Abstract: The disclosed embodiments provide a method and system for performing regularized model adaptation for in-session recommendations. During operation, the system obtains, from a server, a first global version of a statistical model. During a first user session with a user, the system improves a performance of the statistical model by using the first global version to output one or more recommendations to the user and using the first global version and user feedback from the user to create a first personalized version of the statistical model. At an end of the first user session, the system transmits an update containing a difference between the first personalized version and the first global version to the server for use in producing a second global version of the statistical model by the server.

Abstract: In order to leverage annotation bias in batch annotations, obtained via crowdsourcing, on a set of comments on user posts in a social network, a system may select a subset of the comments for annotation based on how informative expected annotations for the comments in the subset are for the one or more classifiers and probabilities of occurrence of the expected annotations based on a predetermined annotation probability distribution. Note that the classifier may predict how likely the expected annotations are accurate for the comments in a given subset. Moreover, the predetermined annotation probability distribution may specify the annotation bias. In this way, the system may use the annotation bias to select the subset that is likely to receive expected annotations and, thus, are that are easier to use in training the classifier.

Abstract: In order to address annotation bias in batch annotations, obtained via crowdsourcing, on a set of comments on user posts in a social network, a system determines an annotation probability distribution based on a factor-graph model of the batch annotations. In particular, during operation the system computes the factor-graph model that represents relationships between feature vectors that represent the comments and the annotations for the comments. Note that, for a given batch of k comments, the factor-graph model may include a statistically dependent combination of statistically independent models of the interrelationships between the feature vectors and the annotations for the k comments. Then, the system calculates the annotation probability distribution based on model parameters associated with the factor-graph model, a mapping function that maps from the feature vectors to the annotations, and an indicator function that represents the annotations for the comments in the batches.