Abstract: In an example embodiment, a deep learning model is used to learn embedding representations of a heterogeneous information network, where the embedding represents entity-specific properties and network environment properties. Position-aware embeddings specific to the heterogeneous information network may be used as input features of the deep learning model. Furthermore, meta-path embedding specific to the heterogeneous information network may also be used as input features of the deep learning model. Modified embedding propagation methods are further designed to explore better ways to capture network meta-path properties.

Abstract: In an example embodiment, a deep learning model is used to learn embedding representations of a heterogeneous information network, where the embedding represents entity-specific properties and network environment properties. Position-aware embeddings specific to the heterogeneous information network may be used as input features of the deep learning model. Furthermore, meta-path embedding specific to the heterogeneous information network may also be used as input features of the deep learning model. Modified embedding propagation methods are further designed to explore better ways to capture network meta-path properties.

Abstract: Techniques for generating training data to capture entity-to-entity affinities are provided. In one technique, first interaction data is stored that indicates interactions, that occurred during a first time period, between a first set of users and content items associated with a first set of entities. Also, second interaction data is stored that indicates interactions, that occurred during a second time period, between a second set of users and content items associated with a second set of entities. For each interaction in the first interaction data: (1) a training instance is generated; (2) it is determined whether the interaction matches one in the second interaction data; and (3) if the interaction does not match, then a negative label is generated for the training instance, else a positive label is generated for the training instance. Machine learning techniques are then used to train a machine-learned model based on the generating training instances.