Learning Consumer and Producer Embeddings for User-Generated Content Recommendation

TL;DR

This paper introduces CPRec, a novel recommendation method for UGC platforms that models user roles as producer and consumer through learned embeddings and transformations, improving recommendation accuracy.

Contribution

The paper presents a new embedding-based approach that explicitly models user roles in UGC content recommendation, outperforming existing collaborative filtering methods.

Findings

Outperforms standard collaborative filtering methods.

Better models producer information than item feature-based methods.

Effective on large-scale UGC datasets.

Abstract

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.