Abstract: A computer-implemented method for training a neural network based ranking model includes performing a training data augmentation operation on a set of training data to generate a set of synthesized training data, and training a neural network based ranking model using the set of training data and the set of synthesized training data. The set of training data includes, for each of a plurality of queries, respective query-document data and respective relevance judgement data. The query-document data for a query includes data associated with a plurality of query-documents pairs for the query. The relevance judgement data for a query includes one or more sets of user feedback data associated with the query. The set of training data has an imbalanced training data distribution and the set of synthesized training data is arranged for use to reduce training data distribution imbalance of the set of training data.

Abstract: A computer-implemented method for operating a recommender system. The method includes processing user-item interaction data associated with interactions between users and items and processing contextual data associated with the users and/or the items. The method further includes determining, based on the processing of the user-item interaction data and the contextual data, a recommendation of at least one of the items for at least one of the users. The user-item interaction data changes less frequently over time than the contextual data. The at least one of the users have not interacted with the at least one of the items in the recommendation.

Abstract: A federated recommendation system with a server and client devices. The server can group client device users into clusters. The server can further: for each respective cluster, process local model parameters associated with local graph neural networks for at least some client device users in the corresponding cluster, to obtain cluster-level model parameters associated with a cluster-level federated model for the corresponding cluster; and process local model parameters associated with local graph neural networks for at least some client device users in each of two or more of the clusters to obtain global model parameters associated with a global federated model. The server can further provide, to a client device, the cluster-level model parameters associated with the corresponding cluster-level federated model and the global model parameters associated with the global federated model, for facilitating generation or update of a personalized recommendation model for the client device user.