近期團隊論文“A Novel Group Recommendation Model with Two-stage Deep Learning”被自動化&計算機交叉領(lǐng)域國際頂刊IEEE Transactions on Systems Man Cybernetics-Systems（中科院一區(qū)，IF：13.451，中國自動化學(xué)會A類）錄用 。

Abstract—Group recommendation has recently drawn a lot of attention to the recommender system community. Currently several deep learning-based approaches are leveraged to learn preferences of groups for items and predict next items in which groups may be interested. Yet, their recommendation performance is still unsatisfactory due to sparse group-item interactions. To address this challenge, this study presents a novel model, called GRMTDL (Group Recommendation Model with Two-stage Deep Learning), which encompasses two sequential stages: group representation learning (GRL) and group preference learning (GPL). In GRL, we first construct an undirected tripartite graph over group-user-item interactions, and then employ it to accurately learn group semantic features through a spatial-based variational graph auto-encoder network. While in GPL, we first introduce a dual PL-network that contains two structure-sharing sub-networks: group PL-network employed for group preference learning and user PL-network utilized for user preference learning. Then, we design a novel layered transfer learning (LTL) method to learn group preferences by alternately optimizing these two sub-networks. In particular, it can effectively absorb knowledge of user preferences into the process of group preference learning. Furthermore, extensive experiments on four real-world datasets demonstrate that the proposed GRMTDL model outperforms the state-of-the-art baselines for group recommendation.

Keywords—Group recommendation, graph auto-encoder, knowledge transferring, deep learning, representation learning.