Abstract: Systems and methods of the present disclosure are directed to a method for training a machine-learned semantic matching model. The method can include obtaining a first and second document and a first and second activity log. The method can include determining, based on the first document activity log and the second document activity log, a relation label indicative of whether the documents are related. The method can include inputting the documents into the model to receive a semantic similarity value representing an estimated semantic similarity between the first document and the second document. The method can include evaluating a loss function that evaluates a difference between the relation label and the semantic similarity value. The method can include modifying values of parameters of the model based on the loss function.

Abstract: Techniques are described herein for enabling more computationally efficient organization of files within a cloud storage system. A method includes: receiving information identifying a document and a set of folders; for each folder in the set of folders, using a trained model to predict a similarity measure between the folder and the document; for each folder in the set of folders, determining a score for the folder based on the predicted similarity measure for the folder; selecting a candidate folder from the set of folders using the scores of the folders within the set of folders; and providing, on a user interface, a selectable option to associate the document with the candidate folder.

Abstract: Methods, systems and programming for providing query suggestions based on user feedback. In one example, a prefix of a query is first received. An input including a prefix of a query is received from a user in a search session. A plurality of query suggestions are fetched based on the prefix of the query. Rankings of the plurality of query suggestions are determined based, at least in part, on the user's previous interactions in the search session with respect to at least one of the plurality of query suggestions. The at least one of the plurality of query suggestions has been previously provided to the user in the search session. The plurality of query suggestions are provided in the search session based on their rankings as a response to the input.

Abstract: Methods, systems and programming for providing query suggestions based on user feedback. In one example, a prefix of a query is first received. An input including a prefix of a query is received from a user in a search session. A plurality of query suggestions are fetched based on the prefix of the query. Rankings of the plurality of query suggestions are determined based, at least in part, on the user's previous interactions in the search session with respect to at least one of the plurality of query suggestions. The at least one of the plurality of query suggestions has been previously provided to the user in the search session. The plurality of query suggestions are provided in the search session based on their rankings as a response to the input.

Abstract: Methods, systems and programming for providing query suggestions based on user feedback. In one example, a prefix of a query is first received. An input including a prefix of a query is received from a user in a search session. A plurality of query suggestions are fetched based on the prefix of the query. Rankings of the plurality of query suggestions are determined based, at least in part, on the user's previous interactions in the search session with respect to at least one of the plurality of query suggestions. The at least one of the plurality of query suggestions has been previously provided to the user in the search session. The plurality of query suggestions are provided in the search session based on their rankings as a response to the input.