# Learning Consumer and Producer Embeddings for User-Generated Content Recommendation

arXiv: https://arxiv.org/abs/1809.09739 · HTML: https://mlsanity.com/paper/1809.09739/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

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

- [Evaluation on Entity Matching in Recommender Systems](https://mlsanity.com/paper/2601.17218/) (2026)

## Abstract (arXiv)

User-Generated Content (UGC) is at the core of web applications where users can both produce and consume content. This differs from traditional e-Commerce domains where content producers and consumers are usually from two separate groups. In this work, we propose a method CPRec (consumer and producer based recommendation), for recommending content on UGC-based platforms. Specifically, we learn a core embedding for each user and two transformation matrices to project the user's core embedding into two 'role' embeddings (i.e., a producer and consumer role). We model each interaction by the ternary relation between the consumer, the consumed item, and its producer. Empirical studies on two large-scale UGC applications show that our method outperforms standard collaborative filtering methods as well as recent methods that model producer information via item features.