Visa International Service Association

Aravind Sankar

Wei Zhang

Hao Yang

Yanhong Wu

Liang Gou

Yuhang Wu

A method includes extracting, by an analysis computer, a plurality of first datasets from a plurality of graph snapshots using a structural self-attention module. The analysis computer can then extract at least a second dataset from the plurality of first datasets using a temporal self-attention module across the plurality of graph snapshots. The analysis computer can then perform graph context prediction with at least the second dataset.

Dynamic graph representation learning via attention networks

A computer-implemented method is disclosed for training neural networks of a group recommender to provide item recommendations for ephemeral groups having group interaction sparsity. A preference encoder and aggregator generate user and group preference embeddings from user-item interactions, wherein the preference embeddings form a latent user-group latent embedding space. The neural preference encoder and the aggregator are trained by regularizing the latent user-group embedding space to overcome the group interaction sparsity by: i) maximizing user-group mutual information (MI) between the group embeddings and the user embeddings so that the group embeddings encode shared group member preferences, while regularizing the user embeddings to capture user social associations, and ii) contextually identifying informative group members and regularizing the corresponding group embeddings using a contextually weighted user loss value to contextually weight users' personal preferences in proportion to their user-group MI to reflect personal preferences of the identified informative group members.

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San Francisco, CA, United States of America

Aravind Sankar