# Enhancing Live Broadcast Engagement: A Multi-modal Approach to Short Video Recommendations Using MMGCN and User Preferences

arXiv: https://arxiv.org/abs/2506.23085 · HTML: https://mlsanity.com/paper/2506.23085/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 73 references that are in the map.
- First posted on arXiv 29 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [A systematic review and taxonomy of explanations in decision support and recommender systems](https://mlsanity.com/paper/2006.08672/) (2020)
- [Do Offline Metrics Predict Online Performance in Recommender Systems?](https://mlsanity.com/paper/2011.07931/) (2020)
- [Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation](https://mlsanity.com/paper/2412.14978/) (2024)
- [Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation](https://mlsanity.com/paper/2106.03415/) (2021)
- [DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering](https://mlsanity.com/paper/2111.04105/) (2021)
- [Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images](https://mlsanity.com/paper/2306.12510/) (2023)
- [Multi-Modal Recommendation Unlearning for Legal, Licensing, and Modality Constraints](https://mlsanity.com/paper/2405.15328/) (2024)
- [Transforming Dental Diagnostics with Artificial Intelligence: Advanced Integration of ChatGPT and Large Language Models for Patient Care](https://mlsanity.com/paper/2406.06616/) (2024)
- [Explore Cross-Codec Quality-Rate Convex Hulls Relation for Adaptive Streaming](https://mlsanity.com/paper/2408.09044/) (2024)
- [MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion](https://mlsanity.com/paper/2407.00056/) (2024)
- [Leveraging Large Language Models for Cybersecurity: Enhancing SMS Spam Detection with Robust and Context-Aware Text Classification](https://mlsanity.com/paper/2502.11014/) (2025)
- [MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2504.16576/) (2025)

## Abstract (arXiv)

The purpose of this paper is to explore a multi-modal approach to enhancing live broadcast engagement by developing a short video recommendation system that incorporates Multi-modal Graph Convolutional Networks (MMGCN) with user preferences. To provide personalized recommendations tailored to individual interests, the proposed system considers user interaction data, video content features, and contextual information. With the aid of a hybrid approach combining collaborative filtering and content-based filtering techniques, the system can capture nuanced relationships between users, video attributes, and engagement patterns. Three datasets are used to evaluate the effectiveness of the system: Kwai, TikTok, and MovieLens. Compared to baseline models, such as DeepFM, Wide & Deep, LightGBM, and XGBoost, the proposed MMGCN-based model shows superior performance. A notable feature of the proposed model is that it outperforms all baseline methods in capturing diverse user preferences and making accurate, personalized recommendations, resulting in a Kwai F1 score of 0.574, a Tiktok F1 score of 0.506, and a MovieLens F1 score of 0.197. We emphasize the importance of multi-modal integration and user-centric approaches in advancing recommender systems, emphasizing the role they play in enhancing content discovery and audience interaction on live broadcast platforms.