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.
|Title of host publication||SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||10|
|Publication status||Published - 2021 Jul 11|
|Event||44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021 - Virtual, Online, Canada|
Duration: 2021 Jul 11 → 2021 Jul 15
|Name||SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Conference||44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021|
|Period||21/7/11 → 21/7/15|
Bibliographical noteFunding Information:
This work was supported by the NRF grant funded by the MSIT (No. 2020R1A2B5B03097210, 2021R1C1C1009081), and the IITP grant funded by the MSIT (No. 2018-0-00584, 2019-0-01906).
© 2021 ACM.
All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design
- Information Systems