Microsoft Technology Licensing, LLC

Baolei Li

Yi Wu

Wei Guang Lu

Yang Yang

Qi Guo

Bing Zhao

Romer E Rosales-Delmoral

Daniel Sairom Krishnan Hewlett

Dan Liu

Xuhong Zhang

Mingzhou Zhou

Anthony Hsu

Kexin Nie

Wensheng Sun

Keqiu Hu

Chenya Zhang

In an example embodiment, a platform is provided that utilizes information available to a computer system to feed a neural network. The neural network is trained to determine both the probability that a searcher would select a given potential search result if it was presented to him or her and the probability that a subject of the potential search result would respond to a communication from the searcher. These probabilities are essentially combined to produce a single score that can be used to determine whether to present the searcher with the potential search result and, if so, how high to rank the potential search result among other search results. In a further example embodiment, embeddings used for the input features are modified during training to maximize an objective.

Embedding layer in neural network for ranking candidates

This disclosure provides for systems and methods that generate personalized electronic messages for members of a networked communication service. The personalized electronic messages are generated according to commonalities between member profiles. In one approach, an electronic message template is referenced based on the commonalities. The electronic message template is populated with member profile attribute values selected from the member profiles. In another approach, a long-short term memory (LSTM) recurrent neural network (RNN) is used to generate the electronic messages. Under this approach, a sequence-to-sequence model is trained using previous electronic messages labeled with one or more member profile attributes and/or member profile attribute values. When provided with one or more member profile attribute values associated with matching member profiles, the LSTM RNN outputs a relevant and appropriate electronic message designed to create an interest in the recipient of the electronic message.

Writing personalized electronic messages using template-based and machine-learning approaches

Techniques for auto-tuning anomaly detection are provided. In one technique, training data is stored that comprises training instances, each of which comprises a severity-duration pair and a label that indicates whether the severity-duration pair represents an anomaly. A model is trained based on a first subset of the training data. A second subset of the training data is identified where each training instance includes a positive label that indicates that that training instance represents an anomaly. Based on the second subset of the training data, the model generates multiple scores, each of which corresponds to a different training instance. A minimum score is identified that ensures a particular recall rate of the model. In response to receiving a particular severity-duration pair, the model generates a particular score for the particular severity-duration pair. A notification of an anomaly is generated if the particular score is greater than the minimum score.

Auto-tune anomaly detection

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Santa Clara, CA, United States of America

Baolei Li