The problem of career trajectory prediction (CTP) aims to predict one's future employer or job position. While several CTP methods have been developed for this problem, we posit that none of these methods (1) jointly considers the mutual ternary dependency between three key units (i.e., user, position, and company) of a career and (2) captures the characteristic shifts of key units in career over time, leading to an inaccurate understanding of the job movement patterns in the labor market. To address the above challenges, we propose a novel solution, named as CAPER, that solves the challenges via sophisticated temporal knowledge graph (TKG) modeling. It enables the utilization of a graph-structured knowledge base with rich expressiveness, effectively preserving the changes in job movement patterns. Furthermore, we devise an extrapolated career reasoning task on TKG for a realistic evaluation. The experiments on a real-world career trajectory dataset demonstrate that CAPER consistently and significantly outperforms four baselines, two recent TKG reasoning methods, and five state-of-the-art CTP methods in predicting one's future companies and positions--i.e., on average, yielding 6.80% and 34.58% more accurate predictions, respectively. The codebase of CAPER is available at https://github.com/Bigdasgit/CAPER.

Despite substantial advancements in various fields of AI, computational research in career and job domains has been significantly hindered by a critical lack of accessible datasets. This limitation is mainly due to the proprietary nature of job platforms, which restrict the sharing of job-domain datasets with the research community. The scarcity is particularly pronounced for career trajectory and resume datasets, severely constraining academic researchers in developing and evaluating new models. In this paper, we address the crucial issue of resume dataset unavailability in the job domain, identified through our comprehensive comparison of existing job-domain machine learning studies. To the best of our knowledge, we introduce OpenResume, the first publicly available, anonymized, and structured resume dataset, specifically designed for job-domain downstream tasks. This dataset aims to catalyze advancements in AI and foster new markets for machine learning and data science within career trajectory modeling. OpenResume is comprehensively processed from real-world resume data. We anonymize and substitute personal identifiers and company names, normalize job titles into ESCO-based ones (i.e., one of the most common occupation taxonomies), and employ differential privacy techniques on temporal features to ensure open accessibility and privacy protection. Additionally, we augment OpenResume with a synthetically generated resume dataset derived from the post-processed real-world data, extending its diversity and utility. To demonstrate that OpenResume retains challenges and properties similar to real-world job datasets, we benchmark OpenResume on state-of-the-art job-domain prediction models across four prevalent downstream tasks: (1) next job title prediction, (2) next company prediction, (3) turnover prediction, and (4) link prediction. Our experimental results show that these job-domain models perform comparably on OpenResume and the original data across all tasks, demonstrating OpenResume as a valuable career trajectory dataset for both academic research and practical applications. We also indicate the OpenResume applicability for the other eight downstream tasks. Our datasets are available at: https://tinyurl.com/OpenResumeData.

In online job marketplaces, it is important to establish a well-defined job title taxonomy for various downstream tasks (e.g., job recommendation, users’ career analysis, and turnover prediction). Job Title Normalization (JTN) is such a cleaning step to classify user-created non-standard job titles into normalized ones. However, solving the JTN problem is non-trivial with challenges: (1) semantic similarity of different job titles, (2) non-normalized user-created job titles, and (3) large-scale and long-tailed job titles in real-world applications. To this end, we propose a novel solution, named JAMES, that constructs three unique embeddings (i.e., graph, contextuat, and syntactic) of a target job title to effectively capture its various traits. We further propose a multi-aspect co-attention mechanism to attentively combine these embeddings, and employ neural logical reasoning representations to collaboratively estimate similarities between messy job titles and normalized job titles in a reasoning space. To evaluate JAMES, we conduct comprehensive experiments against ten competing models on a large-scale real-world dataset with over 350,000 job titles. Our experimental results show that JAMES significantly outperforms the best baseline by 10.06% in Precision@10 and by 17.52% in NDCG@10, respectively. To further facilitate the acquisition of normalized job titles for job-domain applications, our JAMES API is available at: https://tinyurl.con JAMES-job-title-mapping.

It is becoming common to apply location information for actual problems such as automatic routing, creating tour, and the prediction of user activity. In many of these cases, they use a geodata that is based on a user's self check-in. In this study, to focus on a user's daily behavior deeply, we use a large volume of geospatial data, which is detected regularly and automatically from a cell phone's GPS system and the volume of data is totally different from the self check-in based. We can comprehend users activity in more detail than self check-in based data. The main purpose of this research is to extract and interpret user preferences from big geospatial data. Because the auto-detected data is big and spatial, it is necessary to process and analyze it for transformation to a value. Firstly, we extract the spots where people visited and stayed from raw geodata. Next, we categorize spots using the spot information to reduce the dimension. Finally, we interpret user preferences by applying a topic model to the matrix of users and the categorized spot. Our results show that we can interpret user preferences as topics from big geospatial data using dimension reduction and a topic model. In addition, we verify a performance of methods that use the topics to predict purchase. We found that the models that use topics outperforms others on the real prediction problem.