Inductive Learning Approach in Job Recommendation
Keywords:ILA (Inductive Learning Algorithm), SGBA (Scalable Graph-based Approach), MVI (Missing value imputation), MCV (Most common value), PF (Penalty factor), FastILA. ILA-1, ILA-2, ILA-3, ILA-4.
A recommender system is an information filtering system found in various applications, including social networking, e-commerce, business, academics, and research. It assists users with locating the most likely and entertaining facts from a collection of data. The job recommender system aids in the recruitment process by advising candidates and recruiters on suitable jobs and abilities. The current job recommender system provides job recommendations and the necessary abilities to assist in the search for a future profession. The machine learning algorithm is critical in guidance; however, it suffers from cold start and sparsity problems. Many researchers are unconcerned about system and data scalability. As a result, inductive learning can help overcome this problem by providing faster skill suggestions and recommendations. When dealing with a significant amount of data, or big data, the missing value ratio is frequently too high, affecting the learning model. Either discard records with missing values or replace a proper value and solve the problem to enhance model efficiency. The first strategy was ineffective since missing data can be significant in the induction process. We solve the feature selection strategy in this work, which selects relevant features and fills in lacking values. The novel feature selection and missing value handling technique are compared to the baseline algorithm on CareerBuilder’s benchmark dataset. In comparison to baseline approaches, the proposed algorithm produces better outcomes. Intrusion detection, text-to-speech conversion, and job recommendation are some of the most common uses of ILA.
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Copyright (c) 2022 Ravita Ravita, Sheetal Rathi
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