Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning (2022)

Abstract: Works on learning job title representation are mainly based on Job-Transition Graph , built from the working history of talents. However, since these records are usually messy, this graph is very sparse, which affects the quality of the learned representation and hinders further analysis. To address this specific issue, we propose to enrich the graph with additional nodes that improve the quality of job title representation. Specifically, we construct Job-Transition-Tag Graph , a heterogeneous graph containing two types of nodes, i.e., job titles and tags (i.e., words related to job responsibilities or functionalities). Along this line, we reformulate job title representation learning as the task of learning node embedding on the Job-Transition-Tag Graph . Experiments on two datasets show the interest of our approach.

## introduction the learning of job title (referred to as job for short) foot_0 representation has received much attention in the recruitment field because the learned representation is beneficial to various tasks, such as job recommendation @xcite , job title benchmarking @xcite , and job mobility prediction @xcite . however, in practice, learning a good representation is challenging for the following reasons: (i) noisy data: job title data is noisy due to personal subjective reasons (i.e., spelling errors) or objective reasons (i.e., resume parsers are not perfect). (ii) messy data: job titles are messy because people have different ways of thinking, and naming conventions vary by company and industry. for example, there are many alternative job titles for the same position, e.g., "purchasing clerk" and "buyer". another problem is that due to the ambiguity of certain terms, they can refer to different positions in different contexts, e.g., "registered nurses sandwich rehab" and "sandwich maker". for these reasons, standard semantic-based approaches that aggregate (e.g., mean or sum) word representations to get job title semantic representation may lead to mismatches. moreover, these methods ignore hidden relationships between job titles, e.g., titles in the same resume may be similar. @xcite learn representations from graphs. they create graphs from career trajectories, where nodes represent job titles and edges represent job transitions. then they design different loss functions to embed the nodes into a low-dimensional space. however, the generated graphs are usually sparse due to the above reasons, limiting the performance of graph-based methods. standardizing job titles before generating graphs can alleviate the sparsity issue to a certain extent, but at the cost of losing some information. to tackle these challenges, we propose to enrich graphs with structured contextual information and learn job title representations through network embedding methods. more specifically, inspired by domain-specific named entity tags (i.e., responsibility and function) proposed in @xcite , we treat the job title as a combination of responsibilities, functionalities, and other additional information. words related to responsibility and functionality are defined as tags. we assume that job titles with the same tag describe similar job responsibilities or functionalities, making them more likely to have similar representations. along this line, we construct job-transition-tag graph, a heterogeneous graph containing two types of nodes, i.e., job titles and tags, which carries more information, thereby alleviating the sparsity problem. ## experiments we evaluate the proposed job title representation learning scheme through (i) a node classification task (i.e., job title classification) and (ii) a link prediction task (i.e., next-job prediction). this section will first describe the two datasets used and experimental settings, followed by discussing the results.