Bayesian inductive learning in group recommendations for seen and unseen groups

Group recommendation refers to a recommendation of items to a group of users (i.e., mem-bers). When predicting relevant items, a model commonly faces unseen groups that do not appear in the training step. Recently, deep neural networks and an attention mechanism were applied to group recommendations by aggregating user preferences. However, cur-rent methods are insufficient to handle unseen groups (i.e., transductive models) or strug-gle to compute cost-effective attention networks and regularizations. In this study, we propose the novel Bayesian inductive learning method, called IndiG, for making recom-mendations to seen and unseen groups. To learn inductively, a function distribution con-sisting of efficient attention-based aggregation is used as shared information across groups. By incorporating a transductive model as a posterior into the proposed Bayesian method, an inductive model as a prior can learn robustly. We adopt cost-effective regular-ization to prevent degenerated solutions by maximizing a correlation between group rep-resentations of a transductive model and an inductive model, while decorrelating dimensions of group representations. Through experiments, we demonstrated that the pro-posed method outperformed other existing methods. The experiments also showed that the utilization of uncertainty on the predicted ratings of items worked effectively to improve the performance. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This study proposes a novel Bayesian inductive learning method, called IndiG, for making recommendations to both seen and unseen groups. By incorporating a function distribution and a transductive model as prior and posterior, and utilizing cost-effective regularization, the proposed method outperforms existing methods in group recommendation.