# Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization

arXiv: https://arxiv.org/abs/2110.06117 · HTML: https://mlsanity.com/paper/2110.06117/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation](https://mlsanity.com/paper/1805.03352/) (2018)
- [Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery](https://mlsanity.com/paper/1804.08562/) (2018)
- [Collaborative Similarity Embedding for Recommender Systems](https://mlsanity.com/paper/1902.06188/) (2019)
- [You Watch, You Give, and You Engage: A Study of Live Streaming Practices in China](https://mlsanity.com/paper/1803.06032/) (2018)
- [Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping](https://mlsanity.com/paper/2002.04338/) (2020)

## Built on by (most cited first, 1 of 1)

- [MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion](https://mlsanity.com/paper/2407.00056/) (2024)

## Abstract (arXiv)

In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations. However, donation and multi-streaming channel recommendations are challenging due to complicated streamer and viewer relations, asymmetric communications, and the tradeoff between personal interests and group interactions. In this paper, we introduce Multi-Stream Party (MSP) and formulate a new multi-streaming recommendation problem, called Donation and MSP Recommendation (DAMRec). We propose Multi-stream Party Recommender System (MARS) to extract latent features via socio-temporal coupled donation-response tensor factorization for donation and MSP recommendations. Experimental results on Twitch and Douyu manifest that MARS significantly outperforms existing recommenders by at least 38.8% in terms of hit ratio and mean average precision.