Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

Lin, Zihan; Tian, Changxin; Hou, Yupeng; Zhao, Wayne Xin

doi:10.1145/3485447.3512104

Cited by 279 publications

(97 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this way, the prediction is not only based on behavior data. On the other hand, as contrastive learning has been shown effective on sparse data [20], the proposed dual-perspective contrastive learning manner (Eqn. ( 9)) can also benefit recommendation for those users with sparse interactions.…”

Section: Data Sparsity Levels (Rq3)mentioning

confidence: 99%

Modeling Two-Way Selection Preference for Person-Job Fit

Chen¹,

Hou²,

Song³

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

Self Cite

View full text Add to dashboard Cite

Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers. Existing works mainly focus on modeling the unidirectional process or overall matching. However, recruitment is a two-way selection process, which means that both candidate and employer involved in the interaction should meet the expectation of each other, instead of unilateral satisfaction. In this paper, we propose a dual-perspective graph representation learning approach to model directed interactions between candidates and jobs. To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph. To learn dualperspective node representations effectively, we design an effective optimization algorithm, which involves a quadruple-based loss and a dual-perspective contrastive learning loss. Extensive experiments on three large real-world recruitment datasets have shown the effectiveness of our approach. Our code is available at https://github.com/RUCAIBox/DPGNN.

show abstract

Section: Data Sparsity Levels (Rq3)mentioning

confidence: 99%

Modeling Two-Way Selection Preference for Person-Job Fit

Chen¹,

Hou²,

Song³

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…The sequence-item contrastive task aims to capture the intrinsic correlation between sequential contexts (i.e., the observed subsequence) and potential next items in an interaction sequence. Different from previous next-item prediction task [10,15] (using in-domain negatives), for a given sequence, we adopt across-domain items as negatives. Such a way can enhance both the semantic fusion and adaptation across domains, which is helpful to learn universal sequence representations.…”

Section: Multi-domain Sequentialmentioning

confidence: 99%

Towards Universal Sequence Representation Learning for Recommender Systems

Hou¹,

Mu²,

Zhao³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…𝑗 + indicates an observed interaction between user 𝑢 𝑖 and item 𝑣 𝑗 + and 𝑗 − indicates an unobserved one. As high-order neighboring relations within contributors are also useful for recommendations, we enforce users to have similar representations as their structural neighbors through the structure-contrastive learning objective [28]:…”

Section: Optimizationmentioning

confidence: 99%

“…For Node Semantic Modeling (Sec. [28], we adopt the hyperparameter setting from the original implementation and set 𝜆 2 = 1𝑒 − 6, 𝜂 = 2 without further tuning. For the baseline models, the hyperparameters are set to the optimal settings as reported in their original papers.…”

Section: Implementation Detailsmentioning

confidence: 99%

See 1 more Smart Citation

Code Recommendation for Open Source Software Developers

Jin¹,

Bai²,

Zhu³

et al. 2022

Preprint

View full text Add to dashboard Cite

Open Source Software (OSS) is forming the spines of technology infrastructures, attracting millions of talents to contribute. Notably, it is challenging and critical to consider both the developers' interests and the semantic features of the project code to recommend appropriate development tasks to OSS developers. In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects. Considering the complex interactions among multiple parties within the system, we propose CODER, a novel graph-based code recommendation framework for open source software developers. CODER jointly models microscopic user-code interactions and macroscopic userproject interactions via a heterogeneous graph and further bridges the two levels of information through aggregation on file-structure graphs that reflect the project hierarchy. Moreover, due to the lack of reliable benchmarks, we construct three large-scale datasets to facilitate future research in this direction. Extensive experiments show that our CODER framework achieves superior performance under various experimental settings, including intra-project, crossproject, and cold-start recommendation. We will release all the datasets, code, and utilities for data retrieval upon the acceptance of this work.

show abstract

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

Cited by 279 publications

References 23 publications

Modeling Two-Way Selection Preference for Person-Job Fit

Modeling Two-Way Selection Preference for Person-Job Fit

Towards Universal Sequence Representation Learning for Recommender Systems

Code Recommendation for Open Source Software Developers

Contact Info

Product

Resources

About