Abstract: System and methods are provided that can address cold-start problems in database keyword searches. The search system generates machine-learned values for new item and queries based on historical signals for already existing item and queries. The values are used as input in a ranking model to rank search results for a user query. The initial values for the new item query pairs predict user engagement with the new item query pairs based on historical data for existing item query pairs and increase the visibility of new items to accumulate user interaction data for the new items. After additional user interactions are received, the values are updated using a Bayesian formula.

Abstract: A system implements a key value memory network including a key matrix with key vectors learned from training static feature data and time-series feature data, a value matrix with value vectors representing time-series trends, and an input layer to receive, for a target entity, input data comprising a concatenation of static feature data of the target entity, time-specific feature data, and time-series feature data for the target entity. The key value memory network also includes an entity-embedding layer to generate an input vector from the input data, a key-addressing layer to generate a weight vector indicating similarities between the key vectors and the input vector, a value-reading layer to compute a context vector from the weight and value vectors, and an output layer to generate predicted time-series data for a target metric of the target entity by applying a continuous activation function to the context vector and the input vector.

Abstract: A system implements a key value memory network including a key matrix with key vectors learned from training static feature data and time-series feature data, a value matrix with value vectors representing time-series trends, and an input layer to receive, for a target entity, input data comprising a concatenation of static feature data of the target entity, time-specific feature data, and time-series feature data for the target entity. The key value memory network also includes an entity-embedding layer to generate an input vector from the input data, a key-addressing layer to generate a weight vector indicating similarities between the key vectors and the input vector, a value-reading layer to compute a context vector from the weight and value vectors, and an output layer to generate predicted time-series data for a target metric of the target entity by applying a continuous activation function to the context vector and the input vector.

Abstract: This disclosure involves using key-value memory networks to predict time-series data. For instance, a computing system retrieves, for a target entity, static feature data and target time-series feature data. The computing system can normalize the target time-series feature data based on a normalization scale. The computing system also generates input data by, for example, concatenating the static feature data, the normalized time-series feature data, and time-specific feature data. The computing system generates predicted time-series data for the target metric of the target entity by applying a key-value memory network to the input data. The key-value memory network can include a key matrix learned from training static feature data and training time-series feature data, a value matrix representing time-series trends, and an output layer with a continuous activation function for generating predicted time-series data.

Abstract: System and methods are provided that can address cold-start problems in database keyword searches. The search system generates machine-learned values for new items based on historical signals for already existing items. These initial values are generated at the time of new item's inclusion in the search index. The values are used as input in a ranking model to rank search results for a user query. The initial values for the new items predict user engagement with the new items based on historical data for existing items and increase the visibility of new items to accumulate user interaction data for the new items.

Abstract: This disclosure involves using key-value memory networks to predict time-series data. For instance, a computing system retrieves, for a target entity, static feature data and target time-series feature data. The computing system can normalize the target time-series feature data based on a normalization scale. The computing system also generates input data by, for example, concatenating the static feature data, the normalized time-series feature data, and time-specific feature data. The computing system generates predicted time-series data for the target metric of the target entity by applying a key-value memory network to the input data. The key-value memory network can include a key matrix learned from training static feature data and training time-series feature data, a value matrix representing time-series trends, and an output layer with a continuous activation function for generating predicted time-series data.

Abstract: Non-limiting examples of the present disclosure describe query classification to identify appropriateness of a query. A query may be received by at least one processing device. A deep neural network (DNN) model may be applied to evaluate the query. A vector representation may be generated for query based on application of the DNN model, where the DNN model is trained to classify queries according to a plurality of categories of appropriateness. The DNN model may be utilized to classify the query in a category of appropriateness based on analysis of the vector representation. In one example, auto-complete suggestions for the query may be filtered based on the classification of the category of appropriateness. In another example, classification of the query may be provided to an entry point. In yet another example, a response to the query is managed based on the classification of the query. Other examples are also described.

Abstract: Non-limiting examples of the present disclosure describe query classification to identify appropriateness of a query. A query may be received by at least one processing device. A deep neural network (DNN) model may be applied to evaluate the query. A vector representation may be generated for query based on application of the DNN model, where the DNN model is trained to classify queries according to a plurality of categories of appropriateness. The DNN model may be utilized to classify the query in a category of appropriateness based on analysis of the vector representation. In one example, auto-complete suggestions for the query may be filtered based on the classification of the category of appropriateness. In another example, classification of the query may be provided to an entry point. In yet another example, a response to the query is managed based on the classification of the query. Other examples are also described.