# Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation

arXiv: https://arxiv.org/abs/1804.04327 · HTML: https://mlsanity.com/paper/1804.04327/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 10 of 10)

- [Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation](https://mlsanity.com/paper/2109.04200/) (2021)
- [Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network](https://mlsanity.com/paper/2110.04002/) (2021)
- [AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation](https://mlsanity.com/paper/2409.02580/) (2024)
- [Inferring Substitutable and Complementary Products with Knowledge-Aware Path Reasoning based on Dynamic Policy Network](https://mlsanity.com/paper/2110.03276/) (2021)
- [Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation](https://mlsanity.com/paper/2311.09577/) (2023)
- [Price-guided user attention in large-scale E-commerce group recommendation](https://mlsanity.com/paper/2410.02074/) (2024)
- [Explaining Group Recommendations via Counterfactuals](https://mlsanity.com/paper/2601.16882/) (2026)
- [Graph Neural Netwrok with Interaction Pattern for Group Recommendation](https://mlsanity.com/paper/2109.11345/) (2021)
- [Self-supervised Graph Learning for Occasional Group Recommendation](https://mlsanity.com/paper/2112.02274/) (2021)
- [Multi-Granularity Attention Model for Group Recommendation](https://mlsanity.com/paper/2308.04017/) (2023)

## Abstract (arXiv)

This paper proposes Medley of Sub-Attention Networks (MoSAN), a new novel neural architecture for the group recommendation task. Group-level recommendation is known to be a challenging task, in which intricate group dynamics have to be considered. As such, this is to be contrasted with the standard recommendation problem where recommendations are personalized with respect to a single user. Our proposed approach hinges upon the key intuition that the decision making process (in groups) is generally dynamic, i.e., a user's decision is highly dependent on the other group members. All in all, our key motivation manifests in a form of an attentive neural model that captures fine-grained interactions between group members. In our MoSAN model, each sub-attention module is representative of a single member, which models a user's preference with respect to all other group members. Subsequently, a Medley of Sub-Attention modules is then used to collectively make the group's final decision. Overall, our proposed model is both expressive and effective. Via a series of extensive experiments, we show that MoSAN not only achieves state-of-the-art performance but also improves standard baselines by a considerable margin.