Bootstrapping User and Item Representations for One-Class Collaborative Filtering

TL;DR

This paper introduces BUIR, a negative-sampling-free framework for one-class collaborative filtering that uses dual encoders and data augmentation to improve user-item representation learning, especially in sparse data scenarios.

Contribution

BUIR is the first OCCF framework that eliminates negative sampling by employing dual encoders with self-supervised learning and data augmentation techniques.

Findings

BUIR outperforms all baselines on sparse datasets.

The dual encoder architecture effectively prevents collapsed solutions.

Data augmentation improves representation quality in OCCF.

Abstract

The goal of one-class collaborative filtering (OCCF) is to identify the user-item pairs that are positively-related but have not been interacted yet, where only a small portion of positive user-item interactions (e.g., users' implicit feedback) are observed. For discriminative modeling between positive and negative interactions, most previous work relied on negative sampling to some extent, which refers to considering unobserved user-item pairs as negative, as actual negative ones are unknown. However, the negative sampling scheme has critical limitations because it may choose "positive but unobserved" pairs as negative. This paper proposes a novel OCCF framework, named as BUIR, which does not require negative sampling. To make the representations of positively-related users and items similar to each other while avoiding a collapsed solution, BUIR adopts two distinct encoder networks that learn from each other; the first encoder is trained to predict the output of the second encoder as its target, while the second encoder provides the consistent targets by slowly approximating the first encoder. In addition, BUIR effectively alleviates the data sparsity issue of OCCF, by applying stochastic data augmentation to encoder inputs. Based on the neighborhood information of users and items, BUIR randomly generates the augmented views of each positive interaction each time it encodes, then further trains the model by this self-supervision. Our extensive experiments demonstrate that BUIR consistently and significantly outperforms all baseline methods by a large margin especially for much sparse datasets in which any assumptions about negative interactions are less valid.