Task Recommendation Method for Online Labor Market Based on Contrastive Learning

Xuanyu Zhang () and Yixuan Ma ()
Additional contact information
Xuanyu Zhang: Beijing Jiaotong University
Yixuan Ma: Beijing Jiaotong University

A chapter in LISS 2023, 2024, pp 583-594 from Springer

Abstract: Abstract The emergence of the online labor market has been gradual, but workers face challenges in finding tasks that align with their interests among the vast number of available tasks. Consequently, the study of task recommendation algorithms becomes crucial for the advancement of the online labor market. In this paper, we propose a method which utilizing contrastive learning in task recommendation of online labor market. We use constrastive learning to pre-train better embedding features to represent workers and tasks. These features are employed to initialize the embedding layer of popular recommendation system models, thereby exploring the model’s effectiveness further. We find that this method can improve the performance of existing recommendation models. Experimental results using an online labor market dataset indicate that our approach, which incorporates features learned through contrastive learning into PNN, WideDeep, or DeepFM recommendation models, leads to improvements in five evaluation metrics. These results demonstrate the benefits of our method in recommending tasks within the online labor market. The primary contribution of this paper is the utilization of pre-trained embeddings obtained through contrastive learning to initialize the embedding layer within popular recommendation system models.

Keywords: online labor markets; contrastive learning; recommendation algorithm (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-97-4045-1_45

Ordering information: This item can be ordered from
http://www.springer.com/9789819740451

DOI: 10.1007/978-981-97-4045-1_45

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().