Microsoft Technology Licensing, LLC

Avi Caciularu

Nir Nice

Noam Koenigstein

Oren Barkan

Ori Katz

Itzik Malkiel

Roy Hirsch

Yonathan Weill

Omri Armstrong

Amir Hertz

Idan Rejwan

A method of training a machine learning model is provided. The method includes receiving labeled training data in the machine learning model, the received labeled training data including content data for items accessible to a user and input usage data representing recorded interaction between the user and the items, wherein the received content data for each item includes data representing intrinsic attributes of the item. The method further includes selecting a set of the input usage data that excludes input usage data for a proper subset of the items and training the machine learning model based on both the content data and the selected set of input usage data of the received labeled training data for the items.

Model with usage data compensation

A relational similarity determination engine receives as input a dataset including a set of entities and co-occurrence data that defines co-occurrence relations for pairs of the entities. The relational similarity determination engine also receives as input side information defining explicit relations between the entities. The relational similarity determination engine jointly models the co-occurrence relations and the explicit relations for the entities to compute a similarity metric for each different pair of entities within the dataset. Based on the computed similarity metrics, the relational similarity determination engine identifies a most similar replacement entity from the dataset for each of the entities within the dataset. For a select entity received as an input, the relational similarity determination engine outputs the identified most similar replacement entity.

Representation learning with side information

A method of generating an aggregate saliency map using a convolutional neural network. Convolutional activation maps of the convolutional neural network model are received into a saliency map generator, the convolutional activation maps being generated by the neural network model while computing the one or more prediction scores based on unlabeled input data. Each convolutional activation map corresponds to one of the multiple encoding layers. The saliency map generator generates a layer-dependent saliency map for each encoding layer of the unlabeled input data, each layer-dependent saliency map being based on a summation of element-wise products of the convolutional activation maps and their corresponding gradients. The layer-dependent saliency maps are combined into the aggregate saliency map indicating the relative contributions of individual components of the unlabeled input data to the one or more prediction scores computed by the convolutional neural network model on the unlabeled input data.

Deep gradient activation map model refinement

An anchor-based collaborative filtering system receives a training dataset including user-item interactions each identifying a user and an item that the user has positively interacted with. The system defines a vector space and distributes the items of the training dataset within the vector based on a determined similarity of the items. The system further defines a set of taste anchors that are each associated in memory with a subgroup of the items in a same neighborhood of the vector space. To make a recommendation to an individual user, the system identifies an anchor-based representation for the individual user that includes a subset of the defined taste anchors that best represents the types of items that the user has favorably interacted with in the past. The taste anchors included in the identified anchor-based representation for the individual user are used to make recommendations to the user in the future.

Anchor-based collaborative filtering

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Tel Aviv, Israel

Avi Caciularu