Abstract: A system and method for determining whether a computer system is experiencing a problem are provided. Multiple data sets are identified. Each data set includes multiple values indicating a set of attributes that relate to a computer system and that correspond to a different time period of multiple time periods, such as days or hours. A model is generated based on the multiple data sets. A particular data set is identified that includes a set of values that correspond to the set of attributes and a particular time period that is subsequent to each of the multiple time periods. The model is used to generate a predicted value based on the particular data set. An actual value that corresponds to the particular time period is identified. A difference between the actual value and the predicted value is calculated and indicates a likelihood that the computer system is experiencing a problem.

Abstract: Techniques for assisting a user in determining an interest level between a member of a social network system and an organization. According to various embodiments, applicant data is accessed for applicants having applied to an organization. A set of common applicant characteristics is determined for the set of applicant data. Member data is accessed indicative of a member of an online social media network. An interest score is generated based on a comparison of the member data and the set of applicant data. An identification of the organization is presented based on the interest score.

Abstract: Embodiments relate to user attitude modeling and behavior prediction for a social media network. One aspect includes collecting data relating to previously demonstrated sentiments, opinions, and actions attributed to network users toward a topic. Another aspect includes creating a model from the data, which includes factorizing the actions for behavior inference, factorizing auxiliary content from the network for opinion and sentiment inferences, and applying sentiment and opinion regularization to constrain user preferences on implicit topics to explicit sentiments and explicit opinions. Another aspect includes applying the model to a new user of the network with respect to the topic, and generating a prediction with respect to the user that includes predicting sentiment and opinion as a function of the auxiliary content and feature coefficients learned during a training process, and predicting a future action of the user as a function of the auxiliary content and latent profiles of the topic.