Multi-view group representation learning for location-aware group recommendation

With the development of location-based services (LBS), many location-based social sites like Foursquare and Plancast have emerged. People can organize and participate in group activities on those sites. Therefore, recommending venues for group activities is of practical value. However, the group decision making process is complicated, requiring trade-offs among group members. And the data sparsity and cold-start problems make it difficult to make effective group recommendation. In this manuscript, we propose a Multi-view Group Representation Learning (MGPL) framework for location-aware group recommendation. The proposed multi-view group representation learning framework can leverage multiple types of information for deep representation learning of group preferences and incorporate the spatial attributes of locations to further capture the group mobility preferences. Experiments on two real datasets Foursqaure and Plancast show that our method significantly outperforms the-state-of-art approaches. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This manuscript proposes a Multi-view Group Representation Learning framework for location-aware group recommendation, which effectively addresses the complexities of group decision making, data sparsity, and cold-start problems. Experiments on Foursquare and Plancast datasets demonstrate that the proposed method significantly outperforms existing approaches.