Group item recommendations for ephemeral groups based on mutual information maximization

A neural group recommender system, comprising:

• a transaction database storing payment transaction records;
• a processor having access to the transaction database;
• and
• a software component executed by the processor, the software component comprising:
• a neural preference encoder to generate user preference embeddings from individual user-item interactions extracted from payment transaction records stored in a transaction database;
• a neural preference aggregator to aggregate the user preference embeddings into group preference embeddings, wherein the user preference embeddings and the group preference embeddings form a latent user-group latent embedding space;
• wherein the software component is configured to train the neural preference encoder and the neural preference aggregator by regularizing the latent user-group embedding space to overcome the group interaction sparsity by:
• i) training a discriminator to maximize user-group mutual information (MI) between the group preference embeddings and the user preference embeddings of the group members so that the group preference embeddings encode shared group member preferences, while regularizing the user preference embeddings to capture user social associations, and
• ii) contextually identifying informative group members and regularizing the corresponding group preference embeddings using a contextually weighted user loss (LUG) value based at least in part on the output of the discriminator to contextually weight users' personal preferences in each group in proportion to their user-group MI to reflect personal preferences of the identified informative group members.

A non-transitory computer-readable medium containing program instructions, which when executed by a processor cause the processor to train embedding neural networks of a base group recommender to provide item recommendations for ephemeral groups of group members, the ephemeral groups having group interaction sparsity, the program instructions for:

• providing a neural preference encoder to generate user preference embeddings from individual user-item interactions extracted from payment transaction records stored in a transaction database;
• providing a neural preference aggregator to aggregate the user preference embeddings into group preference embeddings, wherein the user preference embeddings and the group preference embeddings form a latent user-group latent embedding space;
• and
• training the neural preference encoder and the neural preference aggregator by regularizing the latent user-group embedding space to overcome the group interaction sparsity by:
• i) training a discriminator to maximize user-group mutual information (MI) between the group preference embeddings and the user preference embeddings of the group members so that the group preference embeddings encode shared group member preferences, while regularizing the user preference embeddings to capture user social associations, and
• ii) contextually identifying informative group members and regularizing the corresponding group preference embeddings using a contextually weighted user loss (LUG) value based at least in part on the output of the discriminator to contextually weight users' personal preferences in each group in proportion to their user-group MI to reflect personal preferences of the identified informative group members.