# Generating Job Recommendations Based on User Personality and Gallup Tests
## 2 Related Work  
The literature on recommender systems and their various applications is full of variations of collaborative filtering, such as content-based or hybrid methods [4], which take advantage of the available information expressed as user or item content, user-to-item preferences, etc. to recommend the most appropriate choices for each individual user or group of users [5]. Content-based approaches, in the case of job recommendations, try to match resume information with job descriptions by quantifying the similarity of the respective texts. This was traditionally performed using cosine similarity at the high-dimensional vector space of keywords [6]. However, recent advances in transformers and embeddings allow more proficient semantic-based matching to be employed and improve matching performance [7]. A recent survey on e-recruitment recommendation systems [8] examines the more practical aspects of job recommendations and focuses on the challenges that e-recruitment systems have to resolve. As shown in the survey, job recommendation systems differ significantly from traditional general-purpose recommender systems due to several distinct characteristics. Unlike traditional systems, job recommenders adhere to the \"one worker, one job\" principle, where job seekers and positions are typically limited to singular matches, creating competition and necessitating a small, highly selective set of recommendations. These systems also operate on a two-sided engagement model, requiring mutual actions from job seekers and employers, unlike unilateral user actions in traditional domains like movie recommendations. Additionally, job recommenders emphasize both suitability and user preferences, integrating multi-faceted data such as skills, experience, and cultural fit, while addressing challenges like fairness and trustworthiness in this high-stakes domain. Furthermore, the transient nature of job listings and episodic engagement of users result in short interaction histories, making it harder to gather and utilize data compared to other domains. According to the same survey, job recommendation systems face challenges, including data quality issues like noise, semantic gaps, multilingual data, and sparsity, which complicate feature extraction and matching. A different group of recommender systems in the recruitment industry comprises works that aim to generate career improvement recommendations via upskilling or reskilling actions based on user profiles and their desired positions [9; 10; 11]. The various methods used for tackling this task comprise content-based matching between user profiles and training courses [11], the use of knowledge graphs in order to perform information-rich matching [10], or even dynamic programming [9] and other matching optimization techniques. Another recent survey [12] summarizes the techniques that can be employed for recommending skills for upskilling or reskilling, to Content-Based Filtering (CBF), which identifies skills similar to a user's profile by analyzing their history and preferences; Collaborative Filtering (CF), which leverages the preferences of other users, either through memory-based approaches using similarity measures or model-based methods for predicting new skills; Knowledge-Based techniques (KB), which infer skills based on predefined rules and user preferences; Utility-Based techniques (UB), which calculate the utility of skills based on multi-attribute preferences; Rule-Based techniques (RB) that use if-then rules to match users with relevant skills; and Hybrid Filtering (HF), which combines these methods to enhance accuracy and address limitations like sparsity, scalability, and cold-start issues.