: Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.

Abstract: The present disclosure is directed to systems and methods for determining semantically related terms. Generally, one or more seed terms are received from a user. A system searches a first index comprising a plurality of terms and one or more webpages associated with each term of the plurality of terms to determine a plurality of webpages associated with the seed terms. The system then searches a second index comprising a plurality of webpages and one or more terms associated with each webpage of the plurality of webpages to determine a plurality of potential terms associated with the plurality of webpages associated with the seed terms. At least one term of the plurality of potential terms is suggested to a user.

Abstract: The present disclosure is directed to systems and methods for determining semantically related terms. Generally, one or more seed terms are received from a user. A system searches a first index comprising a plurality of terms and one or more webpages associated with each term of the plurality of terms to determine a plurality of webpages associated with the seed terms. The system then searches a second index comprising a plurality of webpages and one or more terms associated with each webpage of the plurality of webpages to determine a plurality of potential terms associated with the plurality of webpages associated with the seed terms. At least one term of the plurality of potential terms is suggested to a user.

Abstract: The present disclosure is directed to systems and methods for determining semantically related terms. Generally, one or more seed terms are received from a user. A system searches a first index comprising a plurality of terms and one or more webpages associated with each term of the plurality of terms to determine a plurality of webpages associated with the seed terms. The system then searches a second index comprising a plurality of webpages and one or more terms associated with each webpage of the plurality of webpages to determine a plurality of potential terms associated with the plurality of webpages associated with the seed terms. At least one term of the plurality of potential terms is suggested to a user.